Real-time quality control method and system for voice coil winding, and related device

Abstract

The present invention relates to a production process optimization method, and in particular, to a real-time quality control method and system for voice coil winding, and a related device. In the method, an influence model is built by means of data analysis to calibrate the correlation between product quality and production line-related performance input parameters, so that debugging personnel can be guided to perform targeted remote parameter adjustment, and continuous trial and error and shutdown adjustment which are performed on a production line in the non-related art have important significance for maintaining stable production capacity of the production line, and can effectively improve the yield and working efficiency of the voice coil production line.

Description

Voice coil winding real-time quality control method, system and related equipment Technical Field

[0001] The present invention relates to a production process optimization method, and in particular to a voice coil winding real-time quality control method, system and related equipment. Background Art

[0002] The voice coil is an important component of sound-generating devices such as speakers. In industrial production, the voice coil is obtained by winding the raw materials using a production machine that includes a cylinder and a bending wire structure.

[0003] In related technologies, the winding production and real-time quality control of voice coils cannot be separated from the assistance of technical personnel. A workshop with four production lines requires five technical debugging personnel to perform quality image comparison, process monitoring, and parameter adjustment operations based on quality changes during the winding process. Technical issues

[0004] However, there are two key issues in the real-time quality control process of voice coils in related technologies:

[0005] 1. Debugging personnel conduct debugging based on data from on-site testing equipment and videos captured by high-definition cameras. Because there is no quantitative data for adjusting the tightness of machine cylinders and knobs, the production process relies heavily on the trial-and-error experience of frontline personnel, which is technically uncontrollable and may lead to production line capacity issues due to the absence of key personnel.

[0006] 2. The debugging personnel are responsible for debugging the equipment PLC parameters, cylinder air pressure, and the tightness of the wire bending knob on the machine. However, due to the uncontrollable fluctuations in raw materials, the debugging personnel are required to be on standby and constantly confirm the quality status. This leads to low work efficiency of technicians and unstable product yield during the production process.

[0007] Therefore, it is necessary to provide a new real-time quality control method for voice coil winding to solve the above problems. Technical Solutions

[0008] The technical problem to be solved by the present invention is to provide a voice coil winding real-time quality control method, system and related equipment that reduce trial and error costs and facilitate human operation.

[0009] To solve the above technical problems, in a first aspect, the present invention provides a method for real-time quality control of voice coil windings, the method comprising the following steps:

[0010] S101. Construct a qualitative influence model on the relationship between parameter input factors and quality inspection results in the voice coil winding process;

[0011] S102, performing real-time inspection of the voice coil produced in the voice coil winding process according to preset defective product inspection rules through machine vision inspection, and obtaining a defective voice coil image of the corresponding voice coil when the preset defective product inspection rules are met;

[0012] S103, obtaining a quality inspection result of the defective voice coil image through a machine self-learning unit;

[0013] S104, adjusting the corresponding parameter input factors according to the qualitative impact model and the quality detection result;

[0014] S105, performing real-time inspection of the voice coil produced in the voice coil winding process after the parameter input factor is adjusted according to preset good product inspection rules through machine vision inspection, and outputting parameter adjustment results when the preset good product inspection rules are met;

[0015] S106 , feeding back the parameter adjustment result to the machine self-learning unit, and returning to step S102 to continue the voice coil winding process production.

[0016] Preferably, the preset defective product detection rule in step S102 is: detecting a voice coil that does not meet the voice coil winding process requirements for N consecutive times, where N is a positive integer.

[0017] Preferably, the preset defective product detection rule in step S102 is: calculating the yield rate of the first A voice coils currently produced, and the yield rate decreases for B consecutive times, where A and B are positive integers.

[0018] Preferably, the preset good product detection rule in step S105 is: a voice coil that meets the voice coil winding process requirements is detected M times in succession, where M is a positive integer.

[0019] Preferably, in step S104, the corresponding parameter input factor is adjusted remotely.

[0020] Preferably, the machine vision detection method in step S102 is CCD detection.

[0021] Preferably, the machine vision detection method in step S105 is a dual detection of CCD detection and AOI detection.

[0022] In a second aspect, the present invention further provides a voice coil winding real-time quality control system, the voice coil winding real-time quality control system comprising:

[0023] The impact model building module is used to build a qualitative impact model related to the parameter input factors and quality inspection results in the voice coil winding process;

[0024] a production inspection module, configured to perform real-time inspection of voice coils produced in the voice coil winding process according to preset defective product inspection rules through machine vision inspection, and to obtain a defective voice coil image of the corresponding voice coil when the preset defective product inspection rules are met;

[0025] A quality detection module, configured to obtain a quality detection result of the defective voice coil image through a machine self-learning unit;

[0026] A parameter adjustment module, configured to adjust the corresponding parameter input factors according to the qualitative impact model and the quality detection result;

[0027] an adjustment detection module, configured to perform real-time detection of the voice coil produced in the voice coil winding process after the parameter input factor is adjusted according to preset good product detection rules through machine vision detection, and output a parameter adjustment result when the preset good product detection rules are met;

[0028] The quality feedback module is used to feed back the parameter adjustment result to the machine self-learning unit and return it to the production detection module to continue the voice coil winding process production.

[0029] In a third aspect, the present invention further provides a computer device comprising: a memory, a processor, and a voice coil winding real-time quality control program stored in the memory and executable on the processor, wherein when the processor executes the voice coil winding real-time quality control program, the steps of the voice coil winding real-time quality control method as described in any one of the above embodiments are implemented.

[0030] In a fourth aspect, the present invention further provides a computer-readable storage medium, on which a voice coil winding real-time quality control program is stored. When the voice coil winding real-time quality control program is executed by a processor, the steps of the voice coil winding real-time quality control method as described in any one of the above embodiments are implemented. Beneficial effects

[0031] Compared with the related art, the real-time quality control method of coil winding of the present invention comprises the following steps:

[0032] S101. Construct a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process; S102. Perform real-time inspection of the voice coils produced in the voice coil winding process according to preset defective detection rules through machine vision inspection, and obtain a defective voice coil image of the corresponding voice coil when the preset defective detection rules are met; S103. Obtain the quality inspection result of the defective voice coil image through a machine self-learning unit; S104. Adjust the corresponding parameter input factors according to the qualitative influence model and the quality inspection results; S105. Perform real-time inspection of the voice coils produced in the voice coil winding process after the parameter input factors are adjusted according to preset good product inspection rules through machine vision inspection, and output the parameter adjustment results when the preset good product inspection rules are met; S106. Feedback the parameter adjustment results to the machine self-learning unit, and return to step S102 to continue the voice coil winding process production. In the above method, an impact model is constructed through data analysis to calibrate the correlation between product quality and production line-related performance input parameters, thereby guiding debugging personnel to make targeted remote parameter adjustments, rather than the continuous trial and error and shutdown adjustments performed on the production line in related technologies. This is of great significance for maintaining the stability of production line capacity and can effectively improve the yield rate and work efficiency of the voice coil production line. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive work, among which:

[0034] FIG1 is a flowchart of the steps of a method for real-time quality control of voice coil winding provided by the present invention;

[0035] FIG2 is a schematic structural diagram of a voice coil winding real-time quality control system provided by an embodiment of the present invention;

[0036] FIG3 is a schematic diagram of the structure of a computer device provided by an embodiment of the present invention. Modes for Carrying Out the Invention

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0038] Example 1

[0039] Please refer to FIG1 , which is a flowchart of the steps of the real-time quality control method for voice coil winding provided by the present invention. The real-time quality control method for voice coil winding includes the following steps:

[0040] S101. Construct a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process.

[0041] Specifically, the voice coil winding process in the embodiment of the present invention refers to a process in which, during the actual production process, relevant personnel such as debugging personnel adjust the parameters of the production line to control production machines such as winding machines to perform voice coil winding production. The qualitative influence model is used to calibrate the correlation between parameter input factors and quality inspection results, wherein the parameter input factors are production machine parameters pre-input by the debugging personnel, and the quality inspection results are quality data of the voice coils actually produced by the production machines.

[0042] S102 , performing real-time inspection on the voice coil produced in the voice coil winding process according to preset defective product inspection rules through machine vision inspection, and obtaining a defective voice coil image when the preset defective product inspection rules are met.

[0043] The machine vision inspection method in step S102 is CCD (Charge Coupled Device) inspection. In this embodiment of the present invention, the good quality standard is set according to the existing voice coil winding process standards. The real-time machine vision inspection in step S102 can detect voice coils that do not meet the good quality standard and obtain images of defective products with defects to be determined according to preset defective product detection rules.

[0044] The preset defective product detection rule in step S102 is: a voice coil that does not meet the voice coil winding process requirements is detected N times in succession, where N is a positive integer.

[0045] In another possible implementation, the preset defective product detection rule in step S102 may be: calculating the yield rate of the first A voice coils currently produced, with the yield rate decreasing B times in a row, where A and B are positive integers. The preset defective product detection rule may be configured as needed based on the actual output and efficiency of the voice coil winding process.

[0046] S103 : Obtaining a quality inspection result of the defective voice coil image through a machine self-learning unit.

[0047] The machine self-learning unit in the embodiment of the present invention is a computing unit based on industrial big data analysis. Specifically, the machine self-learning unit is used to identify specific defects in the voice coil defective image according to the qualitative influence model and provide quality inspection results. Since the quality inspection results are correlated with the parameter input factors in the qualitative influence model, the machine self-learning unit actually determines the actual parameter input factors that cause the defects through the voice coil defective image.

[0048] S104: Adjust the corresponding parameter input factors according to the qualitative impact model and the quality detection result.

[0049] This step can be implemented in a variety of ways, depending on the actual deployment of the real-time voice coil winding quality control method described in an embodiment of the present invention. In step S104, the corresponding parameter input factors are adjusted remotely. This implementation is based on the deployment of a control console for inputting parameters outside the production line. In this case, the winding machine and other production machines input the adjusted parameter input factors through remote parameter control to achieve real-time adjustment of the voice coil winding.

[0050] S105 , performing real-time inspection on the voice coil produced in the voice coil winding process after the parameter input factor is adjusted according to preset good product inspection rules through machine vision inspection, and outputting parameter adjustment results when the preset good product inspection rules are met.

[0051] The preset good product detection rule in step S105 is: a voice coil that meets the voice coil winding process requirements is detected M times in succession, where M is a positive integer.

[0052] The machine vision inspection in step S105 utilizes a dual-processing approach, combining CCD and AOI (Automatic Optical Inspection). This dual-processing approach allows for a more detailed analysis of whether the voice coil meets acceptable quality standards after the parameter input factors have been adjusted, thus avoiding ineffective adjustments. The parameter adjustment results are used to improve the correlation between the parameter input factors and the quality inspection results in the qualitative impact model, allowing the adjusted production line conditions to be reflected in the qualitative impact model.

[0053] S106 , feeding back the parameter adjustment result to the machine self-learning unit, and returning to step S102 to continue the voice coil winding process production.

[0054] Specifically, in the embodiment of the present invention, the adjustment based on the parameter input factors is only used when defective products or a reduced yield rate occur in the production line. Once the adjustment is completed, the voice coil winding process can continue according to the normal production process of the production line. At this time, the winding machine and other production machines have already been producing according to the adjusted parameters, thereby improving the yield rate. After the parameter adjustment results in the embodiment of the present invention are fed back to the machine self-learning unit, the accuracy of the machine self-learning unit in detecting defective products can be improved, thereby improving the work efficiency of the debugging personnel.

[0055] Compared with the related art, the real-time quality control method of coil winding of the present invention comprises the following steps:

[0056] S101. Construct a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process; S102. Perform real-time inspection of the voice coils produced in the voice coil winding process according to preset defective detection rules through machine vision inspection, and obtain a defective voice coil image of the corresponding voice coil when the preset defective detection rules are met; S103. Obtain the quality inspection result of the defective voice coil image through a machine self-learning unit; S104. Adjust the corresponding parameter input factors according to the qualitative influence model and the quality inspection results; S105. Perform real-time inspection of the voice coils produced in the voice coil winding process after the parameter input factors are adjusted according to preset good product inspection rules through machine vision inspection, and output the parameter adjustment results when the preset good product inspection rules are met; S106. Feedback the parameter adjustment results to the machine self-learning unit, and return to step S102 to continue the voice coil winding process production. In the above method, an impact model is constructed through data analysis to calibrate the correlation between product quality and production line-related performance input parameters, thereby guiding debugging personnel to make targeted remote parameter adjustments, rather than the continuous trial and error and shutdown adjustments performed on the production line in related technologies. This is of great significance for maintaining the stability of production line capacity and can effectively improve the yield rate and work efficiency of the voice coil production line.

[0057] Example 2

[0058] An embodiment of the present invention further provides a voice coil winding real-time quality control system. Please refer to FIG2 , which is a schematic structural diagram of a voice coil winding real-time quality control system provided by an embodiment of the present invention. The voice coil winding real-time quality control system 200 includes:

[0059] An influence model building module 201 is used to build a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process;

[0060] The production inspection module 202 is configured to perform real-time inspection of the voice coils produced in the voice coil winding process according to preset defective product inspection rules through machine vision inspection, and obtain a defective voice coil image of the corresponding voice coil when the preset defective product inspection rules are met;

[0061] A quality detection module 203 is configured to obtain a quality detection result of the defective voice coil image through a machine self-learning unit;

[0062] A parameter adjustment module 204 is configured to adjust the corresponding parameter input factors according to the qualitative impact model and the quality detection result;

[0063] An adjustment detection module 205 is configured to perform real-time detection of the voice coil produced in the voice coil winding process after the parameter input factor is adjusted according to a preset good product detection rule through machine vision detection, and output a parameter adjustment result when the preset good product detection rule is met;

[0064] The quality feedback module 206 is used to feed back the parameter adjustment result to the machine self-learning unit and return it to the production inspection module to continue the voice coil winding process production.

[0065] The voice coil winding real-time quality control system 200 can implement the steps in the voice coil winding real-time quality control method in the above embodiment and can achieve the same technical effects. Please refer to the description in the above embodiment and will not be repeated here.

[0066] Example 3

[0067] An embodiment of the present invention further provides a computer device. Please refer to Figure 3, which is a structural diagram of the computer device provided by an embodiment of the present invention. The computer device 300 includes: a memory 302, a processor 301, and a computer program stored in the memory 302 and executable on the processor 301.

[0068] The processor 301 calls the computer program stored in the memory 302 to execute the steps of the voice coil winding real-time quality control method provided by the embodiment of the present invention. Referring to FIG1 , the method specifically includes the following steps:

[0069] S101. Construct a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process.

[0070] S102 , performing real-time inspection on the voice coil produced in the voice coil winding process according to preset defective product inspection rules through machine vision inspection, and obtaining a defective voice coil image when the preset defective product inspection rules are met.

[0071] The preset defective product detection rule in step S102 is: a voice coil that does not meet the voice coil winding process requirements is detected N times in succession, where N is a positive integer.

[0072] The preset defective product detection rule in step S102 is: calculating the yield rate of the first A voice coils currently produced, and the yield rate decreases for B consecutive times, where A and B are positive integers.

[0073] The machine vision detection method in step S102 is CCD detection.

[0074] S103 : Obtaining a quality inspection result of the defective voice coil image through a machine self-learning unit.

[0075] S104: Adjust the corresponding parameter input factors according to the qualitative impact model and the quality detection result.

[0076] In step S104, the corresponding parameter input factor is adjusted remotely.

[0077] S105 , performing real-time inspection on the voice coil produced in the voice coil winding process after the parameter input factor is adjusted according to preset good product inspection rules through machine vision inspection, and outputting parameter adjustment results when the preset good product inspection rules are met.

[0078] The preset good product detection rule in step S105 is: a voice coil that meets the voice coil winding process requirements is detected M times in succession, where M is a positive integer.

[0079] The machine vision inspection method in step S105 is a dual inspection method of CCD inspection and AOI inspection.

[0080] S106 , feeding back the parameter adjustment result to the machine self-learning unit, and returning to step S102 to continue the voice coil winding process production.

[0081] The computer device 300 provided in the embodiment of the present invention can implement the steps in the real-time quality control method for voice coil winding in the above embodiment and can achieve the same technical effects. Please refer to the description in the above embodiment and will not be repeated here.

[0082] Example 4

[0083] An embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. When the computer program is executed by a processor, the various processes and steps in the real-time quality control method for voice coil winding provided in an embodiment of the present invention are implemented, and the same technical effects can be achieved. To avoid repetition, they will not be described here.

[0084] Those skilled in the art will appreciate that all or part of the processes in the above-described method embodiments can be implemented by instructing the relevant hardware through a computer program. The program can be stored in a computer-readable storage medium, and when executed, the program can include the processes in the above-described method embodiments. The storage medium can be a magnetic disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0085] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0086] Through the above description of the embodiments, those skilled in the art will clearly understand that the methods of the above embodiments can be implemented using software plus the necessary general-purpose hardware platform. Of course, hardware can also be used, but in many cases the former is the more preferred implementation method. Based on this understanding, the technical solution of the present invention, or the portion that contributes to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, or optical disk) and includes a number of instructions for enabling a terminal (such as a mobile phone, computer, server, air conditioner, or network device) to execute the methods described in the various embodiments of the present invention.

[0087] The embodiments of the present invention are described above in conjunction with the accompanying drawings. What is disclosed is only a preferred embodiment of the present invention. However, the present invention is not limited to the above-mentioned specific implementation methods. The above-mentioned specific implementation methods are merely illustrative and not restrictive. Under the guidance of the present invention, ordinary technicians in this field can also make many forms and equivalent changes without departing from the scope of protection of the purpose of the present invention and the claims, which are all within the protection of the present invention.

Claims

1. A real-time quality control method for voice coil winding, characterized in that, the real-time quality control method for voice coil winding includes the following steps: S101. Construct a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process; S102. Through machine vision detection, perform real-time detection on the voice coils produced in the voice coil winding process according to the preset defective product detection rules, and obtain the voice coil defective product images of the corresponding voice coils when the preset defective product detection rules are met; S103. Obtain the quality inspection results of the voice coil defective product images through the machine self-learning unit; S104. Adjust the corresponding parameter input factors according to the qualitative influence model and the quality inspection results; S105. Through machine vision detection, perform real-time detection on the voice coils produced in the voice coil winding process after adjusting the parameter input factors according to the preset good product detection rules, and output the parameter adjustment results when the preset good product detection rules are met; S106. Feed back the parameter adjustment results to the machine self-learning unit, and return to step S102 to continue the voice coil winding process production.

2. The real-time quality control method for voice coil winding according to claim 1, characterized in that, the preset defective product detection rule in step S102 is: continuously detect voice coils that do not meet the voice coil winding process requirements for N times, where N is a positive integer.

3. The real-time quality control method for voice coil winding according to claim 1, characterized in that, the preset defective product detection rule in step S102 is: calculate the good product rate of the first A voice coils produced currently, and the good product rate decreases continuously for B times, where A and B are positive integers.

4. The real-time quality control method for voice coil winding according to claim 1, characterized in that, the preset good product detection rule in step S105 is: continuously detect voice coils that meet the voice coil winding process requirements for M times, where M is a positive integer.

5. The real-time quality control method for voice coil winding according to claim 1, characterized in that, in step S104, the way to adjust the corresponding parameter input factors is remote adjustment.

6. The real-time quality control method for voice coil winding according to claim 1, characterized in that, the machine vision detection method in step S102 is CCD detection.

7. The real-time quality control method for voice coil winding according to claim 1, characterized in that, the machine vision detection method in step S105 is double detection of CCD detection and AOI detection.

8. A real-time quality control system for voice coil winding, characterized in that, the real-time quality control system for voice coil winding includes: An influence model construction module, used to construct a qualitative influence model related to parameter input factors and quality inspection results in the voice coil winding process; A production detection module, used to perform real-time detection on the voice coils produced in the voice coil winding process according to the preset defective product detection rules through machine vision detection, and obtain the voice coil defective product images of the corresponding voice coils when the preset defective product detection rules are met; A quality inspection module, used to obtain the quality inspection results of the voice coil defective product images through the machine self-learning unit; A parameter adjustment module, configured to adjust the corresponding parameter input factor according to the qualitative influence model and the quality inspection result; An adjustment detection module, configured to perform real-time detection on the voice coil produced in the voice coil winding process after the parameter input factor is adjusted by machine vision detection according to a preset good product detection rule, and output a parameter adjustment result when the preset good product detection rule is satisfied; A quality feedback module, configured to feedback the parameter adjustment result to the machine self-learning unit, and return to the production detection module to continue the voice coil winding process production.

9. A computer device, characterized in that, it includes: a memory, a processor, and a voice coil winding real-time quality control program stored on the memory and executable on the processor, and when the processor executes the voice coil winding real-time quality control program, the steps in the voice coil winding real-time quality control method described in any one of claims 1-7 are implemented.

10. A computer-readable storage medium, characterized in that, a voice coil winding real-time quality control program is stored on the computer-readable storage medium, and when the voice coil winding real-time quality control program is executed by a processor, the steps in the voice coil winding real-time quality control method described in any one of claims 1-7 are implemented.

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