Product assembly process screw installation quality analysis, early warning and optimization method and system

Abstract

The application discloses a product assembly process screw installation quality analysis, early warning and optimization method and system, belongs to the product assembly quality control field used in the digital twin field, and comprises the following steps: determining the optimal process parameter range of a screw assembly link through a statistical analysis method, integrating the optimal process parameter range in equipment, utilizing an abnormal situation early warning function to early warn an out-of-tolerance situation in time, and utilizing recorded data to analyze the abnormal situation, determining a model of the relationship between process parameters and assembly quality, and continuously optimizing the process parameter range. The application can analyze the screw installation quality of an equipment production process, continuously optimize process parameters, improve product assembly quality, and has the function of early warning quality abnormal situations in time. Through use of the application, the product assembly reliability can be continuously improved, and the product production cycle can be continuously shortened.

Description

Screw installation quality analysis, early warning and optimization methods and systems in product assembly process Technical Field

[0001] This invention relates to the field of product assembly quality control in the field of digital twins, and more specifically, to a method and system for analyzing, warning, and optimizing screw installation quality in the product assembly process. Background Technology

[0002] With the development of the electronics and information industry, products are becoming increasingly complex, increasing the technical difficulty of assembly and making the processes extremely intricate. Numerous uncertainties exist during assembly, making it difficult to guarantee product quality. Rework and repairs are frequently necessary due to quality issues, especially in the screw installation stage, which severely impacts delivery cycles. Therefore, conducting quality analysis of screw installation during product assembly is an urgent problem to be solved.

[0003] Currently, researchers both domestically and internationally have conducted extensive research on the application of digital twin technology. However, quality-related findings are mostly applied to lifespan and health prediction in product maintenance and 3D visualization monitoring in manufacturing. Effective solutions are still lacking for quality analysis, prediction, and optimization of the product assembly process, especially in screw installation. Summary of the Invention

[0004] The purpose of this invention is to overcome the shortcomings of existing technologies and provide a method and system for analyzing, warning, and optimizing screw installation quality during product assembly. This method can analyze the screw installation quality during equipment production, continuously optimize process parameters, improve product assembly quality, and provide timely warnings for quality anomalies. By using this invention, the reliability of product assembly can be continuously improved, while the product production cycle can be continuously shortened.

[0005] The objective of this invention is achieved through the following approach:

[0006] A method for analyzing, predicting, and optimizing screw installation quality during product assembly, comprising:

[0007] The optimal range of process parameters for screw assembly is determined by statistical analysis and integrated into the equipment. An abnormal situation early warning function is used to promptly warn of out-of-tolerance situations. Furthermore, the recorded data is used to analyze abnormal situations and determine a model of the relationship between process parameters and assembly quality, thereby continuously optimizing the range of process parameters.

[0008] Furthermore, the step of determining the optimal process parameter range for screw assembly through statistical analysis and integrating it into the equipment, utilizing an anomaly warning function to promptly alert for deviations, and analyzing the recorded data to determine a model of the relationship between process parameters and assembly quality, thereby continuously optimizing the process parameter range, specifically includes the following sub-steps:

[0009] S1: Analyze the typical product process flow, identify the key processes that affect product quality, determine the important impact of screw installation on assembly quality, and evaluate whether it can support quality data analysis.

[0010] S2: Narrow and optimize the range of torque parameters recommended in business operations, and use the narrowed and optimized torque range as the theoretical setting value to ensure that the distribution range of the actual torque parameter values ​​does not exceed the initial recommended torque range.

[0011] Furthermore, step S1 includes a sub-step: analyzing the tightening torque parameters of the screws during the mechanical assembly and screw installation process.

[0012] Furthermore, step S2 includes the following sub-steps:

[0013] S21: Divide the torque range recommended by the business into equal intervals according to a certain granularity, and take each division point as the theoretical torque setting value for experimentation.

[0014] S22: Take the division points obtained in S21 as the theoretical torque setting values, obtain the actual torque value groups under each theoretical torque setting value, and calculate the concentration range of each group of actual torque values.

[0015] S23: Find the lower boundary values ​​A and B of two adjacent actual torque value groups. When A is less than the lower boundary of the recommended torque range and B is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to B is taken as the lower boundary of the narrowed and optimized parameter range.

[0016] S24: Find the upper boundary values ​​C and D of two adjacent actual torque value groups. When C is less than the upper boundary of the recommended torque range and D is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to C is taken as the upper boundary of the narrowed and optimized parameter range.

[0017] S25: Use the upper and lower boundaries of the parameter intervals calculated in steps S23 and S24 as the optimization results.

[0018] Further, step S21 includes the following sub-step: the torque range recommended by the business is [1,2], which is divided with a granularity of 0.1 to obtain the division points 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, 2.0, etc.

[0019] A product assembly process screw installation quality analysis, early warning and optimization system includes a processor and a memory. The memory stores a program, and when the program is loaded by the processor, it executes the product assembly process screw installation quality analysis, early warning and optimization method as described in any of the preceding claims.

[0020] The beneficial effects of this invention include:

[0021] This invention establishes a model of the relationship between key process parameters and assembly quality in the screw installation process. This model enables analysis of screw installation quality during equipment production, continuous optimization of process parameters, and improvement of product assembly quality. It also provides timely early warning of quality anomalies. By using this invention, the reliability of product assembly can be continuously improved, while the product production cycle can be continuously shortened. Attached Figure Description

[0022] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0023] Figure 1 is a schematic diagram of a method for determining the optimal range of process parameters provided in an embodiment of the present invention;

[0024] Figure 2 is a distribution diagram of the actual value range of the process parameters before optimization relative to the theoretical set value provided in the embodiment of the present invention;

[0025] Figure 3 is a distribution diagram of the actual value range of the optimized process parameters relative to the theoretical set value provided in the embodiment of the present invention. Detailed Implementation

[0026] All features disclosed in all embodiments of this specification, or steps in all methods or processes implied in the disclosure, may be combined and / or extended or replaced in any way, except for mutually exclusive features and / or steps.

[0027] In the assembly process of electronic products, numerous components are involved, many of which are installed using screws. High assembly precision is required, making it difficult to guarantee the quality of screw installation. This invention provides a method for analyzing, warning, and optimizing screw installation quality during product assembly. By establishing a model of the relationship between key process parameters and assembly quality in the screw installation stage, it can analyze the screw installation quality during equipment production and continuously optimize process parameters to improve product assembly quality. It also provides timely warnings for quality anomalies. By using this invention, the reliability of product assembly can be continuously improved.

[0028] This invention provides a method for analyzing, predicting, and optimizing screw installation quality during product assembly, including:

[0029] Step 1: Analyze the typical product process flow, identify the key processes that affect product quality, determine the significant impact of screw installation on assembly quality, and evaluate whether it can support quality data analysis.

[0030] In the mechanical assembly process, specifically the screw installation stage, a large number of screws of different types are used. The screw torque parameter is crucial; excessive torque may cause the screw to break during later product testing or field applications, while insufficient torque may cause it to loosen. Both excessive and insufficient torque can lead to product assembly quality issues, therefore, it is necessary to analyze the screw tightening torque parameters, as shown in Figure 1.

[0031] Step 2: Typically, there is a recommended torque range for business operations. However, during screw installation, the actual torque value often fluctuates around the theoretical set value, meaning there is a difference between the actual value and the theoretical set value, as shown in Figure 2. If the recommended torque range is used directly to set the theoretical value, the distribution range of the actual torque value will inevitably exceed the recommended torque range.

[0032] Therefore, the range of torque parameters recommended in business operations is narrowed and optimized, and the narrowed and optimized torque range is used as the theoretical set value, so as to ensure that the distribution range of the actual torque parameter values ​​does not exceed the initial recommended torque range, as shown in Figure 3.

[0033] Specific implementation examples are as follows:

[0034] Step 1: Divide the torque range recommended by the business into equal intervals according to a certain granularity, and take each dividing point as the theoretical torque setting value for experimentation. For example, if the torque range recommended by the business is [1,2], it is divided into intervals of 0.1 to obtain dividing points such as 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0.

[0035] Step 2: Take the division points obtained in Step 1 as the theoretical torque setting values, obtain the actual torque value groups under each theoretical torque setting value, and calculate the concentration range of each group of actual torque values.

[0036] Step 3: Find the lower boundary values ​​A and B of two adjacent actual torque value groups. When A is less than the lower boundary of the recommended torque range and B is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to B is taken as the lower boundary of the narrowed and optimized parameter range.

[0037] Step 4: Find the upper boundary values ​​C and D of two adjacent actual torque value groups. When C is less than the upper boundary of the recommended torque range and D is greater than the lower boundary of the recommended torque range, take the torque parameter setting value corresponding to C as the upper boundary of the narrowed and optimized parameter range.

[0038] Step 5: Use the upper and lower boundaries of the parameter intervals calculated in Steps 3 and 4 as the optimization results.

[0039] The above embodiments are preferred embodiments of the present invention, but the embodiments of the present invention are not limited to the embodiments described above. Any changes, modifications, substitutions, combinations, or simplifications made without departing from the spirit and principle of the present invention shall be considered equivalent substitutions and shall be included within the protection scope of the present invention.

[0040] It should be noted that, within the scope of protection defined in the claims of this invention, the following embodiments can be combined and / or extended or replaced in any logical manner from the above specific embodiments, such as the disclosed technical principles, disclosed technical features or implicitly disclosed technical features.

[0041] Example 1

[0042] A method for analyzing, predicting, and optimizing screw installation quality during product assembly, comprising:

[0043] The optimal range of process parameters for screw assembly is determined by statistical analysis and integrated into the equipment. An abnormal situation early warning function is used to promptly warn of out-of-tolerance situations. Furthermore, the recorded data is used to analyze abnormal situations and determine a model of the relationship between process parameters and assembly quality, thereby continuously optimizing the range of process parameters.

[0044] Example 2

[0045] Based on Example 1, the step of determining the optimal process parameter range for screw assembly through statistical analysis and integrating it into the equipment, utilizing an anomaly warning function to promptly warn of out-of-tolerance situations, and analyzing the recorded data to determine a model of the relationship between process parameters and assembly quality, thereby continuously optimizing the process parameter range, specifically includes the following sub-steps:

[0046] S1: Analyze the typical product process flow, identify the key processes that affect product quality, determine the important impact of screw installation on assembly quality, and evaluate whether it can support quality data analysis.

[0047] S2: Narrow and optimize the range of torque parameters recommended in business operations, and use the narrowed and optimized torque range as the theoretical setting value to ensure that the distribution range of the actual torque parameter values ​​does not exceed the initial recommended torque range.

[0048] Example 3

[0049] Based on Example 2, step S1 includes the following sub-step: in the mechanical assembly and screw installation process, the tightening torque parameters of the screws are analyzed.

[0050] Example 4

[0051] Based on Example 2, step S2 includes the following sub-steps:

[0052] S21: Divide the torque range recommended by the business into equal intervals according to a certain granularity, and take each division point as the theoretical torque setting value for experimentation.

[0053] S22: Take the division points obtained in S21 as the theoretical torque setting values, obtain the actual torque value groups under each theoretical torque setting value, and calculate the concentration range of each group of actual torque values.

[0054] S23: Find the lower boundary values ​​A and B of two adjacent actual torque value groups. When A is less than the lower boundary of the recommended torque range and B is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to B is taken as the lower boundary of the narrowed and optimized parameter range.

[0055] S24: Find the upper boundary values ​​C and D of two adjacent actual torque value groups. When C is less than the upper boundary of the recommended torque range and D is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to C is taken as the upper boundary of the narrowed and optimized parameter range.

[0056] S25: Use the upper and lower boundaries of the parameter intervals calculated in steps S23 and S24 as the optimization results.

[0057] Example 5

[0058] Based on Example 4, step S21 includes the following sub-step: the recommended torque range is [1,2], which is divided with a granularity of 0.1 to obtain division points such as 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.0.

[0059] Example 6

[0060] A product assembly process screw installation quality analysis, early warning and optimization system includes a processor and a memory. The memory stores a program, and when the program is loaded by the processor, it executes the product assembly process screw installation quality analysis, early warning and optimization method as described in any one of Examples 1 to 5.

[0061] The units described in the embodiments of the present invention can be implemented in software or hardware, and the described units can also be located in a processor. The names of these units do not necessarily limit the specific unit itself.

[0062] According to one aspect of the present invention, a computer program product or computer program is provided, the computer program product or computer program including computer instructions stored in a computer-readable storage medium. A processor of a computer device reads the computer instructions from the computer-readable storage medium, and executes the computer instructions, causing the computer device to perform the methods provided in the various optional implementations described above.

[0063] In another aspect, embodiments of the present invention also provide a computer-readable medium, which may be included in the electronic device described in the above embodiments; or it may exist independently and not assembled into the electronic device. The computer-readable medium carries one or more programs, which, when executed by the electronic device, cause the electronic device to perform the methods described in the above embodiments.

Claims

1. A method for analyzing, predicting, and optimizing screw installation quality during product assembly, characterized in that, include: The optimal range of process parameters for screw assembly is determined by statistical analysis and integrated into the equipment. An abnormal situation warning function is used to promptly warn of out-of-tolerance situations. And by using the recorded data to analyze anomalies, determine the model of the relationship between process parameters and assembly quality, thereby continuously optimizing the range of process parameters; specifically including: S1: The typical product process flow is analyzed, key processes affecting product quality are identified, the significant impact of screw installation on assembly quality is determined, and its ability to support quality data analysis is evaluated; S2: The recommended torque parameter range is narrowed and optimized, and the narrowed and optimized torque range is used as the theoretical setpoint, thereby ensuring that the distribution range of the actual torque parameter value does not exceed the initial recommended torque range; Step S2 specifically includes the following sub-steps: S21: The recommended torque range is divided into equal intervals according to a certain granularity, and each division point is taken as the theoretical torque setpoint for experimentation; S22: The division points obtained in S21 are used as the theoretical torque setpoints, and the theoretical torque setpoints are obtained. S23: Find the lower boundary values ​​A and B of two adjacent actual torque value groups. When A is less than the lower boundary of the recommended torque range and B is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to B is taken as the lower boundary of the narrowed and optimized parameter interval. S24: Find the upper boundary values ​​C and D of two adjacent actual torque value groups. When C is less than the upper boundary of the recommended torque range and D is greater than the lower boundary of the recommended torque range, the torque parameter setting value corresponding to C is taken as the upper boundary of the narrowed and optimized parameter interval. S25: Take the upper and lower boundaries of the parameter interval calculated in steps S23 and S24 as the optimization result.

2. The method for analyzing, warning, and optimizing screw installation quality during product assembly as described in claim 1, characterized in that, Step S1 includes the following sub-step: in the mechanical assembly and screw installation process, the tightening torque parameters of the screws are analyzed.

3. The method for analyzing, warning, and optimizing screw installation quality during product assembly as described in claim 1, characterized in that, Step S21 includes the following sub-step: The recommended torque range is [1, 2], which is divided with a granularity of 0.1 to obtain the division points 1.0, 1.1, 1.2, 1.3, 1.4, 1.5, 1.6, 1.7, 1.8, 1.9, and 2.

0.

4. A system for analyzing, warning, and optimizing screw installation quality during product assembly, characterized in that, It includes a processor and a memory, in which a program is stored. When the program is loaded by the processor, it executes the product assembly process screw installation quality analysis, early warning and optimization method as described in any one of claims 1 to 3.

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