Automatic shaping and automatic detection method for precision hardware

Abstract

The invention relates to the technical field of precision hardware machining, in particular to an automatic shaping and detecting method for precision hardware. The method comprises the following steps: acquiring a main assembly closed-loop path and a corresponding main assembly tolerance source by identifying external contour features of to-be-shaped precision hardware, accumulating an actual size variable of the closed-loop description main assembly tolerance source on the main assembly closed-loop path, and generating an assembly closed-loop model of the to-be-shaped precision hardware on an assembly scene; and performing tolerance accumulation calculation on the accumulation error item of the main assembly tolerance source by using the assembly closed-loop model, outputting an accumulation tolerance value, performing qualification analysis based on the accumulation tolerance value, controlling the gain coefficient of the accumulation tolerance to correct and update the existing abnormal main assembly tolerance source, and generating an automatic detection result. According to the invention, the shaping processing equipment can be accurately and efficiently controlled to automatically detect the abnormal tolerance of the precision hardware and achieve the automatic shaping effect of accurately shaping and eliminating the precision hardware, and the intelligent automation degree of precision hardware processing is improved.

Description

Technical Field

[0001] The invention relates to the technical field of precision hardware processing, and in particular to an automatic shaping and automatic detection method for precision hardware. Background Art

[0002] Precision hardware refers to metal parts that have strict requirements on size, shape, and fit during the manufacturing process. Such parts are usually used in high-precision equipment and systems, and are widely used in electronics, machinery, automobiles, aerospace, medical equipment and other fields. Precision hardware has the main characteristics of high precision, good surface quality, high strength and durability, and is of great importance in manufacturing processes, application fields and the market. For the product measurement of precision hardware, the most important thing is the assembly tolerance, but the existing precision hardware detection technology is usually a simple feature extraction and comparison operation to determine whether it is qualified or not. The detection results are prone to errors, and it is difficult to perform specific positioning analysis on the required shaping area from the perspective of tolerance under the condition of automation effect, resulting in low detection accuracy; and the existing precision hardware shaping technology is also unable to achieve accurate step-by-step shaping control and stress minimization shaping control of the required shaping area under the condition of automation effect, resulting in over-shaping of the shaped precision hardware, and the product shaping results are extremely deviated from the expected requirements, and both shaping and detection are not accurate. Therefore, it is necessary to develop a method that can improve and optimize the reliability of automated detection and automatic shaping accuracy of precision hardware to solve the above problems. Summary of the invention

[0003] The invention overcomes the shortcomings of the prior art and provides an automatic shaping and automatic detection method for precision hardware.

[0004] To achieve the above object, the technical solution adopted by the present invention is: A first aspect of the present invention provides an automatic shaping and automatic detection method for precision hardware, comprising the following steps: S102: Obtaining a main assembly closed-loop path and a corresponding main assembly tolerance source by identifying the external contour features of the precision hardware to be shaped, accumulating the actual size variables of the main assembly tolerance source on the main assembly closed-loop path by closed-loop description, and generating an assembly closed-loop model of the precision hardware to be shaped in the assembly scene; S104: using the assembly closed-loop model to perform tolerance accumulation calculation on the accumulated error items of the main assembly tolerance source, outputting the accumulated tolerance value of the assembly closed-loop model, performing a qualified analysis based on the accumulated tolerance value and controlling the gain coefficient of the accumulated tolerance to correct and update the abnormal main assembly tolerance source that needs to be shaped, and generating an automatic detection result; S106: Obtaining the abnormal tolerance correction amount of the tolerance source to be shaped in the automatic detection result, and constraining and planning a sub-region model on multiple working condition time series that produces abnormal tolerances on the suspicious contour model that can be shaped of the tolerance source to be shaped based on the abnormal tolerance correction amount, and iteratively solving the equation of the variational curve for the necessary shaping texture of the model in the sub-region model to obtain a shaping equation solution set, so as to perform automatic shaping on the precision hardware to be shaped; S108: After obtaining the solution set of the running shaping equation, the historical shaping path and historical shaping stress monitoring data of the necessary shaping texture of the model are obtained, the grid unit simulation of the model is performed based on the historical shaping path and the historical shaping stress monitoring data, a shaping stress distribution cloud map is generated, a bubble map is constructed, and a chromaticity update analysis is performed based on the shaping stress distribution cloud map, so as to adjust the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area blocks.

[0005] More specifically, the step S102 includes the following steps: Acquire the precision hardware to be shaped, and photograph the precision hardware to be shaped by controlling the industrial camera on the shaping processing equipment to obtain multiple frames of image data of the precision hardware to be shaped; The image contour feature information contained in the multi-frame image data is extracted by using the Hough transform method to obtain the external contour features of the precision hardware to be shaped, and at the same time, the assembly requirements of the precision hardware to be shaped are obtained; Acquire the assembly scene of the precision hardware to be shaped according to the assembly requirements, construct an assembly knowledge graph of the precision hardware to be shaped for the assembly scene based on the assembly requirements, and identify the external contour features through the assembly knowledge graph; After the recognition is completed, the main assembly closed-loop path of the precision hardware to be shaped applied to the assembly scene and the main assembly tolerance source on each main assembly closed-loop path are output, and the established processing area for the precision hardware to be shaped in the shaping processing equipment is obtained; Construct a virtual detection field for a given processing area, divide the virtual detection field into several sub-detection fields based on the main assembly tolerance source, obtain the actual size variable value of each main assembly tolerance source on the precision hardware to be shaped in the corresponding main assembly closed-loop path, and express the cumulative error term of each sub-detection field in the corresponding main assembly closed-loop path by decomposition according to the actual size variable value; A vector dimension chain equation is established, in which a cumulative closed-loop description is performed on the cumulative error terms of each sub-detection field in the corresponding main assembly closed-loop path, and an assembly closed-loop model of the precision hardware to be shaped in the assembly scene is generated.

[0006] More specifically, the step S104 includes the following steps: Obtaining the predetermined tolerance range of the precision hardware to be shaped in each main assembly tolerance source through assembly requirements, and setting the tolerance critical extreme value of each main assembly tolerance source based on the predetermined tolerance range; Acquire actual tolerance measurement values ​​of each main assembly tolerance source, and inject the actual tolerance measurement value of each main assembly tolerance source into the assembly closed-loop model of the precision hardware to be shaped in the assembly scene; When the actual tolerance measurement value of the main assembly tolerance source reaches its corresponding tolerance critical extreme value, the cumulative error item of the corresponding sub-detection field in the assembly closed-loop model is subjected to tolerance accumulation calculation to obtain the cumulative tolerance value of the assembly closed-loop model; If the cumulative tolerance value is less than the allowable cumulative tolerance value, it means that there is no assembly tolerance defect on the precision hardware to be shaped, and the precision hardware to be shaped is calibrated as a qualified precision hardware; If the cumulative tolerance value is greater than the allowable cumulative tolerance value, it means that a main assembly tolerance source on the precision hardware to be shaped has an assembly tolerance defect, and the precision hardware to be shaped is calibrated as an unqualified precision hardware; When the precision hardware to be shaped is an unqualified precision hardware, a gain feedback algorithm is introduced, and the gain of the control cumulative tolerance is calculated in the gain feedback algorithm based on the cumulative tolerance value to obtain the control gain coefficient of the assembly closed-loop model; Correcting and updating the actual tolerance measurement value of each main assembly tolerance source in the assembly closed-loop model according to the control gain coefficient, repeating the correction and adjustment steps of the control gain coefficient until the actual tolerance measurement value of each main assembly tolerance source reaches a predetermined tolerance range, and obtaining the tolerance correction amount of each main assembly tolerance source; An abnormal tolerance correction interval is preset for each main assembly tolerance source. If the tolerance correction amount is not within the corresponding abnormal tolerance correction interval, the main assembly tolerance source is ignored; if the tolerance correction amount is within the corresponding abnormal tolerance correction interval, the main assembly tolerance source is marked as a tolerance source to be shaped, and an automatic detection result is generated.

[0007] More specifically, the step S106 includes the following steps: The external contour features of the tolerance source to be shaped are separated from the external contour features of the precision hardware to be shaped, which are defined as suspicious contour features. The suspicious contour features are constructed by PROE model design software to obtain a suspicious contour model that can be shaped on the tolerance source to be shaped; Obtaining the tolerance correction amount of the tolerance source to be shaped in the automatic detection result, defining it as the abnormal tolerance correction amount, presetting the tolerance abnormal constraint limit according to the abnormal tolerance correction amount, and obtaining the external contour points of the suspicious contour model; Taking the external contour point as the starting planning point, based on the abnormal tolerance correction amount, constraining the planning of the area range where the abnormal tolerance is generated on the suspicious contour model, until the constrained area range meets the maximum constraint limit in the tolerance abnormal constraint limit, and finally obtaining the necessary shaping area model; Obtaining the ideal automatic working condition strategy formulated by the shaping processing equipment for the precision hardware to be shaped, setting a uniform ideal working condition timing according to the ideal automatic working condition strategy, performing ideal timing shaping decomposition on the necessary shaping area model based on the uniform ideal working condition timing, and obtaining a sub-area model corresponding to each working condition timing; For each sub-region model, the necessary shaping texture pattern distribution map of the sub-region model is drawn and the curvature energy functional after variation is calculated to establish the Lagrangian equation and solve it iteratively, and finally the shaping equation solution set of the necessary shaping texture of the model in the sub-region model is obtained by the shaping processing mechanism. Based on the shaping equation solution, the shaping processing equipment is controlled to perform automatic shaping on the precision hardware to be shaped.

[0008] More specifically, for each sub-region model in step S106, by drawing a distribution map of the necessary shaping texture pattern of the sub-region model and calculating the curvature energy functional after variation to establish the Lagrangian equation and iteratively solve it, finally a shaping equation solution set of the necessary shaping texture of the model in the sub-region model by the shaping processing mechanism is obtained, and based on the shaping equation solution, the shaping processing equipment is controlled to perform automatic shaping on the precision hardware to be shaped, which specifically includes the following steps: Obtaining the necessary shaping texture of the model of the sub-region model in the ideal time series shaping decomposition process, and obtaining the position information of the necessary shaping texture of the model on the necessary shaping region model, so as to draw a distribution map of the necessary shaping texture pattern of the model; According to the necessary shaping texture pattern distribution diagram of the model, a shaping space termination vector of the shaping processing mechanism for the sub-region model is set, and a variational curve method is introduced to convert the necessary shaping texture of the model into a variational curve to obtain a variational curve, and the smoothness of the variational curve is calculated to obtain a curvature energy functional; By using the Lagrangian function, the curvature energy functional and the shaping space termination vector are integrated into the Lagrangian function for calculation, so as to generate the Lagrangian equation of the necessary shaping texture of the model in the sub-region model; Preset a minimum value of the curvature energy functional, and discretize the derivative of the variational curve corresponding to the necessary shaping texture of the model based on the curvature energy functional to obtain discrete equation points on the variational curve; Iteratively update and solve the discrete equation points in the Lagrangian equation to control the curvature energy of the variational curve to reach the minimum value of the curvature energy functional, and finally obtain the solution set of the shaping equations for the necessary shaping texture of the model in the sub-region model by the shaping processing mechanism, and upload the solution set of the shaping equations to the control terminal of the shaping processing mechanism.

[0009] More specifically, the step S108 includes the following steps: Obtain the historical shaping path and historical shaping stress monitoring data of the necessary shaping texture of the model after the shaping processing mechanism runs the shaping equation solution set under the ideal automatic working condition strategy, and construct a standard three-dimensional model of the necessary shaping area model in the precision hardware to be shaped through PROE model design software; Taking the historical shaping path as the constraint benchmark, the standard three-dimensional model of the precision hardware to be shaped is divided into a number of grid units; within the constraint benchmark, the historical shaping stress monitoring data is applied to each grid unit through PROE model design software for simulation, and a shaping stress distribution cloud map is generated; Obtain one or more working condition posture retention areas of the shaping mechanism for the historical shaping path, divide the standard three-dimensional model of the necessary shaping area model into a plurality of sub-model area blocks according to the one or more working condition posture retention areas, preset the stress abnormality index interval, and obtain the stress index displayed in the stress distribution cloud map of each sub-model area block based on the shaping stress distribution cloud map; Only the sub-model area blocks whose stress index is within the stress anomaly index interval are extracted and marked as pending model area blocks, the stress color coding of the pending model area blocks displayed in the stress distribution cloud map is obtained, and the missing coding between the stress color coding and the ideal stress color coding is calculated; Constructing a bubble graph, fitting the ideal stress chromaticity code through the bubble graph to generate an ideal stress bubble graph, updating the chromaticity of the ideal stress bubble graph according to the lost code, and stopping updating after the chromaticity code in the ideal stress bubble graph only has the stress chromaticity code of the block in the to-be-determined model region, and obtaining the bubble volume value after stopping updating; If the bubble volume value after stopping updating is less than the preset bubble volume value, the pending model area block is calibrated as a reasonable stress model area block; if the bubble volume value after updating is greater than the preset bubble volume value, the pending model area block is calibrated as an unreasonable stress model area block, and the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area block are adjusted to eliminate the stress concentration phenomenon in the shaping process of precision hardware.

[0010] A second aspect of the present invention provides an automatic shaping and automatic detection system for precision hardware, the automatic shaping and automatic detection system comprising a memory and a processor, the memory storing an automatic shaping and automatic detection method program for precision hardware, and when the automatic shaping and automatic detection method program is executed by the processor, any one of the steps of the automatic shaping and automatic detection method is implemented.

[0011] The present invention solves the technical defects existing in the background technology, and the beneficial technical effects of the present invention are: By identifying the external contour features of the precision hardware to be shaped, the main assembly closed-loop path and the corresponding main assembly tolerance source are obtained, and the actual size variables of the main assembly tolerance source on the main assembly closed-loop path are described by the cumulative closed-loop, and the assembly closed-loop model of the precision hardware to be shaped in the assembly scene is generated; the tolerance accumulation calculation of the cumulative error terms of the main assembly tolerance source is performed using the assembly closed-loop model, and the cumulative tolerance value of the assembly closed-loop model is output. Based on the cumulative tolerance value, a qualified analysis is performed and the gain coefficient correction of the cumulative tolerance is controlled to update the abnormal main assembly tolerance source that needs to be shaped, and an automatic detection result is generated; based on the abnormal tolerance correction of the tolerance source to be shaped The sub-regional models on multiple working condition time series that produce abnormal tolerances are constrained and planned on the suspicious contour model, and the equations of the variational curves are iteratively solved for the necessary shaping textures of the model in the sub-regional model to obtain the shaping equation solution set, so as to perform automatic shaping on the precision hardware to be shaped; after obtaining the historical shaping path and historical shaping stress monitoring data of the necessary shaping textures of the model after running the shaping equation solution set, the grid unit simulation of the model is performed, and the shaping stress distribution cloud map is generated, and the bubble map is constructed and the color update analysis is performed based on the shaping stress distribution cloud map to adjust the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area block. The present invention can accurately and efficiently control the shaping processing equipment to automatically detect abnormal tolerances of precision hardware and the automatic shaping effect of accurately shaping and eliminating them, saving the traditional manual measurement process and the phenomenon of false detection and missed detection, ensuring the standardization and precision of precision hardware, and improving the intelligent automation degree of shaping processing. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, drawings of other embodiments can be obtained based on these drawings without paying creative work.

[0013] Figure 1 A first method flow chart of an automatic shaping and automatic detection method for precision hardware is shown; Figure 2 A second method flow chart of an automatic shaping and automatic detection method for precision hardware is shown; Figure 3 A third method flow chart of an automatic shaping and automatic detection method for precision hardware is shown; Figure 4 The invention shows a system framework diagram of an automatic shaping and automatic detection system for precision hardware. DETAILED DESCRIPTION

[0014] In order to more clearly understand the above-mentioned purpose, features and advantages of the present invention, the present invention is further described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments can be combined with each other without conflict.

[0015] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Therefore, the protection scope of the present invention is not limited to the specific embodiments disclosed below.

[0016] The first aspect of the present invention provides an automatic shaping and automatic detection method for precision hardware, such as Figure 1 As shown, the following steps are included: S102: Obtaining a main assembly closed-loop path and a corresponding main assembly tolerance source by identifying the external contour features of the precision hardware to be shaped, accumulating the actual size variables of the main assembly tolerance source on the main assembly closed-loop path by closed-loop description, and generating an assembly closed-loop model of the precision hardware to be shaped in the assembly scene; S104: using the assembly closed-loop model to perform tolerance accumulation calculation on the accumulated error items of the main assembly tolerance source, outputting the accumulated tolerance value of the assembly closed-loop model, performing a qualified analysis based on the accumulated tolerance value and controlling the gain coefficient of the accumulated tolerance to correct and update the abnormal main assembly tolerance source that needs to be shaped, and generating an automatic detection result; S106: Obtaining the abnormal tolerance correction amount of the tolerance source to be shaped in the automatic detection result, and constraining and planning a sub-region model on multiple working condition time series that produces abnormal tolerances on the suspicious contour model that can be shaped of the tolerance source to be shaped based on the abnormal tolerance correction amount, and iteratively solving the equation of the variational curve for the necessary shaping texture of the model in the sub-region model to obtain a shaping equation solution set, so as to perform automatic shaping on the precision hardware to be shaped; S108: After obtaining the solution set of the running shaping equation, the historical shaping path and historical shaping stress monitoring data of the necessary shaping texture of the model are obtained, the grid unit simulation of the model is performed based on the historical shaping path and the historical shaping stress monitoring data, a shaping stress distribution cloud map is generated, a bubble map is constructed, and a chromaticity update analysis is performed based on the shaping stress distribution cloud map, so as to adjust the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area blocks.

[0017] More specifically, the step S102 includes the following steps: Acquire the precision hardware to be shaped, and photograph the precision hardware to be shaped by controlling the industrial camera on the shaping processing equipment to obtain multiple frames of image data of the precision hardware to be shaped; The image contour feature information contained in the multi-frame image data is extracted by using the Hough transform method to obtain the external contour features of the precision hardware to be shaped, and at the same time, the assembly requirements of the precision hardware to be shaped are obtained; Acquire the assembly scene of the precision hardware to be shaped according to the assembly requirements, construct an assembly knowledge graph of the precision hardware to be shaped for the assembly scene based on the assembly requirements, and identify the external contour features through the assembly knowledge graph; After the recognition is completed, the main assembly closed-loop path of the precision hardware to be shaped applied to the assembly scene and the main assembly tolerance source on each main assembly closed-loop path are output, and the established processing area for the precision hardware to be shaped in the shaping processing equipment is obtained; Construct a virtual detection field for a given processing area, divide the virtual detection field into several sub-detection fields based on the main assembly tolerance source, obtain the actual size variable value of each main assembly tolerance source on the precision hardware to be shaped in the corresponding main assembly closed-loop path, and express the cumulative error term of each sub-detection field in the corresponding main assembly closed-loop path by decomposition according to the actual size variable value; A vector dimension chain equation is established, in which a cumulative closed-loop description is performed on the cumulative error terms of each sub-detection field in the corresponding main assembly closed-loop path, and an assembly closed-loop model of the precision hardware to be shaped in the assembly scene is generated.

[0018] It should be noted that precision hardware is widely used in assembly in fields such as equipment or devices that require precise operation, so it has high processing requirements for its tolerance precision. Usually, the tolerance requirements of precision hardware need to be kept within 0.05mm, but the existing automatic detection technology still has large errors or mistakes in the tolerance detection of precision hardware, and still needs to rely on manual secondary intervention measurement to ensure the tolerance accuracy of precision hardware. This greatly reduces the reliability of precision hardware detection results, increases the use errors of precision hardware and the output of labor costs, and it is difficult to guarantee the quality and efficiency of precