Mold testing methods, equipment, systems and storage media

Abstract

The application discloses a mold detection method, device, system and storage medium, and relates to the technical field of process control. The method comprises the following steps: collecting first image information in a mold through a collecting device; comparing the first image information with a preset template image through a controller of an upper computer to detect a first residual state in the mold; wherein the preset template image comprises a normal template image and an abnormal template image, and the first residual state comprises a no-residual state and a residual state; if the mold is in the residual state, displaying first prompt information through an operation interface and processing the residual state of the mold, wherein the first prompt information is used for prompting the residual state in the mold; if the mold is in the no-residual state, outputting an ejection control signal to an executing device through the controller; and in response to the control signal, ejecting the mold through the executing device, thereby improving the detection accuracy and process stability of the injection molding machine in the part taking and mold closing process.

Description

Technical Field

[0001] This application relates to the field of process control technology, and in particular to a mold inspection method, equipment, system and storage medium. Background Technology

[0002] With the widespread use of plastic products, injection molding machines have become core production equipment. Robots are widely used in the injection molding machine part handling process because they can improve part handling efficiency and reduce costs.

[0003] In some existing production scenarios where injection molding machines handle parts, inspection equipment is often not introduced during the part removal and mold closing process. Instead, the robot arm is used to complete the part removal and directly close the mold. Alternatively, external inspection equipment is introduced to the injection molding machine to inspect the inside of the mold during the part removal and mold closing process.

[0004] However, solutions without inspection equipment or with external inspection equipment cannot accurately detect product residue inside the mold. Directly removing the part from the injection molding machine and closing the mold afterward can easily damage the mold due to residue. This increases production costs in the injection molding process. It should be noted that existing algorithms for using external inspection equipment to check the inside of the mold require high precision in camera positioning, making them prone to misjudgments and affecting the efficiency and stability of the injection molding process.

[0005] The above content is only used to help understand the technical solution of this application and does not represent an admission that the above content is prior art. Summary of the Invention

[0006] The main objective of this application is to provide a mold inspection method, equipment, system, and storage medium, which aims to solve the technical problem that the insufficient accuracy of existing injection molding machines during part removal and mold closing processes affects the efficiency and stability of the production process.

[0007] To achieve the above objectives, this application proposes a mold inspection method, which is applied to mold inspection equipment.

[0008] The mold inspection equipment includes: a data acquisition device, a host computer, and an execution device. The host computer is equipped with a user terminal, which includes an operation interface and a controller. The operation interface is used to display the workflow of the mold inspection equipment, and the controller is used to output control signals to the data acquisition device and the execution device to control the data acquisition device and the execution device to perform mold inspection.

[0009] The mold inspection method includes:

[0010] In response to the mold opening signal, the acquisition device acquires first image information from the mold.

[0011] The controller of the host computer compares the first image information with a preset template image to detect the first residual state in the mold; wherein, the preset template image includes a normal template image and an abnormal template image, and the first residual state includes a state with no residue and a state with residue.

[0012] If the mold has residue, a first prompt message is displayed through the operation interface, and the residue in the mold is processed. The first prompt message is used to indicate the residue in the mold.

[0013] If the mold is in a state of no residue, the controller outputs an ejection control signal to the actuator.

[0014] In response to the control signal, the mold is ejected by the actuator.

[0015] In one embodiment, the step of ejecting the mold via the actuator in response to the control signal includes:

[0016] In response to the control signal, the acquisition device is triggered to acquire second image information in the mold, the second image information being a real-time image of the mold for secondary detection;

[0017] The second image information is compared with a preset template image to detect the second residual state in the mold;

[0018] If the second residual state of the mold is a residual state, a second prompt message is output. The second prompt message is used to indicate the residual state in the mold and to process the residual state of the mold until the second residual state of the mold is a residue-free state.

[0019] If the second residual state of the mold is a state without residue, then the mold is ejected by the actuator.

[0020] In one embodiment, the preset template image includes a first comparison region, which is a region pre-selected in the preset template image and is used to characterize key image information in the preset template image;

[0021] The step of comparing the first image information with a preset template image to detect the first residual state in the mold includes:

[0022] A second comparison region is selected in the first image information. The second comparison region is associated with the first comparison region. The second comparison region is a region in the first image information that includes visual spots.

[0023] Obtain the spot information of the visual spots in the second comparison region and the first comparison region respectively;

[0024] Based on the visual spot comparison algorithm, the spot information of the visual spots in the second comparison region and the first comparison region is compared to determine the residual state of the first image information. The residual state of the first image information is used to describe the residual state in the mold.

[0025] In one embodiment, the speckle information of the visual speckle includes the number of visual speckles, the position of each visual speckle, the area of ​​each visual speckle, the perimeter of each visual speckle, the roundness of each visual speckle, and the brightness of each visual speckle;

[0026] The step of comparing the speckle information of the second comparison region with that of the first comparison region based on the visual speckle comparison algorithm to determine the residual state of the first image information includes:

[0027] The number of visual spots in the second comparison region is compared with the number of visual spots in the first comparison region;

[0028] If the number of visual spots is inconsistent, the mold residue state corresponding to the first image information is determined to be a residue state.

[0029] If the number of visual spots is the same, then the position, area, perimeter, roundness and brightness of each visual spot in the first comparison area and the second comparison area are calculated in turn to obtain the error result corresponding to each spot information;

[0030] If the error results corresponding to the position, area, perimeter, roundness and brightness of all the visual spots are less than or equal to the preset error threshold, then the mold residue state corresponding to the first image information is determined to be a residue-free state.

[0031] If the error results of the position, area, perimeter, roundness and brightness of any visual spot exceed the preset error threshold, then the mold residue state corresponding to the first image information is determined to be a residue state.

[0032] In one embodiment, the step of sequentially calculating the error of the position, area, perimeter, roundness, and brightness of each visual spot in the first comparison region and the second comparison region to obtain the error result corresponding to each spot information includes:

[0033] When the number of visual spots in the first comparison region is the same as the number of visual spots in the second comparison region, each visual spot in the first comparison region and the second comparison region is paired according to the position of each visual spot in the first comparison region and the second comparison region to obtain at least one paired visual spot;

[0034] The paired visual spots include one visual spot in the first comparison region and the second comparison region respectively, and the two visual spots in the paired visual spots are two visual spots in the first comparison region and the second comparison region whose position error value is less than a preset position threshold.

[0035] Errors are calculated for the position, area, perimeter, roundness, and brightness of the two visual spots in each pair of visual spots to obtain the error result corresponding to each spot information.

[0036] In one embodiment, the step of determining the residual state of the first image information further includes:

[0037] Compare the area information of the two visual spots in each of the paired visual spots;

[0038] Calculate the area error value between the two visual spots in each of the paired visual spots;

[0039] If the area error value of each paired visual spot is less than a preset area threshold, compare the perimeter information of the two visual spots in each paired visual spot.

[0040] Calculate the perimeter error value of the two visual spots in each of the paired visual spots;

[0041] If the perimeter error value of each paired visual spot is less than a preset perimeter threshold, compare the brightness information of the two visual spots in each paired visual spot.

[0042] Calculate the brightness error value between the two visual spots in each of the paired visual spots;

[0043] If the brightness error value of each paired visual spot is less than a preset brightness threshold, the residual state of the first image information is determined to be a state without residue.

[0044] In one embodiment, the mold inspection method further includes:

[0045] Obtain initial image information of the mold in a residue-free state;

[0046] The user terminal's operation interface displays the initial image information through a first operation interface; wherein, the first operation interface is used to perform a selection operation of image selection in the initial image information;

[0047] In response to a user's editing operation in the initial image information, a framed image region is determined in the initial image information; wherein, the framed image region is a sub-image region in the initial image information;

[0048] The second operation interface of the selected image area is displayed through the operation interface of the user terminal; wherein, the second operation interface is used to perform a setting operation of parameter value setting in the selected image area, and the parameter value includes at least one of grayscale range, area range and sensitivity range;

[0049] In response to the user's parameter setting operation on the selected image area, a normal template image is obtained;

[0050] The normal template image is stored in the storage module of the mold inspection device.

[0051] In one embodiment, the mold inspection method further includes:

[0052] When the mold has residue, acquire real-time image information of the mold in the state of residue.

[0053] Extract key parameters of the feature region where the residual spots are located in the real-time image information, wherein the key parameters include the shape features, area range, grayscale distribution and location information of the residual spots;

[0054] The key parameters are compared with the feature parameters of the abnormal template images already stored in the storage module to calculate the feature matching degree.

[0055] If the feature matching degree is greater than or equal to the preset matching threshold, the residual state corresponding to the real-time image information is determined to be a stored abnormal type, and the new template storage operation is not performed.

[0056] If the feature matching degree is less than the preset matching threshold, the residual state corresponding to the real-time image information is determined to be a new anomaly type that has not been stored. The real-time image information is then used as a new anomaly template image, associated with the key parameters of its residual feature region and stored in the storage module. The index information of the anomaly template library is then updated.

[0057] In addition, to achieve the above objectives, this application also proposes a mold inspection system, which includes: a data acquisition device, a host computer, and an execution device. The host computer is equipped with a user terminal, which includes an operation interface and a controller. The operation interface is used to display the workflow of the mold inspection system, and the controller is used to output control signals to the data acquisition device and the execution device to control the data acquisition device and the execution device to perform mold inspection.

[0058] The mold inspection system includes:

[0059] The acquisition module is used to acquire first image information in the mold through the acquisition device in response to the mold opening signal;

[0060] The comparison module is used to compare the first image information with a preset template image through the controller of the host computer to detect the first residual state in the mold; wherein, the preset template image includes a normal template image and an abnormal template image, and the first residual state includes a state with no residue and a state with residue.

[0061] The first output module is used to display a first prompt message through the operation interface if the mold is in a state of residue, and to process the state of residue in the mold. The first prompt message is used to indicate the state of residue in the mold.

[0062] The second output module is used to output an ejection control signal to the actuator through the controller if the mold is in a state of no residue.

[0063] A control module is used to eject the mold via the actuator in response to the control signal.

[0064] In addition, to achieve the above objectives, this application also proposes a mold inspection device, the device comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, the computer program being configured to implement the steps of the mold inspection method as described above.

[0065] In addition, to achieve the above objectives, this application may also propose a storage medium, which is a computer-readable storage medium, on which a computer program is stored, and which, when executed by a processor, implements the steps of the mold detection method described above.

[0066] In addition, to achieve the above objectives, this application may also provide a computer program product, which includes a computer program that, when executed by a processor, implements the steps of the resource allocation method described above.

[0067] One or more technical solutions proposed in this application have at least the following technical effects:

[0068] This application is based on a mold detection device that, in response to a mold opening signal, acquires first image information from the mold via a data acquisition device. The controller on the host computer compares the first image information with a preset template image to detect a first residual state in the mold. The preset template image includes normal template images and abnormal template images, and the first residual state includes a state with no residue and a state with residue. If the mold has residue, a first prompt message is displayed on the operation interface, and the residual state is processed. The first prompt message indicates the presence of residue in the mold. If the mold has no residue, the controller outputs an ejection control signal to the execution device. In response to the control signal, the execution device performs ejection processing on the mold.

[0069] Compared to existing technologies where the lack of detection equipment or the inability of external detection equipment to accurately determine the residue inside the mold leads to mold damage after demolding, further affecting the injection molding machine's part removal and mold closing processes and increasing costs, this application precisely solves the problem of inaccurate detection of residue inside the mold by integrating a user terminal into the mold detection equipment and executing the mold detection method based on the controller of the user terminal integrated into the mold detection equipment, and accurately determining the state of residue inside the mold during the operation through image comparison. Specifically, image comparison enables precise identification of residues within the mold. Whether it's residue from intact plastic parts, plastic debris, or impurities, it can be detected promptly and accurately, preventing mold damage caused by residues at the source and ensuring the mold's structural integrity and lifespan. Reduced mold damage allows for smoother part removal and mold closing processes on the injection molding machine, avoiding downtime and adjustments due to mold malfunctions, greatly improving the continuity and stability of injection molding production. Simultaneously, reduced mold damage means lower frequency and cost of mold repair and replacement, reducing additional losses from production interruptions and effectively controlling production costs. Thus, it achieves the beneficial effect of improving the accuracy of residue detection, thereby enhancing the stability and efficiency of the entire mold workflow and reducing costs. Attached Figure Description

[0070] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0071] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, for those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0072] Figure 1 This is a schematic diagram illustrating the application scenario of the mold inspection method of this application;

[0073] Figure 2 This is a flowchart illustrating an embodiment of the mold testing method of this application.

[0074] Figure 3 This is a flowchart illustrating Embodiment 2 of the mold testing method of this application;

[0075] Figure 4 This is a flowchart illustrating Embodiment 3 of the mold testing method of this application;

[0076] Figure 5 This is a schematic flowchart of Embodiment 4 of the mold inspection method of this application;

[0077] Figure 6 This is another schematic diagram of the process provided in Embodiment 4 of the mold inspection method of this application;

[0078] Figure 7 This is a schematic diagram of the module structure of the mold inspection system according to an embodiment of this application;

[0079] Figure 8 This is a schematic diagram of the equipment structure of the hardware operating environment involved in the mold detection method in this application embodiment.

[0080] The purpose, features, and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0081] It should be understood that the specific embodiments described herein are merely illustrative of the technical solutions of this application and are not intended to limit this application.

[0082] To better understand the technical solution of this application, a detailed explanation will be provided below, taking into account the background and current state of the technical field in which the technical solution of this application is located.

[0083] With the widespread application of plastic products, injection molding machines have become core production equipment. Robotic arms are widely used in the injection molding machine part handling process because they can improve part handling efficiency and reduce costs. However, the two mainstream solutions, namely, no inspection equipment and external inspection equipment, both have obvious technical problems, which seriously restrict the efficiency, cost and stability of injection molding production.

[0084] Against this backdrop, injection molding machines can be applied in the following two specific scenarios:

[0085] (1) In the scenario of injection molding machine taking parts and closing mold without testing equipment, the risk of mold damage is high and the efficiency of troubleshooting is low.

[0086] Specifically, due to the lack of monitoring of the in-mold condition, the robotic arm simply picks up the part according to a preset program and directly closes the mold. This can easily lead to issues such as the plastic part sticking to the mold wall and deviations in the part picking trajectory, resulting in product residue inside the mold. Furthermore, the enormous impact force during mold closing can cause wear, deformation, or even cracking of high-precision molds. Repair or replacement significantly increases direct production costs. At the same time, downtime of the injection molding machine caused by mold repair also leads to indirect costs such as lost working hours and order delays. In addition, without monitoring equipment, there is no abnormal feedback mechanism during the injection molding process. Problems can only be identified manually after mold closing failures or product defects occur. This requires checking multiple aspects such as robotic arm parameters and mold condition one by one, which is time-consuming and labor-intensive, further reducing production efficiency.

[0087] (2) The solution of introducing external detection equipment, although it attempts to solve the problem of in-mold detection, has given rise to new problems such as high cost, complex signal interaction, high misjudgment rate and poor adaptability.

[0088] Specifically, from a cost perspective, external devices require additional purchase of dedicated hardware, software, and supporting terminals, resulting in a relatively high initial investment. At the signal interaction level, external devices operate as independent systems from robotic arms and injection molding machines. Due to differences in signal protocols and interfaces, signal delays and losses are prone to occur. This delay can cause the robotic arm to close the mold before completing part removal, leading to equipment collision malfunctions and disrupting production continuity. At the algorithm level, existing external devices mostly employ frame-by-frame pixel grayscale comparison algorithms, which require extremely high camera position accuracy. Displacement caused by equipment vibration can lead to misjudgments. To avoid misjudgments, high-precision camera fixing and regular calibration are necessary, increasing installation and maintenance difficulty. When dealing with diverse residual scenarios such as intact plastic parts, debris, and impurities, misjudgments or omissions are likely to occur, and the risk of mold damage cannot be completely eliminated.

[0089] The main solution of this application embodiment is to systematically solve core problems such as in-mold residue detection, cost control, and production stability through integrated interactive design and precise spot comparison algorithm. Specifically, at the hardware and interaction level, it does not rely on external terminals and independent detection equipment, but directly integrates the camera configuration page, template creation page, and comparison parameter editing page into the existing robot control system. Operators can complete operations such as image acquisition, region selection, and parameter setting through the system's built-in user terminal, reducing equipment investment costs and operating thresholds, while avoiding signal interaction problems between external devices and the original system, and improving the continuity of the production process. At the detection algorithm level, it abandons the traditional frame-by-frame pixel grayscale comparison algorithm and adopts a comparison logic based on visual spots. Specifically, the system first establishes a template to store the target area and spot baseline parameters (number, area, perimeter, brightness, etc.) of the in-mold image under a residue-free state. During detection, the system automatically locks the corresponding area of ​​the real-time image, extracts the real-time spot information, and then compares the area, perimeter, and brightness error percentages according to the number of spots to determine whether the visual spots are consistent. This not only filters out image deviations caused by slight camera shifts but also accurately identifies different types of residues such as complete plastic parts, debris, and impurities, significantly reducing the false judgment rate. At the production collaboration level, the detection process is deeply integrated with the robot arm's part picking and the injection molding machine's mold closing sequence. From the initial detection triggered after mold opening, to the detection result controlling the ejection action, to the secondary mold cavity detection triggered after manual anomaly handling, and finally controlling mold closing based on the secondary detection result, the system achieves the integration of robot arm picking, placement, and mold protection. This ensures that the detection process dynamically adapts to changes in the production process, avoiding the risk of mold damage caused by in-mold residues. At the same time, the system improves the efficiency of fault diagnosis through functions such as abnormal data storage and manual processing records. Ultimately, this reduces production costs while improving the efficiency and stability of injection molding production.

[0090] To make the technical solutions and advantages of the embodiments of this application clearer, the exemplary embodiments of this application will be described in further detail below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of this application, and not an exhaustive list of all embodiments. It should be noted that, unless otherwise specified, the embodiments and features in the embodiments of this application can be combined with each other.

[0091] The following is a brief description of the application environment of the mold inspection method provided in the embodiments of this application:

[0092] Please see Figure 1 , Figure 1 This is a schematic diagram illustrating an application scenario of the mold inspection method provided in this application embodiment. Specifically, the mold inspection method is applied to a mold inspection device 10, which may include: a data acquisition device 11, a host computer 12, and an execution device 13.

[0093] The host computer 12 is equipped with a user terminal 121, which includes an operation interface 1211 and a controller 1212. The operation interface 1211 is used to display the working process of the mold inspection equipment 10, and the controller 1212 is used to output control signals to the acquisition device 11 and the execution device 13 to control the acquisition device 11 and the execution device 13 to perform mold inspection.

[0094] The acquisition device 11 mainly consists of an industrial camera and a matching light source, which is installed near the mold of the injection molding machine. The acquisition device 11 can be used to respond to the mold opening signal from the host computer 12 and quickly capture real-time images of the inside of the mold after the mold is opened, providing raw visual data for subsequent inspection.

[0095] The host computer 12 is the core control hub of the system, directly integrated with the user terminal 121, without relying on any external devices. The host computer 12 can intuitively display the entire inspection process through the operation interface 1211 in the user terminal 121, such as real-time images, inspection results (OK / NG), and abnormal prompts. Operators can complete basic operations such as template settings and parameter adjustments through the interface. The controller 1212 is the decision-making brain of the host computer 12. On the one hand, it receives image data from the acquisition device 11 and uses a preset algorithm to compare and determine whether there is residue in the mold. On the other hand, based on the judgment result, it outputs control signals to the host computer 12 and the execution device 13 to execute the subsequent actions of the injection molding machine.

[0096] The actuator 13 is the action execution end. The actuator 13 is connected to the ejection mechanism and the mold closing mechanism of the injection molding machine. It mainly acts according to the signal of the controller 1212 in the host computer 12: if no residue is detected, it controls the ejection mechanism to eject the plastic part and then allows the mold to close; if residue is detected, it prohibits ejection and mold closing and waits for manual handling, ultimately achieving mold protection and stable production process.

[0097] In addition, the mold inspection device 10 also includes a storage module 14, which is used to save key data and historical records required by the mold inspection device. Specifically, the storage module 14 can be used to store preset template images (normal templates, abnormal templates) and corresponding parameters (such as comparison areas, spot thresholds, etc.), which facilitates quick comparison during inspection. In addition, the storage module 14 can also record the image, judgment result (OK / NG), abnormal event time and snapshot of each inspection, which is convenient for subsequent traceability; at the same time, it supports updating the abnormal template library. After the new type of residue is confirmed, it can be stored in the module to improve the system's recognition capability. Moreover, the data is not lost when power is off, and it can also be queried and exported through the operation interface, taking into account both practicality and convenience.

[0098] It should be noted that the mold inspection equipment 10 proposed in this application is an integrated mold inspection equipment 10 that combines a data acquisition device 11, a host computer 12, and an execution device 13. Through hardware integration and software integration, the technical problems of traditional external inspection equipment in injection molding production scenarios are solved.

[0099] From a system configuration perspective, the mold inspection equipment 10 does not require additional independent external inspection equipment. The acquisition device 11 is directly adapted to the installation environment of the execution device 13 and can be fixed near the mold without a separate support. The host computer 12 is deeply integrated with the mold inspection equipment 10. The user interface 1211 of the user terminal 121 is directly integrated into the mold inspection equipment 10, and the controller 1212 is built into the host computer 12, which can directly output control signals to the acquisition device 11 and the execution device 13, avoiding signal disconnect between external equipment and the original production system.

[0100] External testing equipment, injection molding machines, and robotic arms are independent systems that require complex signal protocol interoperability, which can easily lead to signal delays and losses, causing a disconnect between testing and production. However, the mold testing equipment 10 provided in this application uses an internal unified protocol for communication between the acquisition device 11, the host computer 12, and the execution device 13. The controller can directly link all three. For example, after the mold opening signal is triggered, the controller can simultaneously instruct the acquisition device to take pictures, process the images in real time, and send instructions to the execution device. Signal transmission is delay-free, avoiding production failures caused by signal problems.

[0101] Furthermore, the algorithms of external detection devices often rely on fixed pixel comparison, which requires extremely high camera positioning accuracy; even slight vibrations can lead to misjudgments. In contrast, the controller 1212 of the user terminal 121 mounted on the host computer 12 of the mold detection device 10 in this application can flexibly set the comparison area and parameters through the operation interface 1211 of the user terminal 121 mounted on the host computer 12 using built-in comparison algorithms, such as spot comparison. This can filter out the influence of slight camera offsets and adapt to different residual scenarios of molds (such as plastic parts and debris), resulting in higher detection accuracy and a significantly reduced misjudgment rate.

[0102] Therefore, the mold testing equipment 10 proposed in this application avoids the cost, signal, and compatibility issues of external testing equipment through integrated design. While ensuring testing accuracy, it can also deeply collaborate with the injection molding production process to improve production stability and reduce overall costs.

[0103] Based on this, this application provides a mold inspection method. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a flowchart illustrating the first embodiment of the mold inspection method of this application.

[0104] In this embodiment, the mold detection method includes steps S21 to S25:

[0105] Step S21: In response to the mold opening signal, the first image information in the mold is acquired by the acquisition device.

[0106] The first image information is a real-time image of the mold interior captured by the acquisition device after the mold opening process is completed. This first image information can be used to present the state of key areas such as the mold cavity and core after mold opening, and is a direct visual basis for judging whether there are plastic parts residues, debris, impurities, etc. inside the mold.

[0107] Here, the acquisition device may include an industrial camera, a customized light source, and a dustproof protective housing. This acquisition device can be directly fixed to the mold side or the end effector of a robotic arm using a special clamp adapted to the injection molding machine body. After receiving the mold opening signal, the host computer can immediately output a photo-taking command to the acquisition device via the controller, thereby acquiring real-time first image information of the mold.

[0108] The mold opening signal can be an electrical or digital signal sent by the injection molding machine to the mold inspection equipment after completing the mold opening action. This mold opening signal is used to trigger the inspection system to start the acquisition device to capture the first image information, and it is the starting trigger point of the entire inspection process.

[0109] Step S22: The first image information is compared with the preset template image by the controller of the host computer to detect the first residual state in the mold.

[0110] The preset template images include normal template images and abnormal template images, and the first residual state includes a state with no residue and a state with residue.

[0111] It should be noted that the preset template images serve as the system's reference standard for determining the presence of residues within the mold, including key images for both residual and residue-free states. The normal template image is a baseline image captured and calibrated by the acquisition device when the mold is free of any residue. This image accurately records the original texture of the mold cavity, the core position, and other residue-free features, serving as the reference standard for determining the residue-free state within the mold. The abnormal template images can be a pre-stored collection of images containing various typical residue scenarios, such as images of intact plastic parts with residue, edge debris residue, and metal impurity residue. These images record the characteristics of different residue morphologies, helping the controller quickly identify whether similar residue features appear in new detection images.

[0112] After acquiring real-time first image information of the mold through the acquisition device, the controller can call the built-in visual comparison algorithm to perform pixel-level feature comparison between the real-time image of the mold after mold opening and the preset template image stored in the system. Here, the comparison is not a simple comparison of the overall image, but focuses on key areas such as the mold cavity and core. By analyzing the differences in features such as the number, area, shape, and gray value of spots in the image, the current first residual state of the mold is finally determined.

[0113] Step S23: If the mold has residue, the first prompt message is displayed on the operation interface, and the residue in the mold is processed.

[0114] The first prompt message is used to indicate the presence of residue in the mold.

[0115] Once the controller compares the first image information with the preset template image, it can determine that the real-time state inside the mold is a residual state. The mold detection equipment can then activate the response mechanism for the residual state to deal with the residual state inside the mold.

[0116] Specifically, after confirming the presence of residue inside the mold, the host computer's operating interface can simultaneously display the first warning message. This warning message is presented in an intuitive, multi-dimensional format. For example, a red "NG" indicator pops up on the screen, a text warning indicating that residue has been detected inside the mold and mold closing is prohibited is displayed, and a buzzer or indicator light flashes. These visual and auditory signals attract the operator's attention, ensuring that the abnormal condition is not overlooked. Furthermore, the mold detection equipment can automatically pause subsequent production processes, forcibly maintaining the mold in an open state, preventing mold closing actions caused by misoperation from a hardware perspective.

[0117] Furthermore, after the controller compares the first image information with the preset template image, and the operator sees the first prompt message, the real-time captured first image information can be retrieved through the controller on the user terminal of the host computer. This first image information is displayed on the operation interface to help the operator determine the location and shape of residues inside the mold, such as plastic parts stuck in the corner of the cavity or debris adhering to the core. Targeted cleaning can then be performed, for example, manually removing the residual plastic parts or blowing away debris with an air gun. After cleaning, a secondary detection command can be triggered through the operation interface. The system will then re-capture and compare the image inside the mold, and only after confirming that the residue has been removed will the process lock be released, allowing production to resume.

[0118] In step S24, if the mold is in a state of no residue, the controller outputs an ejection control signal to the actuator.

[0119] The ejection control signal can be a trigger instruction for the mold detection equipment to execute corresponding subsequent steps when the mold is in a residue-free state. This ejection control signal can include a combination of information such as action trigger, force parameters, and stroke range.

[0120] For example, the ejection control signal can specify the extension length of the ejector pin to match the current ejection requirements of the plastic part, the ejection speed to avoid deformation of the plastic part due to excessive speed, and the number of ejections to accommodate complex plastic parts that may require multiple light ejections.

[0121] After receiving the ejection control signal, the actuator can precisely control the ejector pin movement according to the parameters corresponding to the instruction information in the ejection control signal, so as to smoothly eject the plastic part from the mold cavity and prepare for the subsequent robot arm to pick up the part.

[0122] It should be noted that the controller is directly integrated into the host computer and can output a signal instantly after determining that there is no residue, avoiding the situation of delayed ejection action or erroneous ejection caused by signal transmission delay of external devices; at the same time, the signal parameters can be preset through the operation interface and bound to the mold and plastic part type to ensure that the ejection action is adapted to different production scenarios, which not only ensures production efficiency, but also avoids damage to plastic parts or molds caused by improper ejection parameters.

[0123] Step S25: In response to the control signal, the mold is ejected by the actuator.

[0124] When the actuator receives the ejection control signal from the host computer controller, it can immediately activate the internal drive mechanism to respond to the signal. Specifically, the solenoid valve group in the actuator controls the ejector pin drive unit to move according to the parameters in the signal, such as ejection force, stroke, and speed, pushing the ejector pins in the mold to extend from the preset position and smoothly eject the molded plastic part from the cavity. This ensures that the plastic part leaves the mold cavity without deforming the plastic part or damaging the mold due to excessive force. The ejection stroke is matched to the embedding depth in the mold to avoid insufficient ejection causing the plastic part to stick to the mold cavity or excessive ejection causing the ejector pin to collide with the mold.

[0125] After the ejection process is completed, the actuator can send an ejection completion signal to the controller to inform the mold inspection equipment that the ejection action has been completed. This provides a trigger signal for the subsequent robot arm to pick up the part and for secondary inspection to confirm whether there are still residues in the mold after ejection.

[0126] Therefore, by directly linking the actuator and the controller, the ejection action is precisely matched with the detection results and production rhythm, which solves the problem of ejection timing deviation caused by signal delay of traditional external equipment. This not only ensures the stability of plastic part ejection, but also lays the foundation for the safe advancement of the subsequent mold closing process.

[0127] This embodiment provides a mold detection method. A first image of the mold is acquired by a data acquisition device. The first image is compared with a preset template image by a controller on a host computer to detect a first residual state in the mold. The preset template images include normal and abnormal template images, and the first residual state includes a state with no residue and a state with residue. If the mold has residue, a first prompt is displayed on the user interface, and the residual state is processed. The first prompt indicates the presence of residue in the mold. If the mold has no residue, the controller outputs an ejection control signal to an execution device. In response to the control signal, the execution device ejects the mold. Thus, by integrating a user terminal into the mold detection equipment and executing the mold detection method based on the controller of the user terminal, and by accurately determining the residual state inside the mold during operation through image comparison, the method precisely solves the problem of inaccurate detection of residue inside the mold. Specifically, image comparison enables precise identification of residues within the mold. Whether it's residue from intact plastic parts, plastic debris, or impurities, it can be detected promptly and accurately, preventing mold damage caused by residues at the source and ensuring the mold's structural integrity and lifespan. Reduced mold damage allows for smoother part removal and mold closing processes on the injection molding machine, avoiding downtime and adjustments due to mold malfunctions, greatly improving the continuity and stability of injection molding production. Simultaneously, reduced mold damage means lower frequency and cost of mold repair and replacement, reducing additional losses from production interruptions and effectively controlling production costs. Thus, it achieves the beneficial effect of improving the accuracy of residue detection, thereby enhancing the stability and efficiency of the entire mold workflow and reducing costs.

[0128] Based on the first embodiment of this application, a second embodiment of this application is proposed. Please refer to [link to relevant documentation]. Figure 3 , Figure 3 This is a flowchart illustrating the second embodiment of the mold inspection method of this application. As an extension of the first embodiment, in the second embodiment of this application, the content that is the same as or similar to that in the first embodiment described above can be referred to the above description and will not be repeated hereafter. Based on this, the mold inspection method of this application further includes steps S26-S29:

[0129] Step S26: In response to the control signal, the acquisition device is triggered to acquire the second image information in the mold.

[0130] The second image information is a real-time image of the mold undergoing secondary inspection.

[0131] After completing the initial inspection and ejection process, the system enters a secondary inspection phase to further ensure the safety and reliability of the mold. Once the actuator completes the first ejection operation, the mold inspection equipment responds to the corresponding control signal, triggering the acquisition device to capture images of the mold interior, obtaining real-time images for secondary inspection—the second image information. This second image information is used to capture any foreign matter that may remain after ejection, such as fragments of incompletely ejected plastic parts or debris that fell during the ejection process, providing the latest visual evidence for subsequent verification.

[0132] Step S27: Compare the second image information with the preset template image to detect the second residual state in the mold.

[0133] Subsequently, the mold inspection equipment can compare this second image information with the preset template image, and determine the second residual state of the mold by analyzing whether there are any newly added or unremoved foreign objects inside the mold.

[0134] Step S28: If the second residual state of the mold is a residual state, then output the second prompt message.

[0135] The second prompt message is used to indicate the presence of residue in the mold and to process the residue until the second residue state of the mold is a residue-free state.

[0136] If the comparison reveals that the mold still has residue, the mold detection equipment can output a second warning message. This second warning message often indicates the presence of residue, such as a continuous audible and visual alarm and a flashing red warning light on the interface, clearly informing the operator that residue remains after ejection, and simultaneously forcibly pausing all subsequent processes. The operator must accurately locate the residue based on the second image information and take targeted cleaning measures, such as partially disassembling the mold or using specialized tools to remove it. After cleaning, the mold must be inspected again until it is confirmed that there is no residue, ensuring that the residue problem is completely resolved.

[0137] Step S29: If the second residual state of the mold is a state without residue, the mold is ejected by the actuator.

[0138] If the comparison results confirm that the mold is free of residue, it means that the ejection process is thorough and the mold interior is clean. At this point, the mold inspection equipment can perform ejection operations based on the actuator and the corresponding ejection signal, such as fine-tuning the ejector pin reset and confirming that the part-removing robot is ready, thus clearing the final obstacles for the subsequent mold closing process.

[0139] Thus, this series of continuous secondary inspections and processing actions adds another layer of safety on top of the initial inspection, effectively avoiding problems such as missed detection and incomplete ejection that may occur in a single inspection. This upgrades mold inspection from a single confirmation to double protection, making it particularly suitable for production scenarios with high precision requirements and high residual risks, further improving the safety, stability, and product quality of the entire production process.

[0140] This embodiment provides a mold inspection method that, through a secondary inspection mechanism, further detects the residual state of the mold based on the initial inspection, thereby improving inspection accuracy. Specifically, through secondary image acquisition and comparison, it can accurately capture minute foreign objects that may remain after the first ejection, compensating for the risk of missed detection due to changes in lighting or momentary occlusion in a single inspection, and raising the coverage of residue identification to a higher level. Secondly, the second prompt information forms a reinforced processing mechanism for residue problems. Compared with the first inspection, the secondary warning is more targeted and mandates that the process must be completely cleared before continuing, avoiding operator negligence in handling minor residues and ensuring that every residue is thoroughly removed, significantly reducing the batch product defect rate caused by residues. Thus, by upgrading mold inspection from a single to a dual-stage process, while improving inspection reliability, it further reduces production risks and stabilizes product quality. Especially in the demanding field of precision injection molding, it can significantly improve the safety and economy of the production process.

[0141] Based on the first embodiment of this application, a third embodiment of this application is proposed. Please refer to [link to third embodiment]. Figure 4 , Figure 4 This is a flowchart illustrating the third embodiment of the mold inspection method of this application. As a sub-process of step S22 in the first embodiment, in this third embodiment, the preset template image includes a first comparison area. This first comparison area is a pre-selected region within the preset template image, used to characterize key image information in the preset template image. Other content that is the same as or similar to that in Embodiment 1 above can be referred to the above description and will not be repeated hereafter. Based on this, step S22, comparing the first image information with the preset template image through the controller of the host computer to detect the first residual state in the mold, includes steps S221~S223:

[0142] Step S221: Select the second comparison region in the first image information.

[0143] The second comparison region is associated with the first comparison region, and the second comparison region is the region in the first image information that includes visual spots.

[0144] After acquiring the first image information, the mold inspection device can select a second comparison area from the first image information.

[0145] The second comparison area is associated with the preset first comparison area. Specifically, the second comparison area and the first comparison area are selection areas corresponding to key areas such as mold cavities and cores in the real-time image and template image, respectively. Here, visual spots can be the visual features of foreign objects such as residual plastic parts and debris in the image. By locking this area, invalid analysis of irrelevant backgrounds can be avoided, thus improving comparison efficiency.

[0146] Specifically, the second comparison region and the first comparison region can be specifically pointed to the parts in the real-time image and the template image where visual spots appear, respectively. That is, the second comparison region can be the region in the real-time image that contains all visual spots, and the first comparison region can be the region in the template image that contains all visual spots.

[0147] Step S222: Obtain the spot information of visual spots in the second comparison region and the first comparison region, respectively.

[0148] The information about visual spots includes the number of visual spots, the location of each visual spot, the area of ​​each visual spot, the perimeter of each visual spot, the roundness of each visual spot, and the brightness of each visual spot.

[0149] After selecting the second comparison region in the first image information, the mold inspection device can determine the pre-selected first comparison region in the template image and the second comparison region in the real-time image. Thus, the mold inspection device can extract the speckle information of visual spots in the second comparison region and the first comparison region, respectively.

[0150] Here, the speckle information extracted from the second comparison region and the first comparison region can cover multiple dimensions. Specifically, the speckle information can include the number of visual specks, the specific location coordinates of each visual speckle, and the area, perimeter, roundness, and brightness of each visual speckle.

[0151] It should be noted that the position of the visual spot can determine the residual location of the visual spot within the mold. The area, perimeter, and roundness of each visual spot can be used to determine whether the residual object is a regular plastic part. The brightness of the visual spot can be used to distinguish between metal and plastic residues.

[0152] Thus, multi-dimensional acquisition of speckle information of visual specks in the first and second comparison regions was achieved, and the feature parameters of each visual speckle in the first and second comparison regions were determined for subsequent accurate comparison.

[0153] Step S223: Based on the visual blob comparison algorithm, compare the blob information of the visual blobs in the second comparison region with that in the first comparison region to determine the residual state of the first image information.

[0154] Among them, the residual state of the first image information is used to describe the residual state in the mold.

[0155] After determining the spot information extracted from the second comparison area and the first comparison area, the visual spots in the second comparison area and the first comparison area can be compared based on the preset visual spot algorithm and the extracted spot information. After the comparison, the presence of residue in the mold can be determined based on the comparison result, thereby determining the residual state in the mold corresponding to the first image information.

[0156] Here, the mold inspection equipment uses a visual spot comparison algorithm to compare the spot information in the two selected areas, the second comparison area and the first comparison area, item by item, in order to determine whether the visual spots in the second comparison area are consistent with the visual spots in the first comparison area.

[0157] Specifically, the method compares whether the number of spots is consistent. When the number of spots is consistent, it analyzes whether the position of each visual spot matches the residual position in the abnormal template, and compares whether parameters such as area and perimeter conform to the characteristic range of typical residuals. Through this comprehensive comparison of multiple features, the residual state of the mold reflected by the first image information is finally determined. Compared with simple overall image comparison, this analysis method focusing on spot features can filter background interference, significantly improve the recognition accuracy of small and atypical residuals, and reduce false judgments.

[0158] In one specific embodiment, the step of comparing the speckle information of visual specks in the second comparison region and the first comparison region based on the visual speckle comparison algorithm to determine the residual state of the first image information includes: comparing the number of visual specks in the second comparison region with the number of visual specks in the first comparison region; if the number of visual specks is inconsistent, the mold residual state corresponding to the first image information is determined to be a residual state; if the number of visual specks is consistent, the position, area, perimeter, roundness, and brightness of each visual speckle in the first comparison region and the second comparison region are calculated sequentially to obtain the error result corresponding to each speckle information; if the error results corresponding to the position, area, perimeter, roundness, and brightness of all visual specks are less than or equal to a preset error threshold, the mold residual state corresponding to the first image information is determined to be a non-residual state; if the error results of the position, area, perimeter, roundness, and brightness of any visual speckle exceed the preset error threshold, the mold residual state corresponding to the first image information is determined to be a residual state.

[0159] As an example, the mold inspection equipment can prioritize comparing the number of visual spots in the second comparison area with the number of visual spots in the first comparison area. For instance, if the first comparison area of ​​the template image should be free of any spots (normal state), but two spots appear in the second comparison area of ​​the real-time image, this discrepancy in the number of visual spots will directly determine that there are residual spots in the real-time image of the mold; conversely, if the number of spots in both is 0, or if there is one fixed spot in the template image due to the normal structure and there is also exactly one spot in the real-time image, then the next step of feature comparison will proceed.

[0160] When the number of spots is consistent, the mold inspection equipment can perform multi-dimensional error calculations on each corresponding visual spot in the second comparison area and the first comparison area. Here, taking a single spot as an example, assuming the position coordinates of a spot in the template image are (X1, Y1) and the position of the corresponding spot in the real-time image is (X2, Y2), the error calculation will yield the distance difference between the two; the area error is the percentage of the difference between the area of ​​the real-time spot and the area of ​​the template spot relative to the area of ​​the template; the error calculation methods for perimeter, roundness, and brightness are similar.

[0161] Subsequently, the mold inspection equipment compares these error results with preset thresholds. These preset thresholds can be set according to the mold's precision requirements; for example, a position error threshold of 0.5mm and an area error threshold of 5%. If the position, area, perimeter, roundness, and brightness errors of all spots do not exceed the thresholds, it indicates that the spots in the real-time image completely match the normal features in the template, and the real-time image of the mold is determined to be residue-free. For example, if the area of ​​a spot formed by a fixed structure in the template image is 10mm², and the corresponding spot area in the real-time image is 10.3mm², the error is less than the 5% threshold, and other feature errors are also within range, thus it is determined to be residue-free.

[0162] Conversely, if the error of any feature exceeds the threshold, it is directly determined that there is a residual state. For example, if there should be no spots in the template (number 0), but one spot appears in the real-time image (inconsistent number); or if the number of spots is the same, but the area error of a certain spot exceeds the corresponding preset threshold, or the position of the visual spot is offset by 0.8mm (exceeding the corresponding threshold), it can be determined that there is a residual state.

[0163] Therefore, the comparison logic of first comparing quantity and then comparing features can not only quickly identify obvious residues, but also capture subtle differences through multi-dimensional error analysis. Compared with single feature comparison, it is more accurate and effectively reduces the false judgment rate.

[0164] In one specific embodiment, the step of sequentially calculating the error of the position, area, perimeter, roundness, and brightness of each visual spot in the first comparison area and the second comparison area to obtain the error result corresponding to each spot information includes: when the number of visual spots in the first comparison area is the same as the number of visual spots in the second comparison area, pairing each visual spot in the first comparison area and the second comparison area according to the position of each visual spot in the first comparison area and the second comparison area to obtain at least one paired visual spot; wherein, the paired visual spot includes one visual spot in the first comparison area and the second comparison area respectively, and the two visual spots in the paired visual spot are two visual spots in the first comparison area and the second comparison area whose position error value is less than a preset position threshold; and calculating the error of the position, area, perimeter, roundness, and brightness of the spot information of the two visual spots in each paired visual spot to obtain the error result corresponding to each spot information.

[0165] For example, when the number of visual spots in the first comparison area and the second comparison area are equal, the mold inspection device can pair the spots based on the principle of position priority. Specifically, the mold inspection device can calculate the positional error between all visual spots in the first and second comparison areas, which can be represented by pixel distance or actual physical distance, and group the spots with positional errors less than a preset positional threshold into a group to form paired visual spots.

[0166] For example, the template image has two visual spots A and B in the first comparison region, and the real-time image has two spots A' and B' in the second comparison region. If the positional error between A and A' is 0.2mm (less than the 0.3mm threshold) and the positional error between B and B' is 0.15mm (less than the threshold), then two pairs of paired spots (A, A') and (B, B') are formed. If a spot cannot find a corresponding spot with a positional error that meets the standard in another region (e.g., the error between A and B' is 0.5mm, which exceeds the threshold), then the spot cannot be paired, and the residual judgment is directly triggered.

[0167] After pairing the visual spots in the first and second comparison areas, the mold inspection equipment can perform item-by-item error calculations on each feature of each paired visual spot. Specifically, the positional error can be determined by the difference between the coordinates of two points. The calculation formula for item-by-item error calculation for each feature of each paired visual spot is as follows:

[0168] (1) The positional error between the coordinates of point A (x1, y1) and point A' (x2, y2) can be determined by the straight-line distance between point A and point A'.

[0169] (2) The formula for calculating the area error between the coordinates of point A and point A' is:

[0170] Real-time spot area (A') - Template spot area (A) ÷ Template spot area × 100%;

[0171] (3) The formula for calculating the error between the coordinates of point A and the perimeter of point A' is:

[0172] Real-time spot perimeter (A') - Template spot perimeter (A) ÷ Template spot perimeter × 100%;

[0173] (4) The formula for calculating the brightness error between the coordinates of point A and point A' is:

[0174] Real-time spot perimeter (A') - Template spot perimeter (A) ÷ Template spot perimeter × 100%

[0175] Here, the error calculation methods for perimeter, roundness, and brightness are similar. They all use the features of visual spots in the template image as a benchmark to calculate the percentage or absolute value of the feature deviation of visual spots in the real-time image in order to determine the corresponding error value.

[0176] For example, in a pair of visual spots, the template spot area is 8 mm², the real-time spot area is 8.4 mm², and the area error is 5%; the roundness of the template spot is 0.8 (close to a circle), the roundness of the real-time spot is 0.78, and the error is 0.02; other feature errors are also within the preset range, then the error result of this pair of spots is valid. Thus, by calculating the feature errors of all paired spots, the accuracy of residue detection is further improved.

[0177] Therefore, by pairing and then calculating the visual spots in the first and second comparison areas, the problem of misjudgment when the number of visual spots is the same but the position or shape is misaligned is solved. This method is especially suitable for scenarios where there are fixed structural spots in the mold. It can accurately distinguish between normal structural spots and residual spots with positional deviations, making the comparison results more in line with actual production needs.

[0178] In one specific embodiment, the step of determining the residual state of the first image information further includes: comparing the area information of two visual spots in each paired visual spot; calculating the area error value of two visual spots in each paired visual spot; if the area error value of each paired visual spot is less than a preset area threshold, comparing the perimeter information of two visual spots in each paired visual spot; calculating the perimeter error value of two visual spots in each paired visual spot; if the perimeter error value of each paired visual spot is less than a preset perimeter threshold, comparing the brightness information of two visual spots in each paired visual spot; calculating the brightness error value of two visual spots in each paired visual spot; and if the brightness error value of each paired visual spot is less than a preset brightness threshold, determining that the residual state of the first image information is a state without residue.

[0179] As an example in a specific scenario, the process begins by loading the original image, then acquiring the blob information from the original image, followed by calling an open-source blob detection function, setting parameter thresholds, and then calling an open-source analysis interface. This interface returns the number of visual blobs in the detected image and the template image. Next, it checks whether the number of blobs in the real-time image and the template image are consistent. If they are inconsistent, an NG result is output directly with an NG message, and the process ends here. If the number of blobs is consistent, the process can proceed to the roundness comparison stage of the total blobs, calculating the roundness percentage of each blob. The specific calculation formula is: (Total roundness of template image - Total roundness of detected image) / Total roundness in template image multiplied by 100%. The process involves comparing the difference in overall roundness. If the difference is greater than or equal to a set threshold, an NG result is output with an NG message. If the difference is less than the set threshold, the process proceeds to calculate the positional percentage of the total perimeter of the spots: (total perimeter of the template image - total perimeter of the detection image) divided by the total perimeter of the template image, multiplied by 100%. The difference in total perimeter is then compared. If the difference is greater than or equal to the set threshold, an NG result is output with an NG message. If the difference is less than the set threshold, the process proceeds to calculate the positional percentage of the total brightness of the spots: (total brightness of the template image - total brightness of the detection image) divided by the total brightness of the template image, multiplied by 100%. The difference in total brightness is then compared. If the difference is greater than or equal to the set threshold, an NG result is output with an NG message. If the difference is less than the set threshold, an OK result is output, and the process ends. In this way, by comparing the number of spots, roundness, perimeter, and brightness dimensions sequentially, and strictly adhering to the relationship between the differences in each dimension and the thresholds, the final result is determined as either OK or NG, thus achieving the detection and judgment of the target object.

[0180] This embodiment constructs a coherent and rigorous detection process by sequentially and precisely comparing multiple dimensions of image features, such as the number of spots, roundness, perimeter, and brightness, and using the relationship between the differences in each dimension and preset thresholds as the judgment basis. From loading the original image and acquiring spot information to gradually performing multi-dimensional feature calculations and difference comparisons, the result of each step directly determines the subsequent process, with each step closely linked and logically connected. In this way, it is possible to accurately and comprehensively detect and judge target objects (such as mold residue), effectively distinguishing between qualified and unqualified situations. This not only improves the accuracy and reliability of detection but also allows for timely output of NG results and related information when non-compliance occurs, providing a clear basis for subsequent processing and thus ensuring the quality and efficiency of the production or inspection process.

[0181] Based on the first embodiment of this application, a fourth embodiment of this application is proposed. Please refer to [link to previous document]. Figure 5 , Figure 5 This is a flowchart illustrating the fourth embodiment of the mold inspection method of this application. As an extension of the first embodiment, in this fourth embodiment, the content that is the same as or similar to that of Embodiment 1 described above can be referred to the above description and will not be repeated hereafter. Based on this, the mold inspection method of this application further includes steps S51 to S56:

[0182] Step S51: Obtain initial image information of the mold in a residue-free state.

[0183] The mold inspection equipment can first acquire initial image information when the mold is in a residue-free state. Here, the initial image information can be used to record the original visual characteristics of the mold in a clean state.

[0184] Step S52: Display the first operation interface for initial image information through the user terminal's operation interface.

[0185] The first operation interface is used to perform the selection operation of selecting an image frame in the initial image information.

[0186] Step S53: In response to the user's editing operation in the initial image information, determine the selected image region in the initial image information.

[0187] The selected image region is a sub-image region in the initial image information.

[0188] Once the initial image information is acquired, the user terminal installed on the host computer of the mold inspection equipment can display the first operation interface through the operation interface. This first operation interface allows the user to select areas in the initial image information. Through this interactive editing, the user can define the sub-image areas of focus (i.e., select image areas) according to the inspection requirements, such as key parts of the mold cavity and core, thereby eliminating irrelevant background interference and making subsequent inspections more targeted.

[0189] Step S54: Display the second operation interface of the selected image area through the operation interface of the user terminal.

[0190] The second operation interface is used to perform parameter value setting operations in the selected image area. The parameter values ​​include at least one of grayscale range, area range, and sensitivity range.

[0191] Step S55: In response to the user's parameter setting operation on the selected image area, a normal template image is obtained.

[0192] After selecting the sub-image region in the initial image information through the second operation interface, the mold inspection device can also display the second operation interface. Here, the second operation interface can be used specifically to set parameter values ​​for the selected image region, including at least one parameter such as grayscale range, area range, and sensitivity range.

[0193] Here, the grayscale range can be used to distinguish the light and dark features of different materials, the area range can be used to define the size boundaries of normal structures, and the sensitivity range can be used to adjust the system's sensitivity to anomalies in that area.

[0194] After the user sets these parameters according to the actual situation of the mold, the system will merge the selected area and parameter configuration to generate a normal template image that reflects the normal state of the mold.

[0195] Step S56: Store the normal template image to the storage module of the mold inspection device.

[0196] After identifying a suitable template image, it can be stored in the storage module of the mold inspection equipment, serving as a benchmark for subsequent actual inspections. Specifically, when the mold inspection equipment acquires real-time images, it can call upon the suitable template image for comparison. By judging the feature differences between the real-time image and the template within the selected area (whether they exceed the parameter setting range), it can accurately identify whether there are residues in the mold, thereby ensuring the accuracy and specificity of the inspection. In this way, through the combination of user interaction and system processing, the template image can not only closely match the actual characteristics of the mold but also meet personalized inspection needs, laying a reliable foundation for subsequent automated inspections.

[0197] Please see Figure 6 , Figure 6 This is another flowchart illustrating the mold inspection method in the fourth embodiment of this application. Specifically, in another embodiment, the mold inspection method further includes steps S57-S61:

[0198] Step S57: When the mold is in a state with residue, acquire real-time image information of the mold in the state with residue.

[0199] Step S58: Extract key parameters of the feature regions where residual spots are located in the real-time image information.

[0200] Key parameters include the shape characteristics, area range, grayscale distribution, and location information of residual spots.

[0201] When a mold is determined to have residual conditions, the mold inspection equipment can acquire real-time image information of that condition and capture the current residual spots in the real-time image information. These residual spots are visual residual spots.

[0202] After obtaining the residual spots in the real-time image information, the key parameters of the feature region where the residual visual spots are located can be extracted from the real-time image. These key parameters can include the shape features, area range, grayscale distribution, and specific location information of the residual spots.

[0203] Here, the shape characteristics of the residual spots are used to determine whether they are regular geometric shapes and whether the edges are smooth; the area range of the residual spots is used to determine the size of the residual spots; and the gray distribution of the residual spots is used to determine the brightness and distribution pattern of the residual spots.

[0204] Step S59: Compare the key parameters with the feature parameters of the abnormal template images stored in the storage module, and calculate the feature matching degree.

[0205] Among them, the feature matching degree is used to reflect the similarity between the residual spots and the types of residual spots in the known anomalous template image.

[0206] After determining the key parameters of the residual spots in the real-time image information, the mold inspection equipment can compare the key parameters of the residual spots in this real-time image information with the feature parameters of the existing abnormal template images in the storage module, and calculate the feature matching degree between the two.

[0207] In step S60, if the feature matching degree is greater than or equal to the preset matching threshold, the residual state corresponding to the real-time image information is determined to be a stored abnormal type, and the new template storage operation is not performed.

[0208] Step S61: If the feature matching degree is less than the preset matching threshold, the residual state corresponding to the real-time image information is determined to be a new abnormal type that has not been stored. The real-time image information is used as a new abnormal template image, the key parameters of its residual feature region are associated and stored in the storage module, and the index information of the abnormal template library is updated.

[0209] If the feature matching degree reaches or exceeds the preset threshold, it means that the residual spots belong to the abnormal types already recorded by the mold inspection equipment, such as common plastic part edge residues or debris residues in specific locations. There is no need to add a new template; existing processing solutions can be directly called.

[0210] If the matching degree is lower than the preset threshold, it indicates that the current residue is a new anomaly type not stored in the system, such as a residue form or location that has never appeared before. At this time, the mold inspection device can use this real-time image information as a new anomaly template image, associate it with the key parameters of its residue feature region, store it together in the storage module, and synchronously update the index information of the anomaly template library to ensure that the new type can be quickly retrieved during subsequent inspections.

[0211] This embodiment uses a real-time capture, feature extraction, comparison and judgment, and dynamic update scheme to continuously enrich the abnormal template library in the mold inspection equipment with actual production. This avoids the repeated storage of the same type of abnormality and can promptly incorporate new abnormality types, gradually improving the ability to identify various complex residues. This makes the mold inspection equipment more adaptable to diverse production scenarios and reduces the risk of missed detection due to unknown abnormalities.

[0212] It should be noted that the above examples are only for understanding this application and do not constitute a limitation on the mold inspection method of this application. Any simple modifications based on this technical concept are within the protection scope of this application.

[0213] This application also provides a mold inspection system 70, which includes: a data acquisition device 11, a host computer 12, and an execution device 13. The host computer 12 is equipped with a user terminal 121, which includes an operation interface 1211 and a controller 1212. The operation interface 1211 is used to display the workflow of the mold inspection system 70, and the controller 1212 is used to output control signals to the data acquisition device 11 and the execution device 13 to control the data acquisition device 11 and the execution device 13 to perform mold inspection. Please refer to [reference needed]. Figure 7 The mold inspection system 70 includes:

[0214] Acquisition module 71 is used to acquire first image information in the mold through the acquisition device in response to the mold opening signal;

[0215] The comparison module 72 is used to compare the first image information with a preset template image through the controller of the host computer to detect the first residual state in the mold; wherein, the preset template image includes a normal template image and an abnormal template image, and the first residual state includes a state with no residue and a state with residue.

[0216] The first output module 73 is used to display a first prompt message through the operation interface if the mold is in a state of residue, and to process the state of residue in the mold. The first prompt message is used to indicate the state of residue in the mold.

[0217] The second output module 74 is used to output an ejection control signal to the actuator through the controller if the mold is in a state of no residue.

[0218] The control module 75 is used to eject the mold via the actuator in response to the control signal.

[0219] The mold inspection equipment provided in this application, employing the mold inspection method described in the above embodiments, can solve the technical problem that insufficient accuracy of existing injection molding machines during part removal and mold closing processes affects the efficiency and stability of the production process. Compared with the prior art, the beneficial effects of the mold inspection equipment provided in this application are the same as those of the mold inspection method provided in the above embodiments, and other technical features of the mold inspection equipment are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0220] This application provides a mold inspection device, which includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, which are executed by the at least one processor to enable the at least one processor to perform the mold inspection method in Embodiment 1 above.

[0221] The following is for reference. Figure 8 The diagram illustrates a structural schematic of a mold inspection device suitable for implementing embodiments of this application. The mold inspection device in these embodiments may include, but is not limited to, mobile terminals such as mobile phones, laptops, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. Figure 8 The mold inspection equipment shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0222] like Figure 8As shown, the mold inspection equipment may include a processing unit 1001 (e.g., a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 into a random access memory (RAM) 1004. The RAM 1004 also stores various programs and data required for the operation of the mold inspection equipment. The processing unit 1001, ROM 1002, and RAM 1004 are interconnected via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, touchpad, keyboard, mouse, image sensor, microphone, accelerometer, gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), speaker, vibrator, etc.; storage devices 1003 including, for example, magnetic tape, hard disk, etc.; and communication devices 1009. Communication device 1009 allows the mold inspection equipment to communicate wirelessly or wiredly with other devices to exchange data. Although the figures show mold inspection equipment with various systems, it should be understood that implementation or possession of all the systems shown is not required. More or fewer systems may be implemented alternatively.

[0223] Specifically, according to the embodiments disclosed in this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments disclosed in this application include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from storage device 1003, or installed from ROM 1002. When the computer program is executed by processing device 1001, it performs the functions defined in the methods of the embodiments disclosed in this application.

[0224] It should be understood that the various parts disclosed in this application can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics can be combined in any suitable manner in one or more embodiments or examples.

[0225] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0226] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., a computer program) stored thereon, the computer-readable program instructions being used to execute the mold detection method in the above embodiments.

[0227] The computer-readable storage medium provided in this application may be, for example, a USB flash drive, but is not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems or devices, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system or device. The program code contained on the computer-readable storage medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination thereof.

[0228] The aforementioned computer-readable storage medium may be included in the mold inspection equipment; or it may exist independently and not be assembled into the mold inspection equipment.

[0229] Computer program code for performing the operations of this application can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a Local Area Network (LAN) or a Wide Area Network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0230] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0231] The modules described in the embodiments of this application can be implemented in software or hardware. The names of the modules do not necessarily limit the functionality of the unit itself.

[0232] The readable storage medium provided in this application is a computer-readable storage medium that stores computer-readable program instructions (i.e., a computer program) for executing the above-described mold inspection method. This solves the technical problem that insufficient accuracy in the part removal and mold closing processes of existing injection molding machines affects the efficiency and stability of the production process. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in this application are the same as those of the mold inspection method provided in the above embodiments, and will not be repeated here.

[0233] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the mold detection method described above.

[0234] The computer program product provided in this application can solve the technical problem that insufficient accuracy in the part removal and mold closing processes of existing injection molding machines affects the efficiency and stability of the production process. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as those of the mold detection method provided in the above embodiments, and will not be repeated here.

[0235] The above description is only a part of the embodiments of this application and does not limit the patent scope of this application. All equivalent structural transformations made under the technical concept of this application and using the contents of the specification and drawings of this application, or direct / indirect applications in other related technical fields, are included in the patent protection scope of this application.

Claims

1. A mold detection method characterized by, The mold detection method is applied to a mold detection device, wherein the mold detection device comprises a collection device, a host computer and an execution device, the host computer is provided with a user terminal, the user terminal comprises an operation interface and a controller, the operation interface is used for displaying the working process of the mold detection device, and the controller is used for outputting control signals to the collection device and the execution device to control the collection device and the execution device to perform mold detection; The mold detection method comprises: In response to an opening signal, the first image information in the mold is collected by the collection device; The first image information is compared with a preset template image by the controller of the host computer to detect the first residual state in the mold; wherein the preset template image comprises a normal template image and an abnormal template image, and the first residual state comprises a no residual state and a residual state; If the mold is in the residual state, first prompt information is displayed on the operation interface, and the residual state of the mold is processed, and the first prompt information is used to prompt the residual state in the mold; If the mold is in the no residual state, an ejection control signal is output to the execution device by the controller; In response to the control signal, the mold is ejected by the execution device, comprising: In response to the control signal, the collection device is triggered to collect the second image information in the mold, and the second image information is real-time image for secondary detection of the mold; The second image information is compared with a preset template image to detect the second residual state in the mold; If the second residual state of the mold is in the residual state, second prompt information is output, the second prompt information is used to prompt the residual state in the mold, and the residual state of the mold is processed until the second residual state of the mold is in the no residual state; If the second residual state of the mold is in the no residual state, the mold is ejected by the execution device; The preset template image comprises a first comparison area, the first comparison area is a pre-selected area in the preset template image, and is used to represent the key image information in the preset template image; The step of comparing the first image information with the preset template image to detect the first residual state in the mold comprises: The second comparison area in the first image information is selected, the second comparison area is associated with the first comparison area, and the second comparison area is an area including visual spots in the first image information; The spot information of the visual spots in the second comparison area and the first comparison area is obtained respectively; The spot information of the visual spots in the second comparison area and the first comparison area is compared based on a visual spot comparison algorithm to determine the residual state of the first image information, and the residual state of the first image information is used to represent the residual state in the mold.

2. The mold detection method according to claim 1, characterized by, The spot information of the visual spots includes the number of visual spots, the position of each visual spot, the area of each visual spot, the perimeter of each visual spot, the roundness of each visual spot, and the brightness of each visual spot. The step of comparing the spot information of the visual spots in the second comparison region and the first comparison region based on the visual spot comparison algorithm to determine the residual state of the first image information includes: comparing the number of visual spots in the second comparison region with the number of visual spots in the first comparison region; if the number of visual spots is inconsistent, determining that the residual state of the mold corresponding to the first image information is a residual state; if the number of visual spots is consistent, sequentially calculating the error of the position, area, perimeter, roundness, and brightness of each visual spot in the first comparison region and the second comparison region to obtain the error result corresponding to each spot information; if the error result corresponding to the position, area, perimeter, roundness, and brightness of all the visual spots is less than or equal to a preset error threshold, determining that the residual state of the mold corresponding to the first image information is a non-residual state; if the error result of the position, area, perimeter, roundness, or brightness of any visual spot exceeds the preset error threshold, determining that the residual state of the mold corresponding to the first image information is a residual state.

3. The mold detection method according to claim 2, characterized by, The step of sequentially calculating the error of the position, area, perimeter, roundness, and brightness of each visual spot in the first comparison region and the second comparison region to obtain the error result corresponding to each spot information includes: in the case that the number of visual spots in the first comparison region is consistent with the number of visual spots in the second comparison region, pairing each visual spot in the first comparison region and the second comparison region according to the position of each visual spot in the first comparison region and the second comparison region to obtain at least one paired visual spot; wherein the paired visual spot includes one visual spot in the first comparison region and one visual spot in the second comparison region, and the two visual spots in the paired visual spot are two visual spots in the first comparison region and the second comparison region with a position error value less than a preset position threshold; calculating the error of the position, area, perimeter, roundness, and brightness of the spot information of the two visual spots in each paired visual spot to obtain the error result corresponding to each spot information.

4. The mold detection method according to claim 3, characterized by, The step of determining the residual state of the first image information further includes: comparing the area information of the two visual spots in each paired visual spot; calculating the area error value of the two visual spots in each paired visual spot one by one; in the case that the area error value of each paired visual spot is less than a preset area threshold, comparing the perimeter information of the two visual spots in each paired visual spot; calculating the perimeter error value of the two visual spots in each paired visual spot one by one; in the case that the perimeter error value of each paired visual spot is less than a preset perimeter threshold, comparing the roundness information of the two visual spots in each paired visual spot; calculating the roundness error value of the two visual spots in each paired visual spot one by one; in the case that the roundness error value of each paired visual spot is less than a preset roundness threshold, comparing the brightness information of the two visual spots in each paired visual spot; calculating the brightness error value of the two visual spots in each paired visual spot one by one. In a case where the perimeter error value of each of the paired visual spots is less than a preset perimeter threshold, comparing the brightness information of the two visual spots in each of the paired visual spots; Calculating the brightness error value of the two visual spots in each of the paired visual spots one by one; In a case where the brightness error value of each of the paired visual spots is less than a preset brightness threshold, determining that the residual state of the first image information is a non-residual state.

5. The mold detection method according to claim 1, characterized by, The mold detection method further comprises: obtaining initial image information of the mold in a non-residual state; displaying a first operation interface of the initial image information through the operation interface of the user terminal; wherein the first operation interface is used to perform a selection operation of image framing in the initial image information; In response to the user's editing operation in the initial image information, determining the framed image region in the initial image information; wherein the framed image region is a sub-image region in the initial image information; displaying a second operation interface of the framed image region through the operation interface of the user terminal; wherein the second operation interface is used to perform a setting operation of parameter value setting in the framed image region, and the parameter value includes at least one of the gray scale range, the area range and the sensitivity range; obtaining a normal template image in response to the user's parameter setting operation on the framed image region; storing the normal template image to the storage module of the mold detection device.

6. The mold detection method according to claim 5, characterized by, The mold detection method further comprises: In a case where the mold is in a residual state, obtaining real-time image information of the mold in the residual state; extracting the key parameters of the feature region where the residual spot is located in the real-time image information, wherein the key parameters include the shape feature, area range, gray scale distribution and position information of the residual spot; comparing the key parameters with the feature parameters of the abnormal template image stored in the storage module, and calculating the feature matching degree; If the feature matching degree is greater than or equal to a preset matching threshold, it is determined that the residual state corresponding to the real-time image information is an abnormal type that has been stored, and a new template storage operation is not performed; If the feature matching degree is less than the preset matching threshold, it is determined that the residual state corresponding to the real-time image information is a new abnormal type that has not been stored, the real-time image information is taken as a new abnormal template image, the key parameters of the residual feature region are associated and stored to the storage module, and the index information of the abnormal template library is updated.

7. A mold detection system applying the method according to any one of claims 1 to 6, characterized in that The mold detection system comprises a collection device, a host computer and an execution device, the host computer carries a user terminal, the user terminal comprises an operation interface and a controller, the operation interface is used to display the working process of the mold detection system, and the controller is used to output control signals to the collection device and the execution device to control the collection device and the execution device to perform mold detection. The mold detection system comprises: The collection module is used to collect the first image information in the mold through the collection device in response to the mold opening signal; The comparison module is configured to compare the first image information with preset template images by the controller of the host computer to detect a first residual state in the mold, wherein the preset template images include normal template images and abnormal template images, and the first residual state includes a no-residual state and a residual state. The first output module is configured to display first prompt information by the operation interface if the mold is in the residual state, and to process the residual state of the mold, wherein the first prompt information is used to prompt the residual state in the mold. The second output module is configured to output an ejection control signal to the execution device by the controller if the mold is in the no-residual state. The control module is configured to respond to the control signal to perform ejection processing on the mold by the execution device.

8. A storage medium, which is a readable storage medium, characterized by, The readable storage medium includes a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the mold detection method according to any one of claims 1 to 6.

Images