CN116214762A - Modified plastic processing system and method - Google Patents

Modified plastic processing system and method Download PDF

Info

Publication number
CN116214762A
CN116214762A CN202310257471.XA CN202310257471A CN116214762A CN 116214762 A CN116214762 A CN 116214762A CN 202310257471 A CN202310257471 A CN 202310257471A CN 116214762 A CN116214762 A CN 116214762A
Authority
CN
China
Prior art keywords
modified plastic
plastic particles
appearance
processing
controlling
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
CN202310257471.XA
Other languages
Chinese (zh)
Other versions
CN116214762B (en
Inventor
冯小超
纪海丽
冯晶晶
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Suzhou Bozhishun Material Technology Co ltd
Original Assignee
Suzhou Bozhishun Material Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Suzhou Bozhishun Material Technology Co ltd filed Critical Suzhou Bozhishun Material Technology Co ltd
Priority to CN202311324486.XA priority Critical patent/CN117261028A/en
Priority to CN202310257471.XA priority patent/CN116214762B/en
Publication of CN116214762A publication Critical patent/CN116214762A/en
Application granted granted Critical
Publication of CN116214762B publication Critical patent/CN116214762B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • BPERFORMING OPERATIONS; TRANSPORTING
    • B29WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
    • B29BPREPARATION OR PRETREATMENT OF THE MATERIAL TO BE SHAPED; MAKING GRANULES OR PREFORMS; RECOVERY OF PLASTICS OR OTHER CONSTITUENTS OF WASTE MATERIAL CONTAINING PLASTICS
    • B29B9/00Making granules
    • B29B9/02Making granules by dividing preformed material
    • B29B9/06Making granules by dividing preformed material in the form of filamentary material, e.g. combined with extrusion
    • YGENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02WCLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO WASTEWATER TREATMENT OR WASTE MANAGEMENT
    • Y02W30/00Technologies for solid waste management
    • Y02W30/50Reuse, recycling or recovery technologies
    • Y02W30/62Plastics recycling; Rubber recycling

Abstract

The embodiment of the specification provides a modified plastic processing system and a modified plastic processing method, wherein the system comprises a material taking device, a processing bin, a mixed heating and pressurizing device, a cooling forming device, an extruding device, a cutting device, a material receiving barrel and a processor; the processor is used for: the material taking device is controlled to respectively grasp the raw material plastic and the processing agent, and the raw material plastic and the processing agent are placed in the processing bin; controlling a mixing heating and pressurizing device to mix raw material plastics and a processing agent in a processing bin based on mixing parameters, and heating and pressurizing based on the heating and pressurizing parameters to obtain a molten mixture, wherein the mixing parameters comprise at least one of mixing strength and mixing duration; in the processing bin, controlling a cooling forming device to cool and form the molten mixture based on cooling parameters, and controlling an extruding device to extrude the cooled and formed modified plastic strips from a discharge hole of the processing bin; and controlling a cutting device to cut the modified plastic strips extruded from the discharge holes to obtain modified plastic particles.

