CN115230034B - Method and system for hierarchical detection of injection molding products - Google Patents
Method and system for hierarchical detection of injection molding products Download PDFInfo
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- CN115230034B CN115230034B CN202210923590.XA CN202210923590A CN115230034B CN 115230034 B CN115230034 B CN 115230034B CN 202210923590 A CN202210923590 A CN 202210923590A CN 115230034 B CN115230034 B CN 115230034B
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- 238000001746 injection moulding Methods 0.000 title claims abstract description 58
- 238000001514 detection method Methods 0.000 title claims abstract description 28
- 238000000034 method Methods 0.000 title claims description 32
- 238000002347 injection Methods 0.000 claims abstract description 162
- 239000007924 injection Substances 0.000 claims abstract description 162
- 238000009826 distribution Methods 0.000 claims abstract description 114
- 230000006698 induction Effects 0.000 claims abstract description 10
- 230000004044 response Effects 0.000 claims abstract description 3
- 238000007689 inspection Methods 0.000 claims description 14
- 230000015654 memory Effects 0.000 claims description 12
- 238000012545 processing Methods 0.000 claims description 4
- 238000003860 storage Methods 0.000 claims description 2
- 238000012549 training Methods 0.000 description 40
- 239000000047 product Substances 0.000 description 38
- 238000000605 extraction Methods 0.000 description 24
- 230000008569 process Effects 0.000 description 13
- 238000012986 modification Methods 0.000 description 8
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- 238000012360 testing method Methods 0.000 description 7
- 238000013527 convolutional neural network Methods 0.000 description 5
- 230000006872 improvement Effects 0.000 description 3
- 238000010801 machine learning Methods 0.000 description 3
- 238000004519 manufacturing process Methods 0.000 description 3
- 238000012546 transfer Methods 0.000 description 3
- 239000000853 adhesive Substances 0.000 description 2
- 230000001070 adhesive effect Effects 0.000 description 2
- 239000003086 colorant Substances 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 230000006978 adaptation Effects 0.000 description 1
- 230000000712 assembly Effects 0.000 description 1
- 238000000429 assembly Methods 0.000 description 1
- 210000000078 claw Anatomy 0.000 description 1
- 230000003749 cleanliness Effects 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000006870 function Effects 0.000 description 1
- 230000014509 gene expression Effects 0.000 description 1
- 238000013508 migration Methods 0.000 description 1
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- 239000002994 raw material Substances 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000011265 semifinished product Substances 0.000 description 1
- 239000000243 solution Substances 0.000 description 1
Classifications
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C37/00—Component parts, details, accessories or auxiliary operations, not covered by group B29C33/00 or B29C35/00
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C31/00—Handling, e.g. feeding of the material to be shaped, storage of plastics material before moulding; Automation, i.e. automated handling lines in plastics processing plants, e.g. using manipulators or robots
- B29C31/04—Feeding of the material to be moulded, e.g. into a mould cavity
- B29C31/08—Feeding of the material to be moulded, e.g. into a mould cavity of preforms to be moulded, e.g. tablets, fibre reinforced preforms, extruded ribbons, tubes or profiles; Manipulating means specially adapted for feeding preforms, e.g. supports conveyors
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- B—PERFORMING OPERATIONS; TRANSPORTING
- B65—CONVEYING; PACKING; STORING; HANDLING THIN OR FILAMENTARY MATERIAL
- B65G—TRANSPORT OR STORAGE DEVICES, e.g. CONVEYORS FOR LOADING OR TIPPING, SHOP CONVEYOR SYSTEMS OR PNEUMATIC TUBE CONVEYORS
- B65G47/00—Article or material-handling devices associated with conveyors; Methods employing such devices
- B65G47/74—Feeding, transfer, or discharging devices of particular kinds or types
- B65G47/90—Devices for picking-up and depositing articles or materials
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B29—WORKING OF PLASTICS; WORKING OF SUBSTANCES IN A PLASTIC STATE IN GENERAL
- B29C—SHAPING OR JOINING OF PLASTICS; SHAPING OF MATERIAL IN A PLASTIC STATE, NOT OTHERWISE PROVIDED FOR; AFTER-TREATMENT OF THE SHAPED PRODUCTS, e.g. REPAIRING
- B29C37/00—Component parts, details, accessories or auxiliary operations, not covered by group B29C33/00 or B29C35/00
- B29C2037/90—Measuring, controlling or regulating
- B29C2037/903—Measuring, controlling or regulating by means of a computer
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- Engineering & Computer Science (AREA)
- Mechanical Engineering (AREA)
- Robotics (AREA)
- Injection Moulding Of Plastics Or The Like (AREA)
Abstract
The embodiment of the specification provides a grading detection method for injection molding products, which comprises the steps of grabbing the injection molding products by using a mechanical arm and placing the injection molding products in an infrared induction area, wherein an infrared acquisition device is arranged in the infrared induction area. The infrared acquisition device is used for acquiring an infrared temperature distribution image of the injection molding product, and the distribution information and the mass fraction of internal bubbles of the injection molding product are determined based on the infrared temperature distribution image; acquiring an X-ray detection image of the injection molded article in response to the mass fraction being less than a threshold; and updating the distribution information of the internal bubbles based on the X-ray detection image.
Description
Description of the division
The application provides a divisional application aiming at China application with the application date of 2021, 09 and 07 and the application number of 202111042966.8, namely a method and a system for detecting the quality of injection molding products.
Technical Field
The specification relates to the field of quality detection of injection molding products, and in particular relates to a method and a system for detecting classification of injection molding products by using infrared detection.
Background
In actual production, bubbles with different sizes and different positions may exist in the injection molding product, and the quality and the usability of the injection molding product are directly affected. The quality detection of the injection molding products at present mainly comprises sampling slice detection and X-ray detection. The former can destroy the sample and only sample detection is possible, while the latter has a relatively slow speed and high cost.
In order to solve the problems, the scheme provides a rapid and low-cost nondestructive grading detection method for injection molding products.
Disclosure of Invention
One embodiment of the specification provides a method for detecting classification of injection molding products. The injection molding product quality detection method comprises the following steps: grabbing an injection molding product through a mechanical arm, and placing the injection molding product in an infrared induction area, wherein an infrared acquisition device is arranged in the infrared induction area; acquiring an infrared temperature distribution image of the injection molding product through the infrared acquisition device; determining distribution information and mass fraction of internal air bubbles of the injection molded article based on the infrared temperature distribution image; acquiring an X-ray detection image of the injection molded article in response to the mass fraction being less than a threshold; and updating the distribution information of the internal bubbles based on the X-ray detection image.
One of the embodiments of the present disclosure provides an injection molded article quality inspection system. The system comprises: the mechanical arm is used for grabbing injection molding products and is placed in the infrared induction area; the infrared acquisition device is positioned in the infrared induction area and is used for acquiring infrared temperature distribution images of the injection molding product; a processor for determining distribution information and mass fraction of internal air bubbles of the injection molded article based on the infrared temperature distribution image; the processor is used for responding to the mass fraction being smaller than a threshold value to acquire an X-ray detection image of the injection molding product; the processor is used for updating the distribution information of the internal bubbles based on the X-ray detection image.
One of the embodiments of the present disclosure provides an injection molded article classification testing apparatus, including a processor for performing a method for performing classification testing of injection molded articles.
One of the embodiments of the present disclosure provides a computer-readable storage medium storing computer instructions that, when read by a computer, perform a method of performing hierarchical inspection of injection molded articles.
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 a schematic illustration of an application scenario of an injection molded article quality inspection system according to some embodiments of the present disclosure;
FIG. 2 is an exemplary flow chart of an injection molded article quality inspection process shown in accordance with some embodiments of the present disclosure;
FIG. 3 is an exemplary flow chart of a process for determining distribution information of internal air bubbles of an injection molded article according to some embodiments of the present disclosure;
FIG. 4 is a schematic illustration of a first predictive model shown in accordance with some embodiments of the present disclosure;
FIG. 5 is an exemplary flow chart of a process for determining distribution information of internal air bubbles of an injection molded article according to some embodiments of the present disclosure.
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 a schematic diagram of an injection molded article quality inspection system 100 according to some embodiments of the present disclosure.
In some embodiments, the injection molded article quality inspection system 100 may include an infrared acquisition device 110, a robotic arm 130, a processor 140, a network 150, a memory 160, and a display device 170.
The infrared acquisition device 110 may be a device for acquiring infrared energy information emitted from an object, and the infrared energy information may reflect temperature information of various parts of the object. The infrared acquisition device 110 may be a thermal infrared imager, an infrared sensor, or the like, which can acquire infrared energy.
In some embodiments, the infrared acquisition device 110 may acquire invisible infrared energy information emitted by the measured object 180, and convert the infrared energy information into a visible infrared temperature distribution image, and then transmit the infrared temperature distribution image to the processor 140 for further processing through the network 150. In some embodiments, different colors on the infrared temperature distribution image represent different temperatures of the object under test. In some embodiments, the infrared acquisition device 110 may acquire infrared energy information emitted by the measured object 180, and directly transmit the acquired infrared energy information to the processor 140 through the network 150 for further processing.
The robotic arm 130 may be a device that holds an object and moves and/or rotates the object to different positions and orientations. In some embodiments, the robotic arm 130 may include a gripper. The mechanical arm 130 can clamp and fix the measured object 180 by using a mechanical claw. In some embodiments, the mechanical arm 130 may move the object 180 to be measured, which is held and fixed, to the infrared sensing area 120, so that the infrared acquisition device 110 disposed in the infrared sensing area 120 acquires infrared energy information of the object 180 to be measured.
In some embodiments, the gripping parameters, movement, and/or rotation parameters of the robotic arm 130 may be set by a worker. In some embodiments, the gripping parameters, movement and/or rotation parameters of the robotic arm 130 may be automatically set according to the initial position and posture, target position and posture of the measured object 180.
Processor 140 may process the infrared energy information collected by infrared collection device 110 and/or the generated infrared temperature distribution image. In some embodiments, the processor 140 may determine distribution information of the internal bubbles of the object under test 180 based on the infrared energy information and/or the infrared temperature distribution image.
Network 150 may include wired networks and wireless networks. In some embodiments, the network 150 may connect the infrared acquisition device 110, the robotic arm 130, the processor 140, the memory 160, and the display device 170 and transfer information between the infrared acquisition device 110, the robotic arm 130, the processor 140, the memory 160, and the display device 170.
Memory 160 may be used to store information and/or instructions. In some embodiments, the memory 160 may be coupled to the infrared acquisition device 110, the robotic arm 130, the processor 140, and the display device 170 via the network 150 for storing information and data that the infrared acquisition device 110, the robotic arm 130, and/or the processor 140 need to use and/or generate. In some embodiments, the infrared acquisition device 110, the robotic arm 130, the processor 140, and the display device 170 may include respective memories for storing respective information and/or instructions.
The display device 170 may be used to display information. In some embodiments, the display device 170 may display the infrared energy information and/or the infrared temperature distribution image acquired by the infrared acquisition device 110. In some embodiments, the display device 170 may display the distribution information of the internal bubbles of the object under test 180 generated by the processor 140. In some embodiments, the display device 170 may display gripping parameters, movement, and/or rotation parameters of the robotic arm 130.
In some embodiments, processor 140 and memory 160 may be integrated in infrared acquisition device 110. In some embodiments, the processor 140 and the memory 160 may be integrated in the display device 170. In some embodiments, processor 140 and memory 160 may be independent of infrared acquisition device 110 and display device 170. In some embodiments, memory 160 may be integrated with processor 140.
It should be noted that the above description of the injection molded article quality inspection system 100 is for convenience of description only and is not intended to limit the present disclosure to the scope of the illustrated embodiments. It will be appreciated by those skilled in the art that, given the principles of the system, it is possible to combine the various devices arbitrarily or to construct a subsystem in connection with other modules without departing from such principles.
Fig. 2 is an exemplary flow chart of an injection molded article quality inspection process 200 according to some embodiments of the present description. As shown in fig. 2, the process 200 includes the following steps. In some embodiments, process 200 may be performed by injection molded article quality inspection system 100.
In step 202, the manipulator 130 may grasp the injection molded article and place the injection molded article in the infrared sensing area 120, where the infrared sensing area 120 is provided with the infrared acquisition device 110.
The injection molding product is a finished product or a semi-finished product prepared by adopting an injection molding process. Injection molded articles can have a variety of shapes and configurations.
In some embodiments, the injection molded article may originate from a production line. In some embodiments, the injection molded article production and transfer system may place the produced injection molded article on a stage, and control a conveyor coupled to the stage to transfer the stage to a predetermined position, such that the robotic arm 130 grips the injection molded article from the predetermined position by a gripper.
In some embodiments, the injection molded article may originate from a worker. In some embodiments, a worker may place the injection molded article to be inspected in the holding area, and the robot arm 130 may grasp the injection molded article from the holding area via a gripper. In some embodiments, a worker may place the injection molded article to be inspected on a support frame that may hold and move and/or rotate the injection molded article.
The infrared sensing region 120 may be a region for collecting infrared energy information of the injection molded article, and the infrared collection device 110 is disposed within or near the infrared sensing region 120.
In some embodiments, the injection molded article may be held stationary by a gripper on the robotic arm 130 from a stationary or moving position, and then moved by the robotic arm 130 to the infrared sensing region 120.
In step 204, the infrared acquisition device 110 may acquire an infrared temperature distribution image of the injection molded article.
The infrared temperature distribution image is an image which is generated based on the acquired infrared energy information of the injection molding product and reflects the temperature of each part of the injection molding product. The infrared temperature distribution image may include coordinate information and temperature information for various portions of the injection molded article. Different colors on the infrared temperature distribution image may represent different temperatures of the injection molded article. For example, the temperature indicated by red is higher than the temperature indicated by blue.
In some embodiments, the infrared temperature distribution image may comprise an infrared temperature distribution image of one orientation of the injection molded article.
In some embodiments, the infrared temperature distribution image may include an infrared temperature distribution image of the injection molded article in at least two orientations. In some embodiments, the orientation of the injection molded article that it needs to acquire may be determined based on its shape. For example, when the injection molded article is disk-shaped, an infrared temperature distribution image of a first circular orientation and a second circular orientation of the injection molded article may be acquired. When the injection molding product is in a cube shape, infrared temperature distribution images of the directions of six faces of the injection molding product can be acquired. In some embodiments, the orientation that it is desired to collect may be determined based on the function of the different portions of the injection molded article. For example, an infrared temperature profile image of a first face of the load bearing face and a second face opposite the first face may be acquired.
In some embodiments, the robotic arm 130 may rotate the injection molded article such that different orientations of the injection molded article face the infrared acquisition device 110, and then acquire infrared temperature distribution images of the different orientations of the injection molded article via the infrared acquisition device 110. In some embodiments, a support for placing the injection molded article may rotate the injection molded article such that different orientations of the injection molded article face the infrared acquisition device 110, and then infrared temperature distribution images of the different orientations of the injection molded article are acquired by the infrared acquisition device 110.
In some embodiments, infrared acquisition device 110 may include at least one infrared sensor that may receive and detect infrared light in multiple bands emitted by different portions of the injection molded article and convert the infrared signals into electrical signals, based on which an infrared temperature profile image may be generated.
The mechanical arm rotates the injection molding product, so that infrared energy information and/or infrared temperature distribution images of different directions of the injection molding product can be automatically and more comprehensively acquired, and the distribution condition of internal bubbles of the injection molding product can be predicted more accurately.
At step 206, the processor 140 may determine distribution information of internal air bubbles of the injection molded article based on the infrared temperature distribution image.
The internal bubbles of the injection molded product may be generated due to insufficient injection pressure and dwell time in the injection stage, or may be caused by inappropriate parameters such as mold closing time, mold opening time, injection time, melt adhesive time, sol temperature, mold temperature, etc.
The distribution information of the internal air bubbles may include size and position information of the internal air bubbles. The size of the internal bubbles may be classified into oversized bubbles, large bubbles, medium bubbles, small bubbles, micro bubbles, and the like. The classification of the size of the internal air bubbles is related to the size, thickness of the injection molded article and the size of the internal air bubbles themselves. For example, internal air bubbles of the same size may be classified as different size types in injection molded articles of different thickness. The location of the internal bubbles can be divided into near the center and near the surface. The positional classification of the internal air bubbles is related to the thickness of the injection molded article and the position of the internal air bubbles themselves. For example, internal air bubbles at the same distance from the surface of the injection molded article, are classified as different types of locations in injection molded articles of different thicknesses.
In some embodiments, the processor 140 may determine distribution information of internal air bubbles of the injection molded article using the first predictive model based on the infrared temperature distribution image. In some embodiments, the processor 140 may determine the distribution information of the internal air bubbles of the injection molded article using the first predictive model or the second predictive model based on the infrared temperature distribution image. For a description of the determination of the distribution information of the internal air bubbles of the injection molded article, reference is made to fig. 3 and 5.
Determining the distribution information of the internal air bubbles of the injection molded article using the infrared temperature distribution image can quickly determine the distribution information of the internal air bubbles of the injection molded article without damaging the injection molded article. And because the detection speed is high and the injection molding products are not damaged, the produced injection molding products can be sequentially inspected or sampled and inspected at a high proportion (such as 10% -50%), so that the injection molding products with the quality not reaching the standard can be found as far as possible, and the delivery qualification rate of the injection molding products is improved.
FIG. 3 is an exemplary flow chart of a process 300 for determining distribution information of internal air bubbles of an injection molded article, according to some embodiments of the present disclosure.
In step 310, the processor 140 may input an infrared temperature distribution image of the injection molded article to be tested into the first predictive model.
In some embodiments, the input of the first predictive model may include an infrared temperature distribution image of the injection molded article. In some embodiments, the input of the first predictive model may also include at least one piece of information related to the injection molded article, such as at least one of a composition of the injection molded article, a shape and size of the injection molded article, information of raw materials of the injection molded article (e.g., dryness, cleanliness), and the like. In some embodiments, the input of the first predictive model may also include at least one injection molding process parameter, such as injection pressure, dwell time, mold close time, mold open time, injection time, melt adhesive time, sol temperature, and mold temperature, when preparing the injection molded article.
By inputting relevant information of the injection molded product and/or injection molding process parameters when the injection molded product is prepared, the prediction of the first prediction model can be more accurate. In addition, by inputting relevant information of the injection molding product, the number of parameters required to be optimized through training in the process of training a model can be reduced, and the load of a server is reduced.
In some embodiments, the first predictive model may include a machine learning model. In some embodiments, the first predictive model may determine distribution information of internal air bubbles of the injection molded article based on an infrared temperature distribution image of the injection molded article. In some embodiments, the first predictive model may determine distribution information of internal air bubbles of the injection molded article based on an infrared temperature distribution image of the injection molded article, related information of the injection molded article, and/or injection molding process parameters at the time of preparing the injection molded article.
In some embodiments, the first predictive model may include an infrared feature extraction layer and a decision layer.
In some embodiments, the infrared feature extraction layer may extract an infrared feature vector of the infrared temperature distribution image based on the infrared temperature distribution image of the injection molded article.
In some embodiments, the input of the infrared feature extraction layer may include an infrared temperature distribution image of the injection molded article. In some embodiments, the input of the infrared feature extraction layer may also include information about the injection molded article and/or injection molding process parameters at the time the injection molded article is prepared.
In some embodiments, the output of the infrared feature extraction layer may include an infrared feature vector.
In some embodiments, the infrared feature extraction layer may be a Convolutional Neural Network (CNN).
In some embodiments, the decision layer may include a first decision layer.
In some embodiments, the first determination layer may determine distribution information of internal air bubbles of the injection molded article based on the infrared feature vector.
In some embodiments, the input to the first determination layer may include an infrared feature vector. In some embodiments, the input of the first determination layer may further include information related to the injection molded article and/or injection molding process parameters at the time of preparing the injection molded article.
In some embodiments, the output of the first determination layer may include distribution information of internal air bubbles of the injection molded article.
In some embodiments, the first determination layer may be a fully connected layer.
In step 320, the first predictive model may output information about the distribution of internal air bubbles of the injection molded article. In some embodiments, the distribution information of the internal air bubbles of the injection molded article may include a multi-dimensional vector reflecting the severity of the air bubbles in different portions of the injection molded article. In some implementations, the distribution information of the internal air bubbles of the injection molded article may include position and size information of the internal air bubbles.
The first predictive model may be obtained based on training samples.
The training samples may include a plurality of infrared temperature distribution images and labels of injection molded samples (including quality substandard). In some embodiments, the infrared temperature distribution image of the injection molded sample may include one or more azimuth infrared distribution images.
In some embodiments, the label of the training sample may include information on the distribution of internal bubbles of each of the plurality of injection molded samples. In some embodiments, the labels of the training samples may be determined manually after slicing the injection molded samples. In some embodiments, the injection molded sample may be placed into a cutting tool for slicing, which may be controlled to slice according to a preset rule (e.g., slices at equal distances apart, such as 1mm, 2mm, etc.). In some embodiments, the label may be determined directly from the slice of the injection molded article by one or more persons. In some embodiments, the label may be determined from the photograph by one or more persons after photographing the sliced injection molded sample. In some embodiments, the cut injection molding sample may be photographed, and then the photographed image is processed through a label prediction model to determine a label. In some embodiments, the label prediction model may be a CNN model.
The label is determined based on the slice image of the injection molding sample through the label prediction model, so that the labor cost can be effectively reduced.
In some embodiments, training samples may be input to an initial first predictive model for training.
FIG. 4 is a schematic illustration of a first predictive model, shown in accordance with some embodiments of the present description.
In some embodiments, the first predictive model 400 may include an infrared feature extraction layer and a judgment layer, and the judgment layer may include a first judgment layer and a second judgment layer. A related description of the infrared feature extraction layer and the first determination layer may be seen in fig. 3.
In some embodiments, the second determination layer may determine a mass fraction of the injection molded article based on the infrared feature vector. The mass fraction can be used to determine whether the injection molded article requires further testing, such as X-ray testing. For example, the injection molded article need not be subjected to a next inspection when the mass fraction of the injection molded article is greater than a first threshold value, and the injection molded article need be subjected to a next inspection when the mass fraction of the injection molded article is less than or equal to the first threshold value. The first threshold may be a fraction of 80%, 85%, 90%, 95%, etc.
In some embodiments, the input to the second decision layer may include an infrared feature vector. In some embodiments, the input of the second determination layer may further include information about the injection molded article and/or injection molding process parameters at the time of preparing the injection molded article.
In some embodiments, the output of the second determination layer may include a mass fraction of the injection molded article.
In some embodiments, the second determination layer may be a fully connected layer.
In some embodiments, the infrared feature extraction layer, the first decision layer, and the second decision layer of the first predictive model may be jointly trained using the first training sample.
In some embodiments, the training sample may include an infrared temperature distribution image and a label. A related description of the infrared temperature image in the training sample may be seen in fig. 3.
In some embodiments, the label of the training sample may include mass fraction of each of the plurality of injection molded samples and distribution information of internal bubbles. A related description of determining the distribution information of bubbles in a tag may be seen in fig. 3. In some embodiments, the determination of the mass fraction may be determined manually from the distribution information of the bubbles. In some embodiments, the determination of mass fraction may be determined by detecting the performance intensity of the injection molded sample.
In some implementations, the first training sample may be input to an initial machine learning model for training.
In some embodiments, after the infrared feature extraction layer, the first judgment layer and the second judgment layer are jointly trained based on the first training sample, a first prediction model of the preliminary training can be obtained, and the first judgment layer can be locally trained by using the second training sample based on the first prediction model of the preliminary training.
In some embodiments, the second training sample may include an infrared temperature distribution image and a label. In some embodiments, the label of the second training sample may include information on the distribution of bubbles for each of the plurality of injection molded samples.
In some embodiments, the second training sample may be determined based on a first predictive model of the preliminary training. In some embodiments, when the first predictive model of the initial training determines that the mass fraction of a certain injection molded article is less than the second threshold, the injection molded article may be sliced, and the distribution information of the internal bubbles is determined and used as a label. And then taking the infrared temperature distribution image and the label of the injection molded product as a second training sample to train the primarily trained first prediction model. In some embodiments, the second threshold may be the same as the first threshold.
In some embodiments, the second threshold may be different from the first threshold.
By taking the injection molded product which possibly has quality problems and is determined by the first predictive model of the preliminary training as a second training sample, the second training sample can be updated in real time, and other products with qualified quality are not required to be worn by the updating, and the second training sample is only required to be updated by slicing the injection molded product which possibly has quality problems. In addition, in the actual use process, when the second training sample is updated, the first prediction model (namely the first prediction model used currently) of the preliminary training can be directly used for determining the infrared temperature distribution image used when the distribution information of the internal air bubbles of the injection molding product, so that the actual prediction and the training sample are updated to form a closed loop, the second training sample can be continuously increased, and the accuracy of the distribution information of the predicted air bubbles is improved.
In some embodiments, the second training sample may be input to the first predictive model of the preliminary training for training to update parameters of the infrared feature extraction layer and the first decision layer in the first predictive model of the preliminary training.
FIG. 5 is an exemplary flow chart of a process 500 for determining distribution information of internal air bubbles of an injection molded article according to some embodiments of the present disclosure.
At step 510, the processor 140 may input an infrared temperature distribution image of the injection molded article to be tested into the first predictive model.
In step 520, the first predictive model outputs mass fraction of the injection molded article and distribution information of internal air bubbles.
At step 530, the processor 140 may determine whether the mass fraction of the injection molded article is less than a first threshold.
In step 540, when the processor 140 determines that the mass fraction of the injection molded article is not less than the first threshold, the detection of the injection molded article is ended.
In step 550, when the processor 140 determines that the mass fraction of the injection molded article is less than the first threshold, the injection molded article is subjected to X-ray detection to obtain an X-ray image.
X-ray detection refers to the use of X-rays to scan an injection molded article to be tested, and the obtained image is an X-ray image. The X-ray image includes information on the intensity of X-rays transmitted through the injection molded article and may reflect information on defects (e.g., bubbles) within the injection molded article.
At step 560, the processor 140 may input the X-ray image and the infrared temperature distribution image of the injection molded article into a second predictive model. In some embodiments, the input of the second predictive model may also include information related to the injection molded article and/or injection molding process parameters at the time the injection molded article is prepared.
In some embodiments, the second predictive model may include a machine learning model. In some embodiments, the second predictive model may update distribution information of internal air bubbles of the injection molded article based on the X-ray image and the infrared temperature distribution image of the injection molded article. In some embodiments, the second predictive model may determine distribution information of internal air bubbles of the injection molded article based on an X-ray image of the injection molded article, an infrared temperature distribution image, related information of the injection molded article, and/or injection molding process parameters at the time of preparing the injection molded article.
In some embodiments, the second predictive model may include an X-ray feature extraction layer, an infrared feature extraction layer, and a third decision layer.
In some embodiments, the infrared feature extraction layer of the second predictive model may be directly derived by migration from the first predictive model.
In some embodiments, the infrared feature extraction layer of the second predictive model is independent of the first predictive model. The relevant description of the input and output of the infrared feature extraction layer of the second predictive model may refer to the relevant description of the infrared feature extraction layer in fig. 3.
In some embodiments, the X-ray feature extraction layer may extract an X-ray feature vector of an X-ray image of the injection molded article based on the X-ray image.
In some embodiments, the input of the X-ray feature extraction layer may comprise an X-ray image of the injection molded article. In some embodiments, the input to the X-ray feature extraction layer may also include information about the injection molded article and/or injection molding process parameters at the time the injection molded article is prepared.
In some embodiments, the output of the X-ray feature extraction layer may include an X-ray feature vector.
In some embodiments, the X-ray feature extraction layer may be a Convolutional Neural Network (CNN).
In some embodiments, the determination layer may determine distribution information of internal air bubbles of the injection molded article based on the X-ray feature vector and the infrared feature vector of the injection molded article.
In some embodiments, the input to the decision layer may include an X-ray feature vector and an infrared feature vector. In some embodiments, the input of the decision layer may also include information about the injection molded article and/or injection molding process parameters at the time of preparation of the injection molded article.
In some embodiments, the output of the diagnostic layer may include information on the distribution of internal air bubbles of the injection molded article.
In some embodiments, the judgment layer may be a fully connected layer fully connected with the infrared feature extraction layer and the X-ray extraction layer.
In step 570, the second predictive model may output updated information for the distribution of internal air bubbles of the injection molded article.
The second predictive model may be obtained based on a third training sample.
In some embodiments, the third training sample may include a plurality of infrared temperature distribution images, X-ray images, and labels of the injection molded sample (including quality non-standard). In some embodiments, the infrared temperature distribution image of the injection molded sample may include one or more azimuth infrared distribution images. The label may include information on the distribution of the internal air bubbles of each of the plurality of injection molded samples.
In some implementations, a third training sample may be input to the initial first predictive model for training.
It should be noted that the above description of the flow is only for the purpose of illustration and description, and does not limit the application scope of the present specification. Various modifications and changes to the flow 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.
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 application.
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. A method for hierarchical inspection of injection molded articles, the method comprising:
grabbing an injection molding product through a mechanical arm, and placing the injection molding product in an infrared induction area, wherein an infrared acquisition device is arranged in the infrared induction area;
acquiring an infrared temperature distribution image of the injection molding product through the infrared acquisition device;
determining distribution information and mass fraction of internal air bubbles of the injection molded article based on the infrared temperature distribution image;
acquiring an X-ray detection image of the injection molded article in response to the mass fraction being less than a threshold;
and updating the distribution information of the internal bubbles based on the X-ray detection image.
2. The method of claim 1, wherein the acquiring, by the infrared acquisition device, an infrared temperature distribution image of the injection molded article comprises:
the mechanical arm rotates the injection molding product, and the infrared acquisition device acquires infrared temperature distribution images of the injection molding product in at least two directions.
3. The method of claim 1 or 2, wherein determining distribution information of internal air bubbles of the injection molded article based on the infrared temperature distribution image comprises:
determining distribution information of internal air bubbles of the injection molded article and the mass fraction based on the infrared temperature distribution image using a first predictive model.
4. The method of claim 1, wherein updating the distribution information of the internal bubbles based on the X-ray detection image comprises:
and processing the X-ray detection image and the infrared temperature distribution image based on a second prediction model, and determining updated distribution information of the internal bubbles.
5. A system for hierarchical inspection of injection molded articles, the system comprising:
the mechanical arm is used for grabbing injection molding products and is placed in the infrared induction area;
the infrared acquisition device is positioned in the infrared induction area and is used for acquiring infrared temperature distribution images of the injection molding product;
a processor for determining distribution information and mass fraction of internal air bubbles of the injection molded article based on the infrared temperature distribution image;
the processor is used for responding to the mass fraction being smaller than a threshold value to acquire an X-ray detection image of the injection molding product; and updating distribution information of the internal air bubbles based on the X-ray detection image.
6. The system of claim 5, wherein the robotic arm is further configured to:
rotating the injection molded article;
the infrared acquisition device is further used for:
and acquiring infrared temperature distribution images of the injection molding product in at least two directions.
7. The system of claim 5 or 6, wherein the processor is further configured to:
determining distribution information of internal air bubbles of the injection molded article and the mass fraction based on the infrared temperature distribution image using a first predictive model.
8. The system of claim 7, wherein the processor is further configured to:
and processing the X-ray detection image and the infrared temperature distribution image based on a second prediction model, and determining updated distribution information of the internal bubbles.
9. An apparatus for quality inspection of injection molded articles, said apparatus 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 1-4.
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 1 to 4.
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| CN113715229A (en) | 2021-11-30 |
| CN113715229B (en) | 2022-07-15 |
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