CN114801100A

CN114801100A - Injection molding sorting unit

Info

Publication number: CN114801100A
Application number: CN202210443731.8A
Authority: CN
Inventors: 杨济宇; 蔡峻峰; 张争鸣
Original assignee: Kenta Enterprise Co ltd
Current assignee: Kenta Enterprise Co ltd
Priority date: 2022-04-26
Filing date: 2022-04-26
Publication date: 2022-07-29
Anticipated expiration: 2042-04-26
Also published as: CN116423785A; CN116423785B; CN114801100B

Abstract

The embodiment of the specification provides an injection molding sorting device, which comprises a controller, a sample conveying channel, a quality detection unit and a plurality of sample sorting channels. The sample conveying channel is used for conveying injection molding products produced by the injection molding machine to the quality detection unit. The quality detection unit is used for detecting the quality of the injection molding product. Each of the plurality of sample sorting channels is used for conveying the injection molded product subjected to the quality inspection to the corresponding storage space.

Description

Injection molding sorting unit

Technical Field

The specification relates to the technical field of injection molding production, in particular to an injection molding product sorting device.

Background

Injection molding is a common industrial product production method. With the wide use of injection molding products, the rapid development of the injection molding industry and the continuous enhancement of product competition, people have higher and higher requirements on the quality and the service performance of the injection molding products. After the injection molding is completed, a manufacturer needs to determine the quality of the injection molded product to know whether the injection molded product has defects, such as short shot, shrinkage, bending, flash, cracks, scratches, and the like. In actual production, a producer is difficult to directly judge the quality of an injection molding product and needs to perform subsequent detection.

Therefore, there is a need for an injection molding product sorting apparatus that can reduce unnecessary labor input and improve the accuracy of quality inspection of injection molding products.

Disclosure of Invention

One of the embodiments of the present specification provides an injection molding sorting apparatus. The injection molding sorting device comprises: the device comprises a controller, a sample conveying channel, a quality detection unit and a plurality of sample sorting channels. The sample conveying channel is used for conveying injection molding products produced by the injection molding machine to the quality detection unit, the quality detection unit is used for detecting the quality of the injection molding products, and each sample sorting channel in the plurality of sample sorting channels is used for conveying the injection molding products with the quality detected to the corresponding storage space.

In some embodiments, the quality detection unit is provided with a camera and a rotation stage. The revolving stage is used for placing injection moulding, camera device is used for based on the rotation of revolving stage, shoots injection moulding is used for quality testing's product image at a plurality of angles.

In some embodiments, the quality detection unit is provided with a quality parameter detection device. The quality parameter detection equipment is used for detecting the quality of the injection molding product.

In some embodiments, the controller is configured to count the number of the injection molding products corresponding to each type of quality detection result, and send warning information to an injection molding machine or a terminal of a manufacturer based on the count result of the number of the products.

Drawings

The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:

FIG. 1 is a diagram of an application scenario of an injection molded article sorting apparatus according to some embodiments herein;

FIG. 2 is an illustration of an injection molded article sorting apparatus according to some embodiments herein;

FIG. 3 is an exemplary diagram of a process for determining whether an injection molded article has been introduced into a quality parameter detection device in accordance with some embodiments of the present description;

FIG. 4 is an exemplary flow diagram illustrating the determination of a region image according to some embodiments of the present description;

FIG. 5 is an exemplary diagram illustrating one role of a controller according to some embodiments herein.

Detailed Description

In order to more clearly illustrate the technical solutions of the embodiments of the present disclosure, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.

It should be understood that "system", "apparatus", "unit" and/or "module" as used herein is a method for distinguishing different components, elements, parts, portions or assemblies at different levels. However, other words may be substituted by other expressions if they accomplish the same purpose.

As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements.

Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.

The embodiment of the present specification relates to an injection molding sorting apparatus, which can be used for quality detection and sorting of injection molding, and it should be noted that the injection molding sorting apparatus in the embodiment can also be used for quality detection and sorting of other products, such as quality detection and sorting of other kinds of products, such as food, electronic products, and the like.

Fig. 1 is a diagram of an application scenario of an injection molded article sorting apparatus according to some embodiments of the present disclosure.

In some embodiments, the application scenario 100 of the injection molding article sorting apparatus may include a computing system 110, a network 120, a storage device 130, a terminal 140, an injection molding article sorting apparatus 150, an injection molding machine 160, and a material 170.

Computing system 110 may process data and/or information retrieved from storage device 130. For example, the computing system 110 can retrieve injection molded article sorting data from the storage device 130. In some embodiments, the computing system 110 can process information and/or data related to the injection molded article sorting apparatus 150 to perform one or more of the functions described herein. In some embodiments, computing system 110 may be a single server or a cluster of servers. The server farm can be centralized or distributed (e.g., computing system 110 can be a distributed system). In some embodiments, the computing system 110 may be local or remote. For example, computing system 110 accesses information and/or data stored in storage device 130 over network 120. As another example, computing system 110 may be directly connected to storage device 130 to access stored information and/or data. In some embodiments, the computing system 110 may be implemented by a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, between clouds, multiple clouds, the like, or any combination thereof.

In some embodiments, the computing system 110 may include one or more computing systems 110 (e.g., a single-core processor or a multi-core processor). By way of example only, the computing system 110 may include one or more hardware processors, such as a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), an image processor (GPU), a physical arithmetic unit (PPU), a Digital Signal Processor (DSP), a field-programmable gate array (FPGA), a Programmable Logic Device (PLD), a controller, a micro-controller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, and the like, or any combination thereof.

Network 120 may be used to facilitate the exchange of information and/or data. In some embodiments, one or more components in the scenario 100 may send and/or receive information and/or data to/from other components in the scenario 100 via the network 120. In some embodiments, the network 120 may be any form or combination of wired or wireless network. By way of example only, network 120 may include a cable network, a wireline network, a fiber optic network, a telecommunications network, an intranet, the Internet, a Local Area Network (LAN), a Wide Area Network (WAN), a Wireless Local Area Network (WLAN), a Metropolitan Area Network (MAN), a Public Switched Telephone Network (PSTN), a Bluetooth network, a ZigBee network, a Near Field Communication (NFC) network, a global system for mobile communications (GSM) network, a Code Division Multiple Access (CDMA) network, a Time Division Multiple Access (TDMA) network, a General Packet Radio Service (GPRS) network, an enhanced data rates for GSM evolution (EDGE) network, a Wideband Code Division Multiple Access (WCDMA) network, a High Speed Downlink Packet Access (HSDPA) network, a Long Term Evolution (LTE) network, a User Datagram Protocol (UDP) network, a Transmission control protocol/Internet protocol (TCP/IP) network, a Short Message Service (SMS) network, a Wireless Application Protocol (WAP) network, An ultra-wideband (UWB) network, a mobile communication (1G, 2G, 3G, 4G, 5G) network, Wi-Fi, Li-Fi, a narrowband Internet of things (NB-IoT), and the like, or any combination thereof.

In some embodiments, network 120 may include one or more network access points. For example, the network 120 may include wired or wireless network access points (e.g., base stations and/or internet switching points) through which one or more components of the scenario 100 may connect to the network 120 to exchange data and/or information.

Storage device 130 may be used to store information and/or instructions. In some embodiments, storage 130 may include mass storage, removable storage, Random Access Memory (RAM), Read Only Memory (ROM), and the like, or any combination thereof. Exemplary mass storage may include magnetic disks, optical disks, solid state disks, and the like. Exemplary removable memory may include flash drives, floppy disks, optical disks, memory cards, compact disks, magnetic tape, and the like. Exemplary random access memories may include Dynamic RAM (DRAM), double-data-rate synchronous dynamic RAM (DDR SDRAM), Static RAM (SRAM), thyristor RAM (T-RAM), zero-capacitance RAM (Z-RAM), and the like. Exemplary read-only memories may include Mask ROM (MROM), Programmable ROM (PROM), erasable programmable ROM (PEROM), Electrically Erasable Programmable ROM (EEPROM), compact disk ROM (CD-ROM), digital versatile disk ROM, and the like. In some embodiments, storage device 130 may be implemented by a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, an internal cloud, a multi-tiered cloud, and the like, or any combination thereof.

In some embodiments, a storage device 130 may be connected to the network 120 to communicate with one or more components in the scenario 100. One or more components in the scenario 100 may access data or instructions stored in the storage device 130 through the network 120. In some embodiments, the storage device 130 may be directly connected or in communication with one or more components in the scenario 100.

The terminal 140 can be a device or other entity directly related to injection molded article sorting data acquisition. In some embodiments, terminal 140 may be a terminal used by a user. Such as terminals used by manufacturing personnel. In some embodiments, the terminal 140 may be a requester of injection molded article sorting data acquisition. In some embodiments of the present description, "user", "user terminal" may be used interchangeably. In some embodiments, the user can be an operator or user of the injection molded article sorting apparatus 150, such as a manufacturer, a researcher, or the like. In some embodiments, the terminals 140 may include mobile terminals 140-1, tablet computers 140-2, notebook computers 140-3, laptop computers 140-4, and the like, or any combination thereof. In some embodiments, mobile terminal 140-1 may comprise a smartphone, smart paging device, or the like, or other smart device. In some embodiments, the terminal 140 may include other smart terminals, such as wearable smart terminals and the like. In some embodiments, the terminal 140 can receive information uploaded by the injection molding sorting device 150, such as injection molding sorting data, via the network 120.

The injection molding sorting device 150 is a device for sorting and screening injection molding. In some embodiments, the injection molding sorting device 150 may sort and screen the injection molding produced by the injection molding machine 160, and deliver the sorted injection molding to the corresponding storage space.

In some embodiments, the injection molded article sorting device 150 can include a controller, a sample transport channel, a quality detection unit, a plurality of sample sorting channels. The sample transport path may be used to transport injection molded articles produced by the injection molding machine 160 to a quality inspection unit. The quality detection unit can be used for detecting the quality of the injection molding product. Each of the plurality of sample sorting channels may be used to transport the injection molded article for which the quality inspection is completed to a corresponding storage space. The controller may be configured to receive and/or count information and/or data about the injection molded articles to adjust and/or perform one or more functions associated with sorting the injection molded articles. For example, the controller can perform a quality check on the injection molded article based on the image of the injection molded article. For another example, the controller may count the proportion of injection molded articles that have quality problems and adjust the frequency of delivery of the injection molded articles. The description of the injection molded article sorting device 150 can be seen in fig. 2, and will not be repeated herein.

The injection molding machine 160, which may also be referred to as an injection molding machine or an injection molding machine, is a main molding apparatus for molding thermoplastic plastics or thermosetting plastics into various shapes of plastic products using a plastic molding die. Injection molding machine 160 may include, but is not limited to, a vertical injection molding machine, a horizontal injection molding machine, and an all-electric injection molding machine. In some embodiments, the injection molding machine 160 may be comprised of a fixed platen, a moving platen, tie bars, mold closing cylinders, linkage mechanisms, mold adjustment mechanisms, and article ejection mechanisms, among others.

Injection molding machine 160 may include a mold 160-1. Mold 160-1 refers to a device for injecting a thermoplastic or thermoset into a mold to form an article. In some embodiments, the mold 160-1 may be located between posts on the injection molding machine 160. In some embodiments, mold 160-1 may be secured to injection molding machine 160 by screws, or may be secured to injection molding machine 160 by a platen (e.g., an automated platen).

Material 170 refers to an injection moldable material for injection molding. The material 170 may include, but is not limited to, polymer materials such as ABS plastic, polyamide 6, PBT, PC, and the like. The material 170 may be selected differently depending on the application, for example, for automobiles (e.g., instrument panels, wheel covers, etc.), refrigerators, and telephone housings, ABS plastic (terpolymer of acrylonitrile, butadiene, and styrene) may be selected. For another example, polyamide 6 may be selected for structural members that need to have good mechanical strength.

It should be noted that the injection molded article sorting apparatus is provided for illustrative purposes only and is not intended to limit the scope of the present application. It will be apparent to those skilled in the art that various modifications and variations can be made in light of the description herein. For example, the injection molded article sorting apparatus can further include an information source. For another example, the injection molded article sorting apparatus may perform similar or different functions on other devices. However, such changes and modifications do not depart from the scope of the present application.

FIG. 2 is an illustration of an injection molded article sorting apparatus according to some embodiments herein.

As shown in fig. 2, the injection molding sorting apparatus 200 includes a controller 210, a sample conveyance path 220, a quality detection unit 230, and a plurality of sample sorting paths 240. The following is a detailed description of each section.

The controller 210 may be used for data processing. In some embodiments, a controller may include a processor and a storage medium. It is understood that the storage medium stores corresponding programs and/or instructions, and when the processor reads the corresponding programs and/or instructions, the processor can acquire the data to be processed and perform corresponding processing on the data. Further, the data to be processed may come from other components (e.g., quality detection units, etc.) in the injection molded article sorting apparatus.

In some embodiments, the controller may store a machine learning model, and when the controller processes data, the machine learning model (e.g., recognition model 220-1 in fig. 2) may be invoked to process the data to be processed.

The sample transport path 220 may be used to transport injection molded articles produced by the injection molding machine to a quality inspection unit. In some embodiments, the sample transport path can be any article transport device, such as a conveyor belt, a transfer plate, or the like. For example, the injection-molded articles produced by the injection-molding machine may be placed on a conveyor belt and transported to a quality inspection unit.

The injection molded article is a product obtained by injection molding of a raw material for injection molding such as a resin, and for example, a molten raw material for resin can be fed into a cavity by an injection molding machine under high pressure, and cooled and solidified to obtain an injection molded article. It is understood that the injection molded article may have various quality problems (e.g., shrinkage cavity, shrinkage, unsaturation, flash, etc.) due to various reasons (e.g., pressure level, dwell condition, temperature, etc.) during the production process of the injection molded article by the injection molding machine, so that the injection molded article may not be used or may not be used well.

The quality detection unit 230 may be used to detect the quality of the injection molded article, determine whether there is a quality problem with the injection molded article, or what kind of quality problem exists. In some embodiments, the quality detection unit may include devices, equipment, models, and the like for quality detection of the injection molded article, for example, the quality detection unit may include a quality detector, an image analysis device, an image recognition model, and the like.

In some embodiments, the quality detection unit may be provided with a camera 230-1 and a rotation stage 230-2. The rotating platform is used for placing injection molding products, and the camera device is used for shooting product images of the injection molding products at a plurality of angles for quality detection based on the rotation of the rotating platform. It can be understood that images of a plurality of angles of the injection molding product can be acquired through the matching of the camera device and the rotating table, and then the quality of the injection molding product is detected through the image of the injection molding product.

In some embodiments, the camera device may be a camera. In some embodiments, the camera device may include at least two cameras by which it may be possible to obtain images of the injection molded article at least two angles based on at least two positional directions.

In some embodiments, the rotation stage may comprise a table top that can be rotated, e.g., a lateral rotation stage, with the table top rotating in a horizontal direction; and a vertical rotary table, the table top of which rotates in the vertical direction. It is understood that the table of the rotating table can be used for placing the injection molding product, so that the injection molding product can rotate horizontally or vertically on the table, and therefore the image of the injection molding product at multiple angles can be captured by the image capturing device.

In some embodiments, the quality detection unit can perform quality detection through the surface corresponding to a plurality of angles of the injection molded product. It will be appreciated that the camera is arranged to capture a plurality of angles of the injection molded article rotating on the turntable, depending on the plurality of angles actually required. For example, the injection molded product is photographed by a camera while rotating, and images of the injection molded product at a plurality of angles such as front view, rear view, left view, and right view are acquired.

In some embodiments, the quality detection unit may be provided with a quality parameter detection device for detecting the quality of the injection molded article.

The quality parameter detection equipment can be equipment and a device for detecting the quality parameters of the injection molding products. In some embodiments, as shown in FIG. 2, the quality parameter detecting device 230-4 may include a density detecting means 230-4-1 for detecting the density of the injection molded article, a hardness detecting means 230-4-2 for detecting the hardness of the injection molded article, a scanning means 230-4-3 for detecting the appearance of the injection molded article, and the like. Wherein, the density detection device can be a densimeter; the hardness detection device can be a shore durometer or a hardness tester; the scanning device may be a Scanning Electron Microscope (SEM).

In some embodiments, the quality parameter detection device detects a corresponding quality parameter of the injection molded article by placing the injection molded article in a detection zone of the quality parameter detection device. The quality parameter may include, for example, one or more of a density parameter, a hardness parameter, an appearance shape, a color, a composition, and the like. For example, the injection molded article is placed in a detection area of a densitometer to measure a density parameter of the injection molded article. For another example, the injection molded article is placed in a detection area of a hardness tester, and a hardness parameter of the injection molded article is measured based on the hardness tester. For another example, the injection molded product is placed in a scanning area of a scanning electron microscope, and then the injection molded product is scanned and inspected to obtain, for example, shape parameters, composition parameters, and the like.

In some embodiments, the quality detection unit further comprises: and the sample detection channel is used for conveying the injection molding product of the rotating table to the quality parameter detection equipment.

The sample detection channel may be a device having an object-moving function. For example, a conveyor such as a conveyor belt.

In some embodiments, the sample detection channel may be connected to a rotation stage. It is understood that after the injection molded product in the rotating table is subjected to image acquisition at multiple angles for quality inspection by the rotating table, the injection molded product can be conveyed to a quality parameter inspection device (such as a densitometer, a hardness tester, or a scanning electron microscope) through a sample inspection channel (such as a conveyor belt) for corresponding quality parameter inspection.

In some embodiments, the sample sorting channel is used to transport injection molded articles for which quality testing is completed to the corresponding storage space. In some embodiments, the sample sorting channel may be a device or apparatus having article transport functionality. Such as conveyor belts, conveyor bars.

In some embodiments, the sample sort channels may include a quality pass channel, a quality problem 1 channel, a quality problem 2 channel … … a quality problem n channel. It is understood that, as shown in fig. 2, the plurality of sample sorting channels 240 include a sample sorting channel 240-1, a sample sorting channel 240-2, a sample sorting channel 240-3, a sample sorting channel 240-4, and the like, and the plurality of samples may be transported to the corresponding storage spaces according to the quality detection results when the quality detection is completed. In some embodiments, each sample sorting channel delivers injection molded articles of the same quality test result, e.g., sample sorting channel 240-1 is used to deliver injection molded articles of acceptable quality to a corresponding storage space, and sample sorting channel 240-2 may be used to deliver injection molded articles of quality problem 1 to a corresponding storage space.

In some embodiments, the quality problems of the injection molded article can be classified into various types based on specific product conditions, for example, the quality problems of the injection molded article can include appearance quality problems (such as burrs, weld marks, cracks, and the like), density quality problems (such as density exceeding a product specification range), and hardness quality problems (such as hardness exceeding a product specification range).

The corresponding storage space may be a storage location for storing the injection molded article corresponding to the detection result, for example, a location for storing an injection molded article of a qualified quality, a space for storing an injection molded article having a quality problem 1, or the like. As can be understood, each corresponding storage space stores injection-molded products with the same quality detection result. For example, the spaces 1 store injection-molded articles of acceptable quality.

In some embodiments, the controller 210, the sample conveying path 220, the quality detecting unit 230, and the plurality of sample data paths 240 in fig. 2 can perform multi-directional quality detection on the injection-molded product, and store the injection-molded product in a sorted manner based on quality conditions (e.g., quality qualification, appearance problem, hardness problem, etc.). The quality detection unit 230 can conveniently acquire a plurality of detection parameters of the injection molding product, detect the quality of the injection molding product, and simultaneously realize the classified transmission and storage of the injection molding product based on the quality detection result so as to realize the classified processing and management of the injection molding product.

FIG. 3 is an exemplary diagram illustrating quality detection based on recognition models in accordance with some embodiments of the present description.

In some embodiments, the controller can be configured to receive an article image and perform a quality check on the injection molded article based on the identified model. In some embodiments, the quality detection based on the recognition model can detect whether the injection-molded product has a certain quality problem, for example, the detection of the injection-molded product based on the recognition model can result in that the injection-molded product has an appearance quality problem (such as cracking/breaking, shrinkage cavity, weld mark, etc.), and it can be understood that the quality detection result can also be a hardness quality problem, a density quality problem, etc.

In some embodiments, as shown in FIG. 3, the recognition model may comprise a first image recognition model.

In some embodiments, the quality problem of the injection-molded product can be detected through the first image recognition model based on one or more images (e.g., images at multiple angles) of the injection-molded product, and in some embodiments, the quality problem detection result obtained based on the first image recognition model can be mainly the detection result of appearance-type quality problems, such as appearance quality problems of the injection-molded product, such as cracking/breaking, shrinkage cavity, weld mark, and the like.

In some embodiments, the first image recognition model may be a neural network model.

In some embodiments, the input of the first image recognition model may be an image of the injection molded article, for example, an image of the injection molded article taken by a camera from a rotational stage at a plurality of angles (e.g., front, back, left, right). The output of the first image recognition model may be the current quality of the injection molded article, e.g., acceptable quality, appearance quality problems, etc. In some embodiments, the first image recognition model may also output the type of appearance quality problem that exists specifically, e.g., the first image recognition model may output a specific problem type label such as flash, rip, etc.

In some embodiments, the first image recognition model may be trained based on a plurality of sets of labeled training samples. Specifically, a training sample with a label is input into a first image recognition model, and parameters of the first image recognition model are trained.

In some embodiments, the set of training samples may include: images of historical injection molded articles at multiple angles (e.g., front, back, left, right). In some embodiments, the training sample may be obtained by photographing the historical injection-molded article on the rotating table by the camera. In some embodiments, the training sample may also be obtained from stored historical images of historical injection molded articles.

In some embodiments, the label at the time of model training may be a quality issue type of the historical injection molded article (e.g., quality pass, shrinkage cavity, flash, mold unsaturation, weld mark, silver wire, spray mark, scorch, warp, crack/break, etc.).

In some embodiments, the label may be obtained by manually labeling the historical injection-molded product based on the detection result after the quality detection of the historical injection-molded product by the quality detection unit, for example, the detection result obtained by scanning the detection device, and the like.

In some embodiments, the first image recognition model may be trained by various methods to update model parameters based on the above samples. For example, the training may be based on a gradient descent method.

In some embodiments, the training is ended when the first image recognition model under training satisfies a preset condition. The preset condition may be that the loss function result converges or is smaller than a preset threshold, etc.

According to the above description, the first image recognition model trained based on a large number of training samples can quickly acquire the quality detection result of the injection molding product and the specific quality problem type through processing the image of the injection molding product. The efficiency of obtaining the injection molding that the quality had the problem can be improved and the accuracy that the problem detected is promoted.

In some embodiments, the controller may recognize a color of the injection molded article, and control a lighting color when the camera photographs based on the recognition result of the color.

The identification result may be the actual color exhibited by the injection molded article itself, e.g., red, blue, gray, etc.

The lighting color can be the color of an auxiliary lamp used by the camera when shooting the image of the injection molding product. For example, the illumination color may be made red, yellow, white, etc. based on the auxiliary light.

In some embodiments, the illumination color may be controlled based on the controller, for example, if the required illumination color is yellow, the controller may send a control signal to a corresponding light source, such as a yellow light bead, to control the light source to turn on.

In some embodiments, the controller may determine the illumination color based on a preset relationship between the injection molded article color and the illumination color, for example, when the injection molded article color is red, the illumination color may be determined to be yellow based on the preset relationship. It will be appreciated that by using a predetermined relationship in which the illumination color is illuminated on the injection molded article of the corresponding color, a more clear image can be displayed.

The preset relationship can be a preset matching relationship between the color of the injection molding product and the illumination color. If the color of the injection-molded article is red, the color of light is yellow. In some embodiments, the preset relationship may be preset manually based on color matching relationships or historical experience.

It can be understood that based on the color of the injection molding product, the camera can acquire a clearer image of the injection molding product by adjusting the illumination color. For example, when injection moulding is red, the illumination colour when the controller can control the camera and shoot is yellow for the image of the injection moulding that obtains is more clear, and then can promote the degree of accuracy based on the quality testing result that image recognition obtained.

In some embodiments, as shown in FIG. 3, the controller may identify the color of the injection molded article through the second image recognition model. Specifically, an image of the injection-molded article is input into the second image recognition model, and the color of the injection-molded article output from the second image recognition model is acquired.

In some embodiments, the second image recognition model may be a neural network.

In some embodiments, the input to the second image recognition model may be images of one or more angles of the injection molded article (e.g., front view, back view, left view, right view, etc.). The output may be the color of the injection molded article.

In some embodiments, the second image recognition model may be obtained based on multiple sets of labeled training samples training. The training sample of the second image recognition model may include an image of a historical injection molded article. For example, a set of training samples are a plurality of images of historical injection molded articles. In some embodiments, the training sample may be obtained from images of the injection molded article taken historically by the camera. In some embodiments, the training samples may also be obtained from stored images of historical injection molded articles.

In some embodiments, the sample label may be a manually labeled color of the historical injection molded article.

In some embodiments, the second image recognition model training method is similar to the first image recognition model, and in particular, with respect to the training method of the second image recognition model, reference is made to the description related to the training method of the first image recognition model.

And identifying the color of the injection molding product through a second image identification model in the controller, and controlling the illumination color when the camera shoots based on the identification result of the color. The color of the injection molding product can be quickly and accurately acquired, and the illumination color of the camera matched with the injection molding product during shooting can be further accurately determined, so that a clearer image of the injection molding product can be acquired.

Fig. 4 is a diagram illustrating an example process for determining whether an injection molded article has been introduced into a quality parameter detection device in accordance with some embodiments of the present description.

In some embodiments, the controller can determine whether the injection molded article is input to the quality parameter detection device based on a first indicator of an image quality result of the injection molded article.

In some embodiments, the first indicator may be a confidence level of a quality inspection result obtained by quality inspecting the injection molded article based on the image of the injection molded article. It can be understood that the possibility that the quality of the quality inspection result of the current injection molding product is problematic can be determined through the first index, namely, the higher the reliability is, the higher the possibility that the quality problem exists is; the lower the reliability, the less likely the quality problem will exist.

In some embodiments, the range of values for confidence may be [0,1 ]. For example, when the reliability of the injection molded article having the problem of shrinkage cavity is 0.1, it means that the possibility of the injection molded article having the problem of appearance quality of shrinkage cavity is 10%. For another example, when the reliability of the injection molded article having the burr problem is 0.8, it means that the possibility of the injection molded article having the burr appearance quality problem is 80%.

In some embodiments, a first threshold may be set, and a comparison between the first index and the first threshold is used to determine whether to input the injection molded product into the corresponding quality parameter detection device for further quality detection, for example, when the first index is greater than the first threshold, the injection molded product corresponding to the index may be input into the quality parameter detection device for detecting the corresponding quality problem.

In some embodiments, the first threshold may be set based on actual conditions, for example, if the detection accuracy needs to be improved, the first threshold may be set to be smaller. For example only, the first threshold may be set to 0.5, and if the first index corresponding to the weld mark problem of the injection-molded article a is 0.6, which is greater than the first threshold, it is determined that the injection-molded article a needs to be input into the scanning device for further scanning detection.

In some embodiments, the first indicator of the quality result of the injection molding product may be determined in various ways, for example, a worker may perform the quality detection on the injection molding product based on historical experience and perform the reliability setting on the detection result, so as to obtain the first indicator corresponding to the corresponding detection result. For another example, if the quality of the injection molded product is detected based on the machine learning model, the confidence corresponding to the detection result may be output while the detection result is output, and the confidence may be used as the first indicator corresponding to the corresponding detection result.

In some embodiments, the reliability of the quality detection performed by the image of the quality of the injection-molded product is judged by the reliability of the image, and whether the quality detection needs to be performed again by inputting the quality parameter detection equipment is further judged. Therefore, the current quality problem of the injection molding product can be more accurately acquired, and the omission of the injection molding product with the quality problem is avoided.

In some embodiments, a first indicator of an image quality result of an injection molded article may be determined based on a third indicator of an image and a second indicator based on an acquisition of an image recognition model.

In some embodiments, the third indicator may be a quality score of an image of the injection molded article. The image quality score can be used to indicate image quality parameters such as the sharpness and resolution of the image of the injection molded article. In some embodiments, the quality score of an image may be represented based on a specific score, for example, 10 total scores of the quality scores of the images, and scoring the images of the injection molded article based on the image quality (e.g., 7 scores) may serve as a third indicator of the images of the injection molded article.

In some embodiments, the third indicator may be obtained by calculating the sharpness and resolution of the image of the injection molded article. Wherein the sharpness may be a parameter reflecting the sharpness of the image of the injection molded article.

In some embodiments, the image sharpness may be obtained in a number of ways, such as the image sharpness may be found using a Laplacian transform. Specifically, the laplacian operator with the same size as the image may be used to perform laplacian transformation on the image, and then the laplacian transformation may be used to obtain the sharpness value of the current frame image of the image.

In some embodiments, the third indicator may be calculated by the following formula:

S＝kD+mF+p

wherein S is mass fraction, D is definition, F is resolution, and k, m, and p are constants.

In some embodiments, the obtaining manner of the third index may also be obtained by a scoring model. Wherein, the scoring model may be a neural network.

In some embodiments, the input to the scoring model may be an image of the injection molded article. For example, front, rear, left, right, etc. images of the injection molded article. The output of the scoring model may be a corresponding mass score of the image of the injection molded article. For example, 8 points (full 10 points).

In some embodiments, the scoring model may be trained once or more times based on the labeled training samples, and the parameters of the scoring model are updated during the training until the loss function result converges or is smaller than a preset threshold, and finally the training is finished.

The training sample may include an image of a historical injection-molded article, for example, in the production of the historical injection-molded article, images of a plurality of angles acquired by a camera are used as the training sample. In some embodiments, the training samples may be obtained from stored historical data. The label may be a scoring of historical quality scores, for example, based on manually scoring a quality score corresponding to a captured image of an injection molded article.

The second index may be a confidence corresponding to a quality recognition result output after processing the image of the injection molded article based on the recognition model, for example, the second index may be a confidence corresponding to a recognition result output by the first image recognition model.

In some embodiments, the first image recognition model may be a multi-classification model. Based on the first image recognition model, confidence degrees corresponding to a plurality of classification results can be obtained. For example, the first image recognition model may perform recognition processing on an input image and obtain a confidence that the image has a quality problem corresponding to each classification result.

For example only, the labels corresponding to the classification results of the multi-classification model may be 1, 2, and 3, where label 1 corresponds to a quality problem of burrs, label 2 corresponds to a quality problem of shrinkage cavities, label 3 corresponds to a quality problem of silver wires, and the output of the first image recognition model may be a vector including three vector elements, each vector element corresponds to a confidence level of a quality problem corresponding to the presence of labels 1, 2, and 3, respectively, and if the result output by the first image recognition model based on the processing of the input image of the injection molded article a is (0.1, 0.15, and 0.85), the confidence level indicating that the injection molded article a has burrs is 0.1, the confidence level of a quality problem with shrinkage cavities is 0.15, and the confidence level of a quality problem with silver wires is 0.85; finally, the quality problem corresponding to the maximum confidence coefficient can be used as a final classification result, and the maximum confidence coefficient is a second index finally used for calculating the first index, as in the previous example, it can be considered that the injection molding product a has the quality problem of the silver wire, and 0.85 is used as the second index for calculating the first index.

In some embodiments, the first indicator corresponding to the image quality result of the injection molded article may be determined based on the third indicator of the image and the second indicator of the image recognition model, and may be obtained, for example, by the following formula:

T＝aC+bM+d

wherein, T is a first index which represents the credibility of a quality inspection result obtained by quality inspection of the injection molding product based on the image of the injection molding product, C is a second index which represents the confidence corresponding to a quality identification result output after the image of the injection molding product is processed based on the identification model, M is a quality score which represents the score scored based on the image quality, and a, b and d are constants.

The first index is determined through the confidence coefficient of the first image recognition model and the quality fraction of the injection molding product image, so that the confidence coefficient of a quality inspection result obtained by quality inspection of the injection molding product based on the image of the injection molding product can be more accurately determined, and the problems of erroneous judgment and the like are avoided.

In some embodiments, the controller may be configured to count the number of products of the injection molding product corresponding to each type of quality detection result, and send the warning information to the injection molding machine or the manufacturer terminal based on the count result of the number of products.

In some embodiments, the statistical result may be a proportion of good or bad articles to the total number of articles. Wherein a good article can be an article that does not have any of a variety of quality issues (e.g., appearance quality issues, hardness quality issues, density quality issues, etc.).

In some embodiments, the number of articles of injection molded articles for each type of quality test result can be the number of articles of injection molded articles for which one or more types of quality problems exist. The quality problems may include, for example, appearance quality problems, hardness quality problems, density quality problems, etc., wherein the number of articles of the injection molded article having appearance quality problems may include the total number of injection molded articles having appearance problems such as weld marks, cracks, breaks, etc. The number of articles of injection molded articles that have density quality problems can include the total number of injection molded articles that have density problems that do not meet industry standards (e.g., density less than a predetermined value or greater than a predetermined value, etc.). The number of articles of injection molded articles having hardness quality problems may include the total number of injection molded articles having hardness problems that do not meet industry standards, etc. (e.g., hardness is less than a predetermined value or greater than a predetermined value, etc.).

In some embodiments, the warning information may be information having a warning effect. Such as early warning text, early warning voice, early warning pictures, etc.

In some embodiments, the warning information can be transmitted to the injection molding machine or a terminal of a producer to prompt the producer of the quality problem of the current injection molded product. For a detailed description of the injection molding machine and the manufacturer terminal, refer to the relevant contents in fig. 1.

In some embodiments, the controller counts the number of products of the injection molding product corresponding to each type of quality detection result, and sends the warning information to the injection molding machine or the manufacturer terminal based on the counting result of the number of products. The statistical method can comprise the following steps: when multiple types of quality problems exist in each product, the count corresponding to each type of quality problem is increased. For example, the count is directly incremented by 1.

In some embodiments, the count increase value is associated with a first indicator of a quality outcome. For example, an index threshold is set, if a first index of a certain type of quality problem is greater than the index threshold, the count of the corresponding statistics having the type of quality problem is increased by 1, otherwise, the count is not increased. It is understood that when the first index of a certain type of quality problem is greater than the threshold index, the injection-molded article has a high reliability of the quality problem, that is, the quality problem is highly likely to exist, and therefore the number of the quality problems needs to be increased.

In some embodiments, the count increase value may be determined according to the following formula:

m＝hT+j

wherein m is a count increment value, T is a first index, and h and j are constants. It is understood that m in this method may not be an integer.

The quantity of the injection molding products corresponding to various quality detection results is counted through the controller, early warning information is sent to the injection molding machine or a producer terminal based on the counting result of the quantity of the injection molding products, the quantity of the injection molding products with each quality problem can be accurately determined, the quality problems of the current injection molding products of the producers can be timely prompted to perform abnormity early warning, meanwhile, the abnormal direction can be guided based on the statistical data of various quality problems, and accordingly follow-up processing can be effectively performed on abnormity.

In some embodiments, the controller can be configured to adjust the frequency of delivery of the injection molded articles to the quality parameter sensing device based on the fraction of injection molded articles that have quality problems.

In some embodiments, the proportion of injection molded articles having quality problems may be the proportion of injection molded articles having quality problems to the total number of articles. It is understood that the quality problem may be any one of the quality problems described above, and the quality of the whole injection-molded product can be judged according to the quality problem.

In some embodiments, the transmission frequency may be the number of injection molded articles transmitted to the quality parameter detecting device per unit time. For example, 1 injection molded article is transferred for 1 minute with a frequency of 1/minute, and for example, 2/minute every 30 seconds.

In some embodiments, the transmission frequency may be adjusted according to the proportion of the injection molded article that has quality problems, e.g., the higher the proportion, the lower the transmission frequency. In order to reserve sufficient detection time for the quality parameter detection device.

In some embodiments, the method for adjusting the transmission frequency may be adjusted by setting a first ratio threshold and a second ratio threshold (where the first ratio threshold is smaller than the second ratio threshold). Further, if the counted proportion of the total number of the injection molding products with quality problems is smaller than a first proportion threshold value, the maximum transmission frequency is kept from being adjusted; if the ratio is smaller than the second ratio threshold and larger than the first ratio threshold, the transmission frequency may be decreased, for example, the transmission frequency is adjusted to 50% of the maximum transmission frequency.

In some embodiments, the method of adjusting the transmission frequency may be adjusted according to a formula, such as the value of the adjusted transmission frequency may be determined by the following formula:

wherein f is the transmission frequency, p is the proportion of the injection molding product with quality problem to the total number of the product, e and g are constants,

is rounding up the symbol.

In some embodiments, the controller can adjust the frequency of delivery of the injection molded articles to the quality parameter detection device based on the fraction of injection molded articles that have quality problems. Adjusting the transmission frequency can be based on, for example, adjusting the sample detection channel (e.g., conveyor belt) in fig. 2 such that the conveyor belt transmits the injection molded article to the quality parameter detection device at the specified transmission frequency.

Based on the proportion that injection moulding's that has the quality problem shared, the adjustment is transmitted to the transmission frequency of injection moulding of quality parameter check out test set, can realize that injection moulding that the quality problem appears is less, when production quality is comparatively steady, corresponding transmission frequency based on promoting the injection moulding that transmits to the quality parameter check out test set, with promote injection moulding's whole production, detection efficiency, it is corresponding, when the product quantity that has the quality problem is more, then corresponding reduction transmission frequency, carry out abundant quality detection to goods in order to reserve sufficient check-out time for quality parameter check out test set.

In some embodiments, the controller may be configured to adjust the number of shooting angles of the camera based on a fraction of injection molded articles that have appearance quality problems.

In some embodiments, the injection molded article having appearance quality problems may be an injection molded article having defects in appearance, for example, weld marks, cracks, fractures, and the like in appearance of the injection molded article. In some embodiments, the injection molded article with appearance quality problem can be obtained from the recognition result output by the first image recognition model, or obtained by a quality parameter detection device (such as a scanning device).

In some embodiments, the ratio of the number of injection-molded articles having appearance-type problems to the total number of all injection-molded articles can be used as the ratio of the injection-molded articles having appearance-quality problems.

The number of shooting angles can be the number of different angles at which the camera shoots images of the injection molded article. For example, front, rear, left, right, top, bottom, etc. It will be appreciated that as the number of injection molded articles for which the appearance quality is a problem increases, more different angle injection molded article images are required.

In some embodiments, the number of angles may be adjusted by setting a third proportional threshold and a fourth proportional threshold (where the third proportional threshold is less than the fourth proportional threshold).

Further, if the proportion of the injection molding products with appearance quality problems is smaller than a third proportion threshold value, setting the number of the shooting angles as a first preset value (such as 3); if the ratio is smaller than the fourth ratio threshold and larger than the third ratio threshold, setting the number of the shooting angles as a second preset value (such as 4); and if the proportion is larger than a fourth proportion threshold value, setting the number of the shooting angles as a third preset value (for example, 5), wherein the first preset value is smaller than the second preset value and smaller than the third preset value.

In some embodiments, the controller may adjust the number of shooting angles of the image capturing device according to any method for adjusting the number of shooting angles (e.g., the threshold setting method described above) based on the ratio of the injection molded product with the appearance problem, and the adjustment of the shooting angles may be performed based on rotating the rotating table, so that different surfaces of the injection molded product are aligned with the image capturing device.

In some embodiments, the number of shooting angles is adjusted in real time according to the proportion of injection-molded products based on appearance problems, so that when the quality problem is high, the number of shooting angles is increased; when the quality problem is small, the number of shooting angles is reduced. Thus, the image resources are effectively utilized while ensuring the quality of the injection molded product with appearance problems.

Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be regarded as illustrative only and not as limiting the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, although not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.

Also, the description uses specific words to describe embodiments of the description. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification is included. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.

Additionally, the order in which the elements and sequences of the process are recited in the specification, the use of alphanumeric characters, or other designations, is not intended to limit the order in which the processes and methods of the specification occur, unless otherwise specified in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose 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 that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.

Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.

Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.

For each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, etc., cited in this specification, the entire contents of each are hereby incorporated by reference into this specification. Except where the application history document does not conform to or conflict with the contents of the present specification, it is to be understood that the application history document, as used herein in the present specification or appended claims, is intended to define the broadest scope of the present specification (whether presently or later in the specification) rather than the broadest scope of the present specification. It is to be understood that the descriptions, definitions and/or uses of terms in the accompanying materials of this specification shall control if they are inconsistent or contrary to the descriptions and/or uses of terms in this specification.

Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of the embodiments of the present disclosure. Other variations are also possible within the scope of the present description. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the specification can be considered consistent with the teachings of the specification. Accordingly, the embodiments of the present description are not limited to only those embodiments explicitly described and depicted herein.

Claims

1. An injection molding sorting device, comprising: a controller, a sample conveying channel, a quality detection unit, a plurality of sample sorting channels,

the sample conveying channel is used for conveying injection molding products produced by the injection molding machine to the quality detection unit, the quality detection unit is used for detecting the quality of the injection molding products, and each sample sorting channel in the plurality of sample sorting channels is used for conveying the injection molding products with the quality detected to the corresponding storage space.

2. The injection molding sorting apparatus according to claim 1, wherein the quality detecting unit is provided with a camera device and a rotary table for placing the injection molding, the camera device being configured to take product images of the injection molding at a plurality of angles for quality detection based on rotation of the rotary table.

3. An injection molding sorting apparatus according to claim 1, wherein the quality detecting unit is provided with a quality parameter detecting device for detecting a quality of the injection molding.

4. The injection molding sorting device according to claim 1 or 3, wherein the controller is configured to count the number of the injection molding products corresponding to each type of quality detection result, and send warning information to the injection molding machine or the manufacturer terminal based on the count result of the number of the products.