Description

Modified plastic processing system and method
Technical Field
The specification relates to the technical field of plastic processing, in particular to a modified plastic processing system and method.
Background
The polymer plastic product has the advantages of excellent mechanical property, wear resistance, corrosion resistance, insulativity and the like, and meanwhile, the density of the polymer material is smaller, so that the polymer plastic product has wider and wider application in life and production. However, in the production process of the polymer plastic product, if the raw material plastic is not processed in advance, a plurality of defects, such as black spots, serious chromatic aberration, poor mechanical strength and the like, of the polymer plastic product obtained by subsequent production can be caused.
Therefore, it is necessary to provide a modified plastic processing method and system to achieve processing of the raw plastic.
Disclosure of Invention
One or more embodiments of the present specification provide a modified plastic processing system, the system comprising: the device comprises a material taking device, a processing bin, a mixed heating and pressurizing device, a cooling and forming device, an extruding device, a cutting device, a material receiving barrel and a processor, wherein the mixed heating and pressurizing device, the cooling and forming device and the extruding device are positioned in the processing bin, and the material taking device and the cutting device are positioned outside the processing bin; the processor is configured to: controlling the material taking device to respectively grasp raw material plastics and a processing agent, and placing the raw material plastics and the processing agent into the processing bin; controlling the mixing heating and pressurizing device to mix the raw material plastic and the processing agent in the processing bin based on mixing parameters, and heating and pressurizing based on heating and pressurizing parameters to obtain a molten mixture, wherein the mixing parameters comprise at least one of mixing strength and mixing duration; in the processing bin, controlling the cooling forming device to cool and form the molten mixture based on cooling parameters, and controlling the extruding device to extrude the cooled and formed modified plastic strips from a discharge hole of the processing bin; and controlling the cutting device to cut the modified plastic strips extruded from the discharging holes to obtain modified plastic particles, wherein the product receiving barrel is used for receiving the modified plastic particles.
One or more embodiments of the present specification provide a modified plastic processing method performed by a processor of a modified plastic processing system comprising a reclaimer, a processing bin, a hybrid warming-pressurizing device, a cooling-forming device, an extrusion device, a cutting device, a product receiving bin, and the processor; the method comprises the following steps: controlling the material taking device to respectively grasp raw material plastics and a processing agent, and placing the raw material plastics and the processing agent into the processing bin; controlling the mixing heating and pressurizing device to mix the raw material plastic and the processing agent in the processing bin based on mixing parameters, and heating and pressurizing based on heating and pressurizing parameters to obtain a molten mixture, wherein the mixing parameters comprise at least one of mixing strength and mixing duration; in the processing bin, controlling the cooling forming device to cool and form the molten mixture based on cooling parameters, and controlling the extruding device to extrude the cooled and formed modified plastic strips from a discharge hole of the processing bin; and controlling the cutting device to cut the modified plastic strips extruded from the discharge holes to obtain modified plastic particles.
One or more embodiments of the present specification provide a modified plastic processing device comprising at least one processor and at least one memory; the at least one memory is configured to store computer instructions; the at least one processor is configured to execute at least some of the computer instructions to implement the modified plastic processing method as described in any of the embodiments above.
One or more embodiments of the present specification provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement a modified plastic processing method as in any of the above embodiments.
Drawings
The present specification will be further elucidated by way of example embodiments, which will be described in detail by means of the accompanying drawings. The embodiments are not limiting, in which like numerals represent like structures, wherein:
FIG. 1 is an exemplary schematic diagram of a modified plastic processing system according to some embodiments of the present description;
FIG. 2 is an exemplary flow chart of a modified plastic processing method according to some embodiments of the present disclosure;
FIG. 3 is an exemplary flow chart for determining the eligibility of modified plastic particles according to some embodiments of the present description;
FIG. 4 is an exemplary schematic diagram of an appearance feature determination model shown in accordance with some embodiments of the present description;
fig. 5 is an exemplary schematic diagram of determining a preset opening and closing degree, a preset extrusion speed, and a preset cutting speed according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings that are required to be used in the description of the embodiments will be briefly described below. It is apparent that the drawings in the following description are only some examples or embodiments of the present specification, and it is possible for those of ordinary skill in the art to apply the present specification to other similar situations according to the drawings without inventive effort. Unless otherwise apparent from the context of the language or otherwise specified, like reference numerals in the figures refer to like structures or operations.
It will be appreciated that "system," "apparatus," "unit" and/or "module" as used herein is one method for distinguishing between different components, elements, parts, portions or assemblies at different levels. However, if other words can achieve the same purpose, the words can be replaced by other expressions.
As used in this specification and the claims, the terms "a," "an," "the," and/or "the" are not specific to a singular, but may include a plurality, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that the steps and elements are explicitly identified, and they do not constitute an exclusive list, as other steps or elements may be included in a method or apparatus.
A flowchart is used in this specification to describe the operations performed by the system according to embodiments of the present specification. It should be appreciated that the preceding or following operations are not necessarily performed in order precisely. Rather, the steps may be processed in reverse order or simultaneously. Also, other operations may be added to or removed from these processes.
FIG. 1 is an exemplary schematic diagram of a modified plastic processing system according to some embodiments of the present description. As shown in fig. 1, the modified plastic processing system 100 may include a reclaimer 110, a process cartridge 120, a hybrid warming and pressurizing device 130, a cooling forming device 140, an extrusion device 150, a cutting device 160, a product receiving tank 170, and a processor 180.
The take-off device 110 may be a device for gripping the raw plastic and the processing agent. In some embodiments, the take off device 110 may be located outside of the process cartridge 120. In some embodiments, the take out device 110 may grasp the raw plastic and process agent separately into the process cartridge 120. For more on the reclaimer device gripping the raw plastic and the processing agent, reference is made to step 210 and its associated description.
The processing bin 120 may be a device that provides a location for the modification processing of the raw plastic and processing agent. In some embodiments, the interior of the process cartridge 120 may include a hybrid warming and pressurizing device 130, a cooling forming device 140, and an extrusion device 150. In some embodiments, the raw plastic and the processing agent may be mixed, heated, pressurized, extruded, and cold formed within the processing bin 120.
The mixing, heating and pressurizing device 130 may be a device for mixing the raw plastic and the processing agent while heating and pressurizing. In some embodiments, the mixing, warming and pressurizing device may mix the raw plastic and the processing agent based on the mixing parameters and warm and pressurize based on the warming and pressurizing parameters to obtain the molten mixture. For more details on obtaining melt mixing see step 220 and its associated description.
The cooling molding apparatus 140 may be an apparatus for cooling molding the molten mixture. In some embodiments, the cooling molding apparatus 140 may extrude and cool the molten mixture to form a modified plastic strip. For more details on the resulting modified plastic strip, see step 230 and the description thereof.
The extrusion device 150 may be a device apparatus for extruding the modified plastic strand. In some embodiments, extrusion device 150 may extrude the cooled, shaped modified plastic strip from the discharge void of processing bin 120.
The cutting device 160 may be a device for cutting the modified plastic strip. In some embodiments, the cutting device 160 may be located outside of the processing bin 120. In some embodiments, the cutting device 160 may cut the modified plastic strands extruded from the discharge aperture of the processing bin to obtain modified plastic pellets. For more details on the obtaining of modified plastic particles, reference can be made to step 240 and the description related thereto.
The product receiving bin 170 may be a receiving bin for receiving modified plastic particles. In some embodiments, different production batches of modified plastic particles may be received with different product receiving barrels.
Processor 180 may be used to process information and/or data related to modified plastic processing system 100. Processor 180 may execute program instructions to perform one or more of the functions described herein based on such data, information, and/or processing results. In some embodiments, the processor 180 may control various portions of the modified plastic processing system 100 to execute relevant instructions. For example, the processor 180 may control the take out device 110 to grasp the raw plastic and the processing agent, respectively, and place them in the processing bin 120.
In some embodiments, processor 180 may include one or more sub-processing devices (e.g., single-core processing devices or multi-core processing devices). For example only, the processor 180 may include a Central Processing Unit (CPU) or the like. In some embodiments, the processor 180 may be a single server or a group of servers. In some embodiments, the processor 180 may be local, remote. For example, the processor 180 may be disposed near the process cartridge 120 or at a greater distance from the process cartridge 120.
FIG. 2 is an exemplary flow chart of a modified plastic processing method according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, the process 200 may be performed by a processor.
Step 210, controlling the material taking device to respectively grasp the raw material plastic and the processing agent, and placing the materials into a processing bin. For more details regarding the take-off and process cartridge, see fig. 1 and its associated description.
The raw material plastic is plastic which needs to be modified. For example, the raw plastic may include, but is not limited to, polypropylene, polyvinyl chloride, polycarbonate, and the like.
The processing agent refers to other materials added in the modification processing process. For example, the processing agent may include, but is not limited to, stabilizers, plasticizers, flame retardants, and the like.
In some embodiments, the take-off device may grasp the raw plastic and process agent into the process cartridge in a variety of ways. For example, the material taking device can grasp raw material plastics and processing agents in a processing bin in a negative pressure suction mode.
Step 220, controlling the mixing heating and pressurizing device to mix the raw material plastic and the processing agent in the processing bin based on the mixing parameters, and heating and pressurizing based on the heating and pressurizing parameters to obtain a molten mixture. For more details on the warming and pressurizing device, see fig. 1 and its associated description.
The mixing parameters refer to parameter information about the mixing of the raw plastic and the processing agent. In some embodiments, the mixing parameters may include at least one of mixing intensity and mixing duration.
Mixing intensity refers to a parameter related to the degree of mixing of the raw plastic and the processing agent. For example, the mixing intensity may include, but is not limited to, mixing speed, mixing power, and the like.
The mixing time period refers to the length of time that the raw plastic and the processing agent are mixed.
In some embodiments, the processor may determine the mixing parameters in a variety of ways. For example, the mixing parameters may be empirically determined based on the kinds, mass ratios, etc. of the raw plastic and the processing agent.
In some embodiments, the mixing parameters may be determined based on at least one of the feed plastics to processing agent addition ratio, the monomer volume ratio, and the monomer mass ratio.
The addition ratio of the raw material plastic to the processing agent refers to the total mass ratio of the raw material plastic and the processing agent when the raw material plastic and the processing agent are mixed. For example, the mass is M 1 The mass of the raw material plastic is M 2 The processing agent of the plastic is mixed, and the addition ratio of the raw material plastic and the processing agent is M 1 :M 2
The monomer volume ratio refers to the volume ratio of the individual raw plastic particles to the individual processing agent particles. For example, the volume of the individual raw plastic particles is V 1 The volume of the single processing agent particle is V 2 The volume ratio of the monomers is V 1 :V 2
The monomer mass ratio refers to the mass ratio of the individual raw plastic particles and the individual processing agent particles. For example, the mass of the individual raw material plastic particles is m 1 The mass of the single processing agent particle is m 2 The mass ratio of the monomers is m 1 :m 2
In some embodiments, the processor may determine the mixing parameters based on at least one of the feed plastics to processing agent addition ratio, the monomer volume ratio, and the monomer mass ratio in a variety of ways. For example, the processor may determine the mixing parameters based on a preset data look-up table by the additive amount ratio of the raw plastic to the processing agent, the monomer volume ratio, and the monomer mass ratio. The preset data comparison table records the corresponding preferable mixing parameters of different additive amount ratios, monomer volume ratios, monomer mass ratios and the like of the raw material plastics and the processing agent. The preset data comparison table can be preset based on priori knowledge or historical data.
In some embodiments, the mixing parameters may be adjusted in real time in different production cycles in the same batch production. Wherein the adjustment amount adjusted in real time is related to the uniformity of quality characteristics of the modified plastic particles produced in the previous production cycle in the same batch. For more on uniformity of quality features see fig. 3 and its associated description.
The same batch refers to the same production conditions. For example, the same batch may be the same in the kind of raw material plastic, the kind of processing agent, the requirements of the produced modified plastic, and the like.
A production cycle refers to the total time from the start of production to the time of production. For example, the production cycle may be the entire time from the start of production of the raw plastic and the processing agent to the production of the modified plastic granules.
In some embodiments, the processor may adjust the mixing parameters of different production cycles in the same batch of production in real time by a preset adjustment method. In some embodiments, the preset adjustment method may include: in the same batch, if the average value of the uniformity of the quality features of the modified plastic particles produced in the previous production period is lower than the uniformity threshold value of the quality features, the processor can increase the mixing intensity, and the increase value of the mixing intensity is directly related to the difference value between the uniformity threshold value of the quality features and the average value of the uniformity of the quality features. In some embodiments, the preset adjustment method may include: in the same batch, if the dispersion degree of the quality characteristic uniformity of the modified plastic particles produced in the previous production period is lower than the dispersion degree threshold value, the processor can increase the mixing duration, and the increase value of the mixing duration is positively related to the difference value between the dispersion degree threshold value and the dispersion degree of the quality characteristic uniformity. Wherein the degree of dispersion of the uniformity of the quality feature can be determined by calculating the variance of uniformity of the quality feature for a plurality of previous production cycles. For example, the smaller the variance of the quality feature uniformity across multiple production cycles, the smaller the degree of dispersion of the quality feature uniformity. The quality feature uniformity threshold and the dispersion threshold may be default values, empirical values, preset values in advance, or any combination thereof, and may be determined according to actual requirements, which is not limited herein.
In some embodiments of the present disclosure, since the raw material plastic and the processing agent generally have different monomer volumes and densities, the mixing parameters are determined by taking into consideration the addition ratio of the raw material plastic and the processing agent, the monomer volume ratio and the monomer mass ratio, and simultaneously, the quality characteristic uniformity of the modified plastic particles produced in the previous production cycles of the same batch is adjusted in real time to the mixing parameters of the subsequent production cycles, so that the quality of the produced modified plastic particles can be effectively improved.
In some embodiments, the mixing, warming and pressurizing device may mix the raw plastic and the processing agent in a variety of ways. For example, the mixing warming and pressurizing device may mix the raw material plastic and the processing agent by screw rotation based on the mixing time period and the mixing strength.
The heating and pressurizing parameters refer to parameters related to heating and pressurizing of the raw plastic and the processing agent. For example, the warming and pressurizing parameters may include, but are not limited to, heating temperature, pressure magnitude, heating time, and the like.
In some embodiments, the processor may determine the warming-pressurization parameters in a variety of ways. For example, the heating and pressurizing parameters may be empirically determined based on the weight, mass ratio, etc. of the raw plastic and the processing agent.
In some embodiments, the processor may determine the warming-pressurization parameter based on at least one of a composition profile of the feedstock plastic, a composition profile of the processing agent. For more details regarding determining the warming and pressurizing parameters, see FIG. 5 and its associated description.
In some embodiments, the warming and pressurizing device may warm and pressurize the mixed raw plastic and processing agent in a variety of ways. For example, the heating and pressurizing device may perform heating by a resistor based on a heating time and a heating temperature, and perform pressurizing by screw rotation based on a pressure.
Molten mixture refers to a mixture in a molten state. For example, the molten mixture may be a mixture of the raw plastic and the processing agent in a molten state.
And 230, controlling a cooling forming device to cool and form the molten mixture based on the cooling parameters in the processing bin, and controlling an extrusion device to extrude the cooled and formed modified plastic strips from the discharge holes of the processing bin. For more on the cooling forming device, the extrusion device, see fig. 1 and the related description thereof.
The cooling parameter refers to a parameter related to cooling of the molten mixture. For example, the cooling parameters may include, but are not limited to, cooling temperature, cooling time, and the like.
In some embodiments, the processor may determine the cooling parameters in a variety of ways. For example, the cooling parameters may be determined empirically based on the type of raw plastic, the mass of the molten mixture, the temperature of the molten mixture, and the like.
In some embodiments, the processor may determine the cooling parameter based on at least one of a composition profile of the feedstock plastic, a composition profile of the processing agent. For more on determining the cooling parameters see fig. 5 and its related description.
In some embodiments, the cooling molding apparatus may cool mold the molten mixture in a variety of ways. For example, the cooling molding device may extrude the molten mixture into a bar shape, and then cool the bar shape by water cooling to obtain the modified plastic bar.
The modified plastic strip is modified plastic after extrusion cooling molding treatment. For example, the modified plastic strips may include, but are not limited to, polypropylene plastic strips, polyvinyl chloride plastic strips, polycarbonate plastic strips, and the like after extrusion cooling molding.
The discharging hole refers to a hole used for extruding materials on the processing bin. For example, the process cartridge discharge aperture may be an aperture for extruding a modified plastic strip.
And 240, controlling a cutting device to cut the modified plastic strips extruded from the discharge holes to obtain modified plastic particles. For more on the cutting device, see fig. 1 and the related description thereof.
In some embodiments, the cutting device may cut the modified plastic strip in a variety of ways. For example, the cutting device may cut the modified plastic strip with a rotary cutter head.
The modified plastic particles refer to plastic particles subjected to modification processing. For example, the modified plastic particles may include, but are not limited to, modified processed polypropylene particles, polyvinyl chloride particles, polycarbonate particles, and the like.
In some embodiments, a product receiving bin may be used to receive the modified plastic particles. For more on the product receiving drum see fig. 1 and its related description.
In some embodiments of the present disclosure, the raw plastic is modified by the mixing heating and pressurizing device, the cooling and forming device, the extruding device, and the cutting device, so that the obtained modified plastic particles are more uniform in shape, volume, color, and the like, and the modified plastic particles can have different characteristics by adding different kinds of processing agents during the modification processing.
FIG. 3 is an exemplary flow chart for determining the eligibility of modified plastic particles according to some embodiments of the present description. As shown in fig. 3, the process 300 includes the following steps. In some embodiments, the process 300 may be performed by a processor.
In some embodiments, the modified plastic processing system 100 may also include a cooperative detection device.
The cooperative detection means may be a device for detecting the quality of the modified plastic particles obtained after processing by the plastic processing system.
In some embodiments, the cooperative detection means may comprise a robot, a vision device and a detection station.
The robot may be a device for gripping the modified plastic granules from the product receiving barrel. For example, the robot may include, but is not limited to, a hydraulically driven robot, a pneumatically driven robot, and the like.
The vision device may be a device apparatus for photographing the inspection station. For example, the visual device may include, but is not limited to, a camera or the like.
The inspection station refers to a device for placing the modified plastic particles to be inspected. In some embodiments, a pressure sensor or the like may be mounted on the detection stage. In some embodiments, the inspection station bottom can be opened and closed, and the inspection station bottom can be connected with the product receiving tub.
In some embodiments, the processor may control the cooperative detection means to perform uniformity detection on the modified plastic particles at each production cycle of the same production lot. For more details on production lots and production cycles, see step 220 and its associated description.
The uniformity detection is a detection of the uniformity of the modified plastic particles. In some embodiments, uniformity detection may include appearance uniformity detection.
Appearance uniformity detection refers to detection of the uniformity of appearance morphology of the modified plastic.
In some embodiments, uniformity detection further comprises quality uniformity detection. For more details on quality uniformity detection see the following description.
In some embodiments, the specific process of controlling the cooperative detecting device to perform appearance uniformity detection on the modified plastic particles by the processor may be referred to as step 310 and the related description.
In step 310, the appearance quality characteristics of the modified plastic particles are determined based on the degree of dispersion of the appearance characteristics of the modified plastic particles obtained by at least one sampling.
The appearance characteristics of the modified plastic particles refer to characteristics related to the appearance morphology of the modified plastic particles. For example, the appearance characteristics of the modified plastic particles may include, but are not limited to, the volume size, shape, color, presence or absence of appearance anomalies, etc. of the modified plastic particles.
The appearance abnormality refers to an abnormality related to the appearance morphology of the modified plastic particles. For example, the appearance anomaly may include, but is not limited to, a plurality of modified plastic particles being connected together, the modified plastic particles having black spots on the surface, the modified plastic particles having severe color differences in color, and the like.
In some embodiments, the appearance characteristics of the modified plastic particles may be represented in a vector form. For example, the appearance characteristics of the modified plastic particles may be expressed as (f, g, h). Wherein f represents the volume size of the modified plastic particles; g represents the difference between the color of the modified plastic particles and the standard color; h represents the difference between the shape of the modified plastic particles and the standard shape. The standard color and standard shape can be set in advance based on the production requirements of the modified plastic particles.
In some embodiments, the processor may determine the appearance characteristics of the modified plastic particles in a variety of ways. For example, the appearance characteristics of the modified plastic particles may be empirically determined based on the appearance morphology or the like of the modified plastic particles.
In some embodiments, the processor may determine the appearance characteristics of the modified plastic particles obtained from each of at least one of the samples in the appearance uniformity test by steps 311-314.
Step 311, controlling the cooperative detection device to randomly grasp a plurality of modified plastic particles from the material receiving barrel through a manipulator, and placing the modified plastic particles on a detection table.
In some embodiments, the robot may randomly grasp a number of modified plastic particles from the product receiving bin in a number of ways. For example, the manipulator may randomly grasp a number of modified plastic particles from the product receiving bin by equidistant random sampling.
In step 312, an image acquisition operation is performed for a preset number of acquisitions.
The preset acquisition times refer to parameters related to the execution times of the image acquisition operations. In some embodiments, the processor may determine the preset number of acquisitions in a number of ways. For example, the preset number of acquisitions may be a default value, an empirical value, an advanced preset value, etc. or any combination thereof, and may be determined according to actual requirements, which is not limited herein.
In some embodiments, the image acquisition operation may include: the processor controls the detection table to vibrate based on preset vibration parameters, so that modified plastic particles are dispersed in the detection table; and controlling the vision device to take a picture of the detection table so as to acquire image data containing the modified plastic particles.
The preset vibration parameter refers to a parameter related to vibration of the inspection stage. For example, the preset vibration parameters may include, but are not limited to, the frequency of the vibration, the amplitude of the vibration, the duration of the vibration, and the like.
In some embodiments, the processor may determine the preset vibration parameters in a variety of ways. For example, the preset vibration parameter may be a default value, an empirical value, an advanced preset value, or the like, or any combination thereof, and may be determined according to actual requirements, which is not limited herein.
In some embodiments, the preset vibration parameter and the preset number of acquisitions are related to the quality of the modified plastic particles and the smoothness of the modified plastic particles.
The mass of the modified plastic particles refers to the weight of the modified plastic particles. For example, the mass of the modified plastic particles may be m.
The smoothness of the modified plastic particles refers to the smoothness of the surface of the modified plastic particles. Wherein the smoothness of the modified plastic particles can be represented by real numbers ranging from 0 to 1, and the larger the numerical value is, the greater the smoothness of the surfaces of the modified plastic particles is represented.
In some embodiments, the greater the mass of the modified plastic particles and/or the less the smoothness of the modified plastic particles, the greater the vibration amplitude and duration of the preset vibration parameters, and the greater the preset number of acquisitions.
In some embodiments of the present disclosure, the quality and smoothness of the modified plastic particles may affect the probability of the modified plastic particles being transformed in its posture after being vibrated (e.g., the original bottom surface of the modified plastic particles becomes a side or top surface after being vibrated). The smaller the smoothness of the modified plastic particles, i.e., the greater the roughness, the less likely the modified plastic particles are to change the posture, and thus a larger vibration amplitude and a longer vibration duration are required to increase the probability of changing the modified plastic particles, while increasing the preset number of acquisitions in order to eliminate the measurement error due to the fact that the posture of the modified plastic particles is not changed during the vibration.
In some embodiments, the detection stage may vibrate in a variety of ways. For example, the inspection station may vibrate by way of automatic vibration.
The image data refers to relevant data information acquired by the vision device. For example, the image data may include an image containing modified plastic particles taken by a vision device.
In some embodiments, the vision device may acquire image data in a variety of ways. For example, the vision device may capture the image data by photographing the inspection station with a camera. In some embodiments, the vision device may capture image data through a single angle or multiple angles. In some embodiments, the vision device may capture image data multiple times at different times.
Step 313, determining appearance characteristics of the modified plastic particles sampled at this time based on the image data acquired by the preset acquisition times.
In some embodiments, the processor may determine the appearance characteristics of the presently sampled modified plastic particles based on the image data in a variety of ways. For example, the appearance characteristics of the modified plastic particles sampled this time may be empirically determined based on the image data.
In some embodiments, the processor may process the image data by an appearance characteristic determination model to determine appearance characteristics of the modified plastic particles that were sampled this time. For more on the determination of the appearance characteristics of the presently sampled modified plastic particles based on the appearance characteristic determination model, see fig. 4 and its related description.
And 314, opening the bottom of the detection table to enable the modified plastic particles sampled at the time to flow back into the product receiving barrel.
Based on the foregoing, the appearance characteristics of the modified plastic particles of the present sample may be obtained, and in some embodiments, the processor may determine the degree of dispersion of the appearance characteristics of the modified plastic particles based on the appearance characteristics of the modified plastic particles obtained by at least one sampling, and further determine the appearance quality characteristics of the modified plastic particles based on the degree of dispersion of the appearance characteristics of the modified plastic particles obtained by at least one sampling.
The degree of dispersion of the appearance characteristics refers to parameter information related to the degree of uniformity of the appearance characteristics of the modified plastic particles. Wherein the degree of dispersion of the appearance characteristics can be represented by a real number ranging from 0 to 1, and the smaller the numerical value, the more uniform the appearance characteristics of the modified plastic particles.
In some embodiments, the processor may determine the degree of dispersion of the appearance characteristics of the modified plastic particles in a variety of ways. For example, the processor may determine the degree of dispersion of the appearance features of the modified plastic particles by calculating the variance of the plurality of appearance features obtained from the plurality of samplings. The smaller the variance of the appearance features obtained by the multiple sampling, the smaller the degree of dispersion of the appearance features of the modified plastic particles.
Appearance quality characteristics refer to parameter information for totally reflecting appearance characteristics of the modified plastic particles. In some embodiments, the appearance quality features may include at least one of appearance average features, appearance feature uniformity.
The appearance average characteristic means an average value of appearance characteristics of a plurality of modified plastic particles.
In some embodiments, the processor may determine the appearance average feature in a variety of ways. For example, the processor may average the appearance characteristics of the plurality of modified plastic particles obtained by the plurality of samplings to obtain an appearance average characteristic of the modified plastic particles.
Appearance characteristic uniformity refers to the degree of uniformity of appearance characteristics of the plurality of modified plastic particles. Wherein the uniformity of the appearance characteristic can be represented by a real number ranging from 0 to 1, and the larger the numerical value is, the higher the uniformity degree of the appearance characteristic representing the plurality of modified plastic particles is.
In some embodiments, the processor may determine the appearance feature uniformity in a variety of ways. For example, the processor may calculate the variance of the appearance characteristics of the plurality of modified plastic particles obtained by sampling a plurality of times, and then calculate the reciprocal of the variance to obtain the uniformity of the appearance characteristics of the modified plastic particles.
Step 320, determining the qualification of the modified plastic particles based at least on the appearance quality characteristics of the modified plastic particles.
The qualification degree of the modified plastic particles refers to the degree to which the produced modified plastic particles meet the production requirements.
In some embodiments, the processor may determine the eligibility of the modified plastic particles in a variety of ways. For example, the processor may calculate a vector distance between the appearance average feature and the preset appearance feature of the modified plastic particles, the smaller the distance, the greater the eligibility of the modified plastic particles. Wherein, the preset appearance characteristic can be set in advance based on the production requirement of the modified plastic particles. Vector distances may include, but are not limited to, cosine distances, euclidean distances, mahalanobis distances, and the like. For another example, the greater the uniformity of the appearance characteristics of the modified plastic particles, the greater the pass of the modified plastic particles. For more on the preset appearance features, see fig. 5 and its associated description.
In some embodiments of the specification, the modified plastic particles produced in each production period are randomly sampled and detected by the cooperative detection device to detect the dispersion degree of the appearance characteristics of the modified plastic particles, so that the qualification degree of the modified plastic particles is determined, the quality of the produced modified plastic particles can be monitored at any time, and the processing parameters of the subsequent production periods can be appropriately adjusted based on the qualification degree of the modified plastic particles obtained in the production period, so that the qualification degree of the modified plastic particles in the whole production batch is improved.
In some embodiments, the eligibility of the modified plastic particles is also related to the mass average characteristics and the mass characteristic uniformity.
The mass average characteristic refers to an average of mass characteristics of a plurality of modified plastic particles. For more on the quality characteristics of the modified plastic particles, see the relevant description below.
In some embodiments, the processor may determine the quality average feature in a variety of ways. For example, the processor may average the mass characteristics of the plurality of modified plastic particles obtained from multiple samplings to obtain a mass average characteristic of the modified plastic particles.
Quality characteristic uniformity refers to the degree of uniformity of quality characteristics of a plurality of modified plastic particles. Wherein the uniformity of the quality features can be represented by real numbers ranging from 0 to 1, and the larger the numerical value is, the higher the uniformity degree of the quality features representing the plurality of modified plastic particles is.
In some embodiments, the processor may determine the quality feature uniformity in a variety of ways. For example, the processor may calculate the variance of the quality characteristics of the plurality of modified plastic particles obtained by the plurality of samplings, and then calculate the reciprocal of the variance to obtain the uniformity of the quality characteristics of the modified plastic particles.
In some embodiments, the greater the uniformity of the quality characteristics of the modified plastic particles, the greater the eligibility of the modified plastic particles. The smaller the vector distance between the mass average characteristic and the preset mass characteristic of the modified plastic particles, the higher the qualification degree of the modified plastic particles. Wherein, the preset quality characteristics can be set in advance based on the production requirements of the modified plastic particles. As for the manner of determining the vector distance between the quality average feature and the preset quality feature, reference may be made to the manner of determining the vector distance between the appearance average feature and the preset appearance feature described above.
In some embodiments, the processor may determine a quality average feature and quality feature uniformity based on the quality uniformity detection. In some embodiments, quality uniformity detection may include: and determining a quality average characteristic and the quality characteristic uniformity based on the dispersion degree of the quality characteristic of the modified plastic particles obtained by at least one sampling.
The mass characteristics of the modified plastic particles refer to characteristics related to the weight of the modified plastic particles. For example, the quality characteristics of the modified plastic particles may include, but are not limited to, total amount and weight of the modified plastic particles, and the like.
In some embodiments, the quality characteristics of the modified plastic particles may be represented in vector form. For example, the quality characteristics of the modified plastic particles may be expressed as (c, d). Wherein c represents the total number of modified plastic particles sampled at this time; d represents the total weight of the modified plastic particles sampled at this time.
The degree of dispersion of the quality features refers to parameter information related to the degree of uniformity of the quality features of the modified plastic particles. Wherein the dispersion degree of the quality features can be represented by real numbers between 0 and 1, and the smaller the numerical value is, the more uniform the quality features of the modified plastic particles are represented.
In some embodiments, the processor may determine the degree of dispersion of the quality features of the modified plastic particles in a variety of ways. For example, the processor may determine the degree of dispersion of the quality features of the modified plastic particles by calculating the variance of the plurality of quality features obtained from the plurality of samplings. The smaller the variance of the plurality of quality features obtained by the multiple sampling, the smaller the degree of dispersion of the quality features of the modified plastic particles.
In some embodiments, in the quality uniformity detection, each of the at least one sampling includes: the cooperative detection device is controlled to randomly grasp a plurality of modified plastic particles from the material receiving barrel through a mechanical arm, the modified plastic particles are placed on a detection table, and the total weight of the grasped modified plastic particles is measured through a pressure sensor arranged at the bottom of the detection table; controlling the detection table to vibrate based on preset vibration parameters, so that modified plastic particles are dispersed in the detection table; photographing the detection table through a vision device to obtain image data containing modified plastic particles; determining the total number of the modified plastic particles sampled at the time based on the image data; the quality characteristics of the modified plastic particles sampled this time are determined based on the total weight and the total amount of the modified plastic particles. For more on random grabbing, preset vibration parameters, image data see the relevant description above.
A pressure sensor refers to a device for measuring the amount of pressure applied. For example, the pressure sensor may include, but is not limited to, a capacitive pressure sensor, a piezoelectric pressure sensor, a piezoresistive pressure sensor, and the like.
In some embodiments, the processor may determine the total weight of the modified plastic particles currently being grasped in a variety of ways. For example, the processor may take the pressure value measured by the pressure sensor on the detection table as the total weight of the modified plastic particles that are grabbed this time.
In some embodiments, the processor may determine the total number of modified plastic particles that are sampled this time in a number of ways. For example, the processor may process the image data through a quantity determination model to determine the total quantity of modified plastic particles that are sampled this time. Wherein the quantity determination model may include one of a neural network model, a convolutional neural network model, or the like, or any combination thereof.
In some embodiments, the processor may determine the quality characteristics of the modified plastic particles that are sampled this time in a number of ways. For example, the processor may input the total number and the total weight of the modified plastic particles sampled at this time into the embedded layer to process, thereby obtaining the quality characteristics of the modified plastic particles sampled at this time.
In some embodiments of the specification, the quality characteristics of the modified plastic particles are determined by random sampling through the cooperative detection device, the qualification degree of the modified plastic particles in the production period is determined based on multiple sampling, and the limitation that the qualification degree of the modified plastic particles is determined only by means of the appearance quality characteristics of the modified plastic particles is avoided, so that the qualification degree of the finally determined modified plastic is more accurate, and a basis is provided for subsequent adjustment of processing parameters of other production periods of the same batch.
It should be noted that the above description of the flow 200 and the flow 300 is for illustration and description only, and is not intended to limit the scope of applicability of the present description. Various modifications and changes to flow 200 and flow 300 may be made by those skilled in the art under the guidance of this specification. However, such modifications and variations are still within the scope of the present description.
FIG. 4 is an exemplary schematic diagram of an appearance feature determination model shown in accordance with some embodiments of the present description.
In some embodiments, the processor may process the image data 410 through the appearance feature determination model 420 to determine the appearance features 450 of the modified plastic particles.
The appearance characteristic determining model 420 may be a machine learning model for determining appearance characteristics of the modified plastic particles. For example, the appearance feature determination model may include one of a Neural Network (NN) model, a convolutional Neural network (Convolutional Neural Networks, CNN) model, or the like, or any combination thereof.
In some embodiments, the input of the appearance feature determination model 420 may include image data 410; the output may include an appearance feature 450 of the modified plastic particles. For more on the appearance characteristics of the modified plastic particles, the image data can be seen in fig. 3 and the related description.
In some embodiments, appearance feature determination model 420 may also include an object detection layer 421 and an appearance feature identification layer 422.
The object detection layer 421 refers to a machine learning model for determining object boxes and object box classifications. In some embodiments, the object detection layer 421 may include a deep neural network model or the like.
In some embodiments, the input of the object detection layer 421 may include image data 410; the output may include at least one object box 430 containing modified plastic particles and an object box classification 440.
The object frame 430 containing the modified plastic particles refers to an image frame containing the modified plastic particles.
The object box classification refers to the kind of object box. For example, the object frame classification may include an object frame containing a single modified plastic particle, an object frame containing multiple modified plastic particles, and so on.
The appearance feature recognition layer 422 may be a machine learning model for determining appearance features of the modified plastic particles. In some embodiments, the appearance feature recognition layer 422 may include a deep neural network model or the like.
In some embodiments, the input of the appearance feature recognition layer 422 may include at least one object box 430 and object box classification 440 comprising modified plastic particles; the output may include an appearance feature 450 of the modified plastic particles in the object box.
In some embodiments, the input to the appearance feature identification layer 422 may also include a preset degree of openness 460, a preset extrusion speed 470, and a preset cutting speed 480.
The preset opening and closing degree refers to the opening and closing degree of a discharging hole of the processing bin.
The preset extrusion speed refers to the speed at which the modified plastic strip is extruded from the discharge aperture by the extrusion device. For example, the preset extrusion speed may be v a Cm per second.
The preset cutting speed refers to the cutting speed of the modified plastic strip by the cutting device. For example, the preset cutting speed may be v b Revolutions per minute.
In some embodiments, the processor may determine the preset opening and closing degree, the preset extrusion speed, and the preset cutting speed in a variety of ways. For example, the preset opening and closing degree, the preset extrusion speed and the preset cutting speed may be default values, empirical values, preset values in advance, etc. or any combination thereof, and may be determined according to actual requirements, which is not limited herein.
In some embodiments, the processor may further determine the preset opening and closing degree, the preset extrusion speed, and the preset cutting speed based on the composition distribution of the raw plastic, the composition distribution of the processing agent, the heating and pressurizing parameters, the cooling parameters, and the morphological requirements of the modified plastic particles corresponding to the current production lot. For more details on determining the preset opening and closing degree, the preset extrusion speed and the preset cutting speed, see fig. 5 and the description thereof.
In some embodiments of the present disclosure, if only image data is used as the input of the appearance feature determining model, no image data obtained by shooting at a single angle or at a plurality of angles may not ensure that appearance features such as volumes, shapes, etc. can be accurately identified by the appearance feature determining model. The shape of the modified plastic particles can be determined macroscopically to a certain extent by the preset opening and closing degree, the preset extrusion speed and the preset cutting speed. Therefore, the accuracy of the finally obtained appearance characteristics can be improved by taking the preset opening and closing degree, the preset extrusion speed and the preset cutting speed as the input of the appearance characteristic determining model.
In some embodiments, the object detection layer 421 and the appearance feature recognition layer 422 may be co-trained.
In some embodiments, the first training sample of the joint training may comprise sample image data of the sample modified plastic particles and the first label may comprise an appearance characteristic of the sample modified plastic particles. Wherein, the sample image data can be obtained by shooting sample modified plastic particles; the first label may be obtained manually based on the morphology (volume, shape, etc.) of the sample modified plastic particles.
In some embodiments, sample image data is input into an initial object detection layer, resulting in at least one object frame and object frame classification comprising modified plastic particles output by the initial object detection layer; and at least one object frame containing modified plastic particles and object frame classification are used as training samples, and the training samples are input into the initial appearance characteristic recognition layer to obtain the appearance characteristics of the modified plastic particles output by the initial appearance characteristic recognition layer. And constructing a loss function based on the sample appearance characteristics of the sample modified plastic particles and the appearance characteristics of the modified plastic particles output by the initial appearance characteristic identification layer, and synchronously and iteratively updating parameters of the initial object detection layer and the initial appearance characteristic identification layer based on the loss function. And when the loss function meets a first preset condition, model training is completed, and a trained object detection layer and appearance characteristic recognition layer are obtained. The first preset condition may be that the loss function converges, the number of iterations reaches a threshold value, and the like.
In some embodiments, the sample may further include a sample opening and closing degree, a sample extrusion speed, and a sample cutting speed played in the production of the sample modified plastic particles.
It can be seen in some embodiments of the present description that numerous features may reflect to some extent the appearance characteristics of the modified plastic particles. However, many times some of the features do not have effective values that can be used, and many times the available feature values are insufficient to determine the appearance of the modified plastic particles alone or by simple rules. Therefore, the appearance characteristics of the modified plastic particles can be determined based on a large number of wide characteristics by using the appearance characteristic determining model, and the limitation of determining the appearance characteristics of the modified plastic particles based on the rule in the prior art is broken. Rule-based approaches are limited by their complexity, can only be based on fewer features, and are limited by artificially defined rules. The prediction is performed based on the machine learning technology, so that the appearance characteristics of the determined modified plastic particles can be higher in accuracy based on more and richer characteristics, and meanwhile, the determination efficiency is improved.
Fig. 5 is an exemplary schematic diagram of determining a preset opening and closing degree, a preset extrusion speed, and a preset cutting speed according to some embodiments of the present description.
In some embodiments, the processor may determine the warming-pressurization parameters 530 and/or the cooling parameters 540 based on at least one of the composition profile 510 of the raw plastic, the composition profile 520 of the processing agent.
The composition distribution refers to the types and mass ratios of the components. For example, the composition distribution of the processing agent may be the mass of processing agent a: mass of the processing agent B: mass=m of processing agent C A :m B :m C
In some embodiments, the processor may determine the warming-pressurization parameter and/or the cooling parameter based on at least one of a composition profile of the feedstock plastic, a composition profile of the processing agent, and the like in a variety of ways. For example, the processor may construct the first target vector based on at least one of a composition profile of the raw plastic and a composition profile of the processing agent; determining, by the first vector database, a first association vector based on the first target vector; and determining the reference heating and pressurizing parameters and/or the reference cooling parameters corresponding to the first association vector as the heating and pressurizing parameters and/or the cooling parameters corresponding to the first target vector.
The first target vector refers to a vector constructed based on at least one of the composition distribution of the raw material plastic and the composition distribution of the processing agent. There are a number of ways to construct the first target vector. For example, the processor may process at least one of the composition distribution of the raw plastic and the composition distribution of the processing agent into the embedded layer to obtain the first target vector.
The first vector database comprises a plurality of first reference vectors, and each first reference vector in the plurality of first reference vectors has a corresponding reference temperature and pressure parameter and/or a reference cooling parameter.
The first reference vector is a vector constructed based on at least one of a historical composition distribution of the raw plastic and a historical composition distribution of the processing agent at the time of production in the historical time period, and the reference heating and pressurizing parameter and/or the reference cooling parameter corresponding to the first reference vector may be a historical heating and pressurizing parameter and/or a historical cooling parameter corresponding to the case that the qualification degree of the modified plastic particles produced in the historical time period is high. The construction method of the first reference vector can be referred to as the construction method of the first target vector. For more on the qualification of the modified plastic particles, see fig. 3 and the description thereof.
In some embodiments, the processor may calculate a vector distance between the first reference vector and the first target vector, respectively, and determine the warming-pressurization parameter and/or the cooling parameter of the first target vector. For example, a first reference vector whose vector distance from the first target vector satisfies a second preset condition is used as a first correlation vector, and a reference heating and pressurizing parameter and/or a reference cooling parameter corresponding to the first correlation vector is used as a heating and pressurizing parameter and/or a cooling parameter corresponding to the first target vector. The second preset condition may be set according to circumstances. For example, the second preset condition may be that the vector distance is minimum or that the vector distance is less than a distance threshold, or the like. Vector distances may include, but are not limited to, cosine distances, mahalanobis distances, euclidean distances, and the like.
In some embodiments, the discharge aperture of the processing bin has a preset opening and closing degree, the extrusion device can extrude the cooled and molded modified plastic strip from the discharge aperture of the processing bin based on a preset extrusion speed, and the cutting device can cut the modified plastic strip extruded from the discharge aperture of the processing bin based on a preset cutting speed. For more details on the preset opening and closing degree, preset extrusion speed and preset cutting speed, see fig. 4 and the description thereof.
In some embodiments, the processor may determine the preset degree of openness 460, the preset extrusion speed 470, and the preset cutting speed 480 based on the composition profile 510 of the raw plastic, the composition profile 520 of the processing agent, the warming-up pressurization parameter 530, the cooling parameter 540, and the morphology requirements 550 of the modified plastic particles corresponding to the current production lot.
The morphological requirement refers to a requirement related to morphological parameters of the modified plastic particles. For example, morphological demands may include, but are not limited to, volume size, color, shape, etc. of the modified plastic particles.
In some embodiments, the morphological requirements may include a predetermined appearance characteristic of the modified plastic particles.
The preset appearance characteristic refers to a parameter related to the appearance morphology of the modified plastic particles. For example, the preset appearance features may include, but are not limited to, volume size, color, shape, etc. of the modified plastic particles.
In some embodiments, the processor may determine the preset appearance characteristics in a variety of ways. For example, the predetermined appearance characteristics may be determined empirically based on the production requirements of the modified plastic particles. For another example, the processor may determine the predetermined appearance characteristics based on a predetermined data look-up table by modifying the production requirements of the plastic particles. Wherein, the preset data comparison table records preset appearance characteristics corresponding to different production requirements of different modified plastic particles. The preset data comparison table can be preset based on priori knowledge or historical data.
In some embodiments, the processor may determine the preset opening and closing degree, the preset extrusion speed, and the preset cutting speed by a variety of methods. For example, the processor may construct a second target vector based on the composition distribution of the raw plastic, the composition distribution of the processing agent, the warming-pressurizing parameters, the cooling parameters, and the morphological requirements of the modified plastic particles corresponding to the current production lot; determining, by the second vector database, a second association vector based on the second target vector; and determining the reference preset opening and closing degree, the reference preset extrusion speed and the reference preset cutting speed corresponding to the second association vector as the preset opening and closing degree, the preset extrusion speed and the preset cutting speed corresponding to the second target vector.
The second target vector is a vector constructed based on the composition distribution of the raw material plastic, the composition distribution of the processing agent, the heating and pressurizing parameters, the cooling parameters, and the morphological requirements of the modified plastic particles corresponding to the current production lot. The construction method of the second target vector can be referred to as the construction method of the first target vector.
The second vector database comprises a plurality of second reference vectors, and each of the plurality of second reference vectors has a corresponding reference preset opening and closing degree, a reference preset extrusion speed and a reference preset cutting speed.
The second reference vector is a vector constructed based on the historical composition distribution of the raw plastic, the historical composition distribution of the processing agent, the historical heating and pressurizing parameters, the historical cooling parameters and the historical morphological demands of the modified plastic particles corresponding to the historical current production batch during production in the historical time period; the reference preset opening and closing degree, the reference preset extrusion speed and the reference preset cutting speed corresponding to the second reference vector may be the historical preset opening and closing degree, the historical preset extrusion speed and the historical preset cutting speed corresponding to the case that the qualification degree of the modified plastic particles produced in the historical time period is high. The construction method of the second reference vector can be referred to as the construction method of the first target vector. For more on the qualification of the modified plastic particles, see fig. 3 and the description thereof.
In some embodiments, the processor may calculate a vector distance between the second reference vector and the second target vector, respectively, and determine a preset opening and closing degree, a preset extrusion speed, and a preset cutting speed of the second target vector. For example, a second reference vector whose vector distance from the second target vector satisfies a third preset condition is used as a second correlation vector, and a reference preset opening and closing degree, a reference preset extrusion speed, and a reference preset cutting speed corresponding to the second correlation vector are used as preset opening and closing degrees, preset extrusion speeds, and preset cutting speeds corresponding to the second target vector. The third preset condition may be set according to circumstances. For example, the third preset condition may be that the vector distance is minimum or that the vector distance is less than a distance threshold, or the like. Vector distances may include, but are not limited to, cosine distances, mahalanobis distances, euclidean distances, and the like.
In some embodiments, the processor may determine the preset opening and closing degree, the preset extrusion speed, and the preset cutting speed through a parameter determination model.
The parameter determination model may be a machine learning model for determining a preset opening and closing degree, a preset extrusion speed, and a preset cutting speed. For example, the parameter determination model may include one of a Neural Network (NN) model, a convolutional Neural network (Convolutional Neural Networks, CNN) model, or the like, or any combination thereof.
In some embodiments, the input of the parameter determination model may include a composition distribution of the raw plastic, a composition distribution of the processing agent, a warming-pressurizing parameter, a cooling parameter, and a morphological requirement of the modified plastic particles corresponding to the current production lot; the output may include a preset opening and closing degree, a preset extrusion speed, and a preset cutting speed.
In some embodiments, the parametric determination model may be obtained by training a plurality of second training samples with second labels. For example, a plurality of second training samples with second labels may be input into the initial parameter determination model, a loss function may be constructed from the outputs of the second labels and the initial parameter determination model, and parameters of the initial parameter determination model may be iteratively updated based on the loss function. And when the loss function of the initial parameter determination model meets a fourth preset condition, model training is completed, and a trained parameter determination model is obtained. The fourth preset condition may be that the loss function converges, the number of iterations reaches a threshold value, and so on.
In some embodiments, the second training sample of the parameter determination model may include a historical composition distribution of raw plastic at the time of production over a historical time period, a historical composition distribution of the processing agent, a historical warming-pressurization parameter, a historical cooling parameter, and a historical morphology requirement of the modified plastic particles corresponding to the historical current production lot. The second label may include a corresponding historical preset opening and closing degree, a historical preset extrusion speed, and a historical preset cutting speed in the case where the qualification of the modified plastic particles produced in the historical time period is high. The qualification degree of the modified plastic particles can be determined based on appearance average characteristics, appearance characteristic uniformity, quality average characteristics, quality characteristic uniformity and the like of the modified plastic particles. For more on the qualification of the modified plastic particles, see fig. 3 and the description thereof.
In some embodiments of the present disclosure, when determining the heating and pressurizing parameters, the preset opening and closing degree of the cooling parameters, the preset extrusion speed and the preset cutting speed, not only the composition distribution of the raw plastic and the composition distribution of the processing agent are considered, but also the morphological requirement of the modified plastic particles is considered, so that the finally produced modified plastic particles have higher qualification degree.
While the basic concepts have been described above, it will be apparent to those skilled in the art that the foregoing detailed disclosure is by way of example only and is not intended to be limiting. Although not explicitly described herein, various modifications, improvements, and adaptations to the present disclosure may occur to one skilled in the art. Such modifications, improvements, and modifications are intended to be suggested within this specification, and therefore, such modifications, improvements, and modifications are intended to be included within the spirit and scope of the exemplary embodiments of the present invention.
Meanwhile, the specification uses specific words to describe the embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic is associated with at least one embodiment of the present description. Thus, it should be emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various positions in this specification are not necessarily referring to the same embodiment. Furthermore, certain features, structures, or characteristics of one or more embodiments of the present description may be combined as suitable.
Furthermore, the order in which the elements and sequences are processed, the use of numerical letters, or other designations in the description are not intended to limit the order in which the processes and methods of the description are performed unless explicitly recited in the claims. While certain presently useful inventive embodiments have been discussed in the foregoing disclosure, by way of various examples, it is to be understood that such details are merely illustrative and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements included within the spirit and scope of the embodiments of the present disclosure. For example, while the system components described above may be implemented by hardware devices, they may also be implemented solely by software solutions, such as installing the described system on an existing server or mobile device.
Likewise, it should be noted that in order to simplify the presentation disclosed in this specification and thereby aid in understanding one or more inventive embodiments, various features are sometimes grouped together in a single embodiment, figure, or description thereof. This method of disclosure, however, is not intended to imply that more features than are presented in the claims are required for the present description. Indeed, less than all of the features of a single embodiment disclosed above.
In some embodiments, numbers describing the components, number of attributes are used, it being understood that such numbers being used in the description of embodiments are modified in some examples by the modifier "about," approximately, "or" substantially. Unless otherwise indicated, "about," "approximately," or "substantially" indicate that the number allows for a 20% variation. Accordingly, in some embodiments, numerical parameters set forth in the specification and claims are approximations that may vary depending upon the desired properties sought to be obtained by the individual embodiments. In some embodiments, the numerical parameters should take into account the specified significant digits and employ a method for preserving the general number of digits. Although the numerical ranges and parameters set forth herein are approximations that may be employed in some embodiments to confirm the breadth of the range, in particular embodiments, the setting of such numerical values is as precise as possible.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., referred to in this specification is incorporated herein by reference in its entirety. Except for application history documents that are inconsistent or conflicting with the content of this specification, documents that are currently or later attached to this specification in which the broadest scope of the claims to this specification is limited are also. It is noted that, if the description, definition, and/or use of a term in an attached material in this specification does not conform to or conflict with what is described in this specification, the description, definition, and/or use of the term in this specification controls.
Finally, it should be understood that the embodiments described in this specification are merely illustrative of the principles of the embodiments of this specification. Other variations are possible within the scope of this description. Thus, by way of example, and not limitation, alternative configurations of embodiments of the present specification may be considered as consistent with the teachings of the present specification. Accordingly, the embodiments of the present specification are not limited to only the embodiments explicitly described and depicted in the present specification.

Claims (10)

1. The modified plastic processing system is characterized by comprising a material taking device, a processing bin, a mixed heating and pressurizing device, a cooling and forming device, an extruding device, a cutting device, a material receiving barrel and a processor, wherein the mixed heating and pressurizing device, the cooling and forming device and the extruding device are positioned in the processing bin, and the material taking device and the cutting device are positioned outside the processing bin;
the processor is configured to:
controlling the material taking device to respectively grasp raw material plastics and a processing agent, and placing the raw material plastics and the processing agent into the processing bin;
controlling the mixing heating and pressurizing device to mix the raw material plastic and the processing agent in the processing bin based on mixing parameters, and heating and pressurizing based on heating and pressurizing parameters to obtain a molten mixture, wherein the mixing parameters comprise at least one of mixing strength and mixing duration;
In the processing bin, controlling the cooling forming device to cool and form the molten mixture based on cooling parameters, and controlling the extruding device to extrude the cooled and formed modified plastic strips from a discharge hole of the processing bin; and
and controlling the cutting device to cut the modified plastic strips extruded from the discharging holes to obtain modified plastic particles, wherein the product receiving barrel is used for receiving the modified plastic particles.
2. The system of claim 1, further comprising a collaboration detection device comprising a robot, a vision device, and a detection station, the processor further configured to:
controlling the cooperative detection device to perform uniformity detection on the modified plastic particles in each production period of the same production batch, wherein the uniformity detection comprises appearance uniformity detection;
the appearance uniformity detection includes:
determining appearance quality characteristics of the modified plastic particles based on the dispersion degree of the appearance characteristics of the modified plastic particles obtained by at least one sampling, wherein the appearance quality characteristics comprise at least one of appearance average characteristics and appearance characteristic uniformity;
Determining a pass of the modified plastic particles based at least on the appearance quality characteristics of the modified plastic particles;
each of the at least one sampling includes:
controlling the cooperative detection device to randomly grasp a plurality of modified plastic particles from the material receiving barrel through the manipulator, and placing the modified plastic particles on the detection table;
performing image acquisition operation of preset acquisition times, wherein the image acquisition operation comprises the following steps:
controlling the detection table to vibrate based on preset vibration parameters, so that the modified plastic particles are dispersed in the detection table; controlling the vision device to take a picture of the detection table so as to acquire image data containing the modified plastic particles;
determining the appearance characteristics of the modified plastic particles sampled at this time based on the image data acquired by the preset acquisition times;
and controlling the opening and closing of the bottom of the detection table to enable the modified plastic particles sampled at the time to flow back into the material receiving barrel.
3. The system of claim 1, wherein the mixing parameter is determined based on at least one of an additive amount ratio, a monomer volume ratio, and a monomer mass ratio of the raw plastic to the processing agent.
4. The system of claim 1, wherein the warming and pressurizing parameters and/or the cooling parameters are determined based on at least one of a composition distribution of the raw plastic, a composition distribution of the processing agent.
5. A modified plastic processing method, which is characterized in that the method is executed by a processor of a modified plastic processing system, wherein the modified plastic processing system comprises a material taking device, a processing bin, a mixed heating and pressurizing device, a cooling forming device, an extruding device, a cutting device, a material receiving barrel and the processor;
the method comprises the following steps:
controlling the material taking device to respectively grasp raw material plastics and a processing agent, and placing the raw material plastics and the processing agent into the processing bin;
controlling the mixing heating and pressurizing device to mix the raw material plastic and the processing agent in the processing bin based on mixing parameters, and heating and pressurizing based on heating and pressurizing parameters to obtain a molten mixture, wherein the mixing parameters comprise at least one of mixing strength and mixing duration;
in the processing bin, controlling the cooling forming device to cool and form the molten mixture based on cooling parameters, and controlling the extruding device to extrude the cooled and formed modified plastic strips from a discharge hole of the processing bin; and
And controlling the cutting device to cut the modified plastic strips extruded from the discharge holes to obtain modified plastic particles.
6. The method of claim 5, wherein the method further comprises:
controlling a cooperative detection device to perform uniformity detection on the modified plastic particles in each production period of the same production batch, wherein the uniformity detection comprises appearance uniformity detection;
the appearance uniformity detection includes:
determining appearance quality characteristics of the modified plastic particles based on the dispersion degree of the appearance characteristics of the modified plastic particles obtained by at least one sampling, wherein the appearance quality characteristics comprise at least one of appearance average characteristics and appearance characteristic uniformity;
determining a pass of the modified plastic particles based at least on the appearance quality characteristics of the modified plastic particles;
each of the at least one sampling includes:
controlling the cooperative detection device to randomly grasp a plurality of modified plastic particles from the material receiving barrel through a mechanical arm, and placing the modified plastic particles on a detection table;
performing image acquisition operation of preset acquisition times, wherein the image acquisition operation comprises the following steps:
Controlling the detection table to vibrate based on preset vibration parameters, so that the modified plastic particles are dispersed in the detection table; controlling a vision device to take a picture of the detection table so as to obtain image data containing the modified plastic particles;
determining the appearance characteristics of the modified plastic particles sampled at this time based on the image data acquired by the preset acquisition times;
and controlling the opening and closing of the bottom of the detection table to enable the modified plastic particles sampled at the time to flow back into the material receiving barrel.
7. The method of claim 5, wherein the mixing parameter is determined based on at least one of an additive amount ratio, a monomer volume ratio, and a monomer mass ratio of the raw material plastic to the processing agent.
8. The method according to claim 5, wherein the heating and pressurizing parameter and/or the cooling parameter are determined based on at least one of a composition distribution of the raw material plastic and a composition distribution of the processing agent.
9. A modified plastic processing device, the device comprising at least one processor and at least one memory;
the at least one memory is configured to store computer instructions;
The at least one processor is configured to execute at least some of the computer instructions to implement the method of any one of claims 5-8.
10. A computer readable storage medium storing computer instructions which, when executed by a processor, implement a method as claimed in any one of claims 5 to 8.
CN202310257471.XA 2023-03-16 2023-03-16 Modified plastic processing system and method Active CN116214762B (en)

Priority Applications (2)

Application Number Priority Date Filing Date Title
CN202311324486.XA CN117261028A (en) 2023-03-16 2023-03-16 Automatic control system and method for modified plastic production
CN202310257471.XA CN116214762B (en) 2023-03-16 2023-03-16 Modified plastic processing system and method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202310257471.XA CN116214762B (en) 2023-03-16 2023-03-16 Modified plastic processing system and method

Related Child Applications (1)

Application Number Title Priority Date Filing Date
CN202311324486.XA Division CN117261028A (en) 2023-03-16 2023-03-16 Automatic control system and method for modified plastic production

Publications (2)

Publication Number Publication Date
CN116214762A true CN116214762A (en) 2023-06-06
CN116214762B CN116214762B (en) 2023-09-29

Family

ID=86585613

Family Applications (2)

Application Number Title Priority Date Filing Date
CN202310257471.XA Active CN116214762B (en) 2023-03-16 2023-03-16 Modified plastic processing system and method
CN202311324486.XA Pending CN117261028A (en) 2023-03-16 2023-03-16 Automatic control system and method for modified plastic production

Family Applications After (1)

Application Number Title Priority Date Filing Date
CN202311324486.XA Pending CN117261028A (en) 2023-03-16 2023-03-16 Automatic control system and method for modified plastic production

Country Status (1)

Country Link
CN (2) CN116214762B (en)

Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4213747A (en) * 1977-11-16 1980-07-22 Werner & Pfleiderer Method of and apparatus for controlling the viscosity of molten plastics material which is to be moulded
US4919536A (en) * 1988-06-06 1990-04-24 Northrop Corporation System for measuring velocity field of fluid flow utilizing a laser-doppler spectral image converter
JPH1090192A (en) * 1996-08-29 1998-04-10 Kla Instr Corp Optical inspection of specimen using multi-channel response from the specimen
DE19820948C1 (en) * 1998-05-11 1999-09-23 Ocs Optical Control Systems Gm Machine controlling plastic granule quality under realistic conditions
US20050099625A1 (en) * 2003-11-09 2005-05-12 Budd Gerald W. NIST traceable automated visual inspection system for an inspection of particles in solution
CN203637196U (en) * 2013-12-26 2014-06-11 江苏奔迪新材料科技有限公司 Equipment for producing polyolefin foamed plastic product
CN106974659A (en) * 2017-05-20 2017-07-25 复旦大学 A kind of latent fingerprint detection method based on red fluorescence carbon point material
CN110871520A (en) * 2019-12-12 2020-03-10 成都瑞琦医疗科技有限责任公司 Granulation system of organic composite material master batch
CN113334609A (en) * 2021-06-03 2021-09-03 金龙鹰电工科技有限公司 Square low smoke and zero halogen plastic granules shaping extrusion device
CN214187943U (en) * 2020-12-28 2021-09-14 瑞思埃克(成都)新材料科技有限责任公司 Modified plastic apparatus for producing
CN215283256U (en) * 2021-06-15 2021-12-24 苏州博之顺材料科技有限公司 Double-screw extruder for producing and processing resin polymer
CN114474473A (en) * 2022-02-08 2022-05-13 苏州博之顺材料科技有限公司 Production method and system of modified plastic

Patent Citations (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US4213747A (en) * 1977-11-16 1980-07-22 Werner & Pfleiderer Method of and apparatus for controlling the viscosity of molten plastics material which is to be moulded
US4919536A (en) * 1988-06-06 1990-04-24 Northrop Corporation System for measuring velocity field of fluid flow utilizing a laser-doppler spectral image converter
JPH1090192A (en) * 1996-08-29 1998-04-10 Kla Instr Corp Optical inspection of specimen using multi-channel response from the specimen
DE19820948C1 (en) * 1998-05-11 1999-09-23 Ocs Optical Control Systems Gm Machine controlling plastic granule quality under realistic conditions
US20050099625A1 (en) * 2003-11-09 2005-05-12 Budd Gerald W. NIST traceable automated visual inspection system for an inspection of particles in solution
CN203637196U (en) * 2013-12-26 2014-06-11 江苏奔迪新材料科技有限公司 Equipment for producing polyolefin foamed plastic product
CN106974659A (en) * 2017-05-20 2017-07-25 复旦大学 A kind of latent fingerprint detection method based on red fluorescence carbon point material
CN110871520A (en) * 2019-12-12 2020-03-10 成都瑞琦医疗科技有限责任公司 Granulation system of organic composite material master batch
CN214187943U (en) * 2020-12-28 2021-09-14 瑞思埃克(成都)新材料科技有限责任公司 Modified plastic apparatus for producing
CN113334609A (en) * 2021-06-03 2021-09-03 金龙鹰电工科技有限公司 Square low smoke and zero halogen plastic granules shaping extrusion device
CN215283256U (en) * 2021-06-15 2021-12-24 苏州博之顺材料科技有限公司 Double-screw extruder for producing and processing resin polymer
CN114474473A (en) * 2022-02-08 2022-05-13 苏州博之顺材料科技有限公司 Production method and system of modified plastic

Also Published As

Publication number Publication date
CN117261028A (en) 2023-12-22
CN116214762B (en) 2023-09-29

Similar Documents

Publication Publication Date Title
US10416617B2 (en) Injection molding system for the calculation of optimum operating conditions and machine learning therefor
EP3632650B1 (en) Injection molding system and injection molding method
CN114474473B (en) Production method and system of modified plastic
CN115122602B (en) Intelligent control method and system for injection molding machine
CN109968671B (en) 3D printing process fault diagnosis method and device
JP6573647B2 (en) An injection molding machine system consisting of a computer and multiple injection molding machines
Huang et al. Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality
CN113165243A (en) Method and system for improving a physical production process
CN116214762B (en) Modified plastic processing system and method
CN114801050B (en) Feeding device of injection molding machine and operation method thereof
Chung et al. Reinforcement learning-based defect mitigation for quality assurance of additive manufacturing
US11660799B2 (en) Method for controlling film production
US11541587B2 (en) Method for controlling film production
CN116494420B (en) Modified plastic production control system and method
CN111428329B (en) Model-based machine learning system
EP3599457A1 (en) Characterization of melted veil strand ratios in plies of fiber material
CN117008557B (en) Production control method and system for blending type interpenetrating network thermoplastic elastomer
Alhindawi et al. Predicting the tensile strength of extrusion-blown high density polyethylene film using machine learning algorithms
CN114986833B (en) Dynamically regulated injection molding method, system, device and medium
CN116414095A (en) Data-driven optimization method for technological parameters in traditional Chinese medicine manufacturing process
Deo et al. Detection of material build-up during online size distribution analysis of iron ore green pellets using MobilenetV2
WO2022210424A1 (en) Quality prediction device and method therefor, program, and storage medium
CN114829102A (en) Learning model generation method, computer program, set value determination device, molding machine, and molding device system
KR20200143166A (en) Method and apparatus for predicting defect of sheet during the ceramic progress
CN114734601B (en) Product flaw online detection method in injection molding process

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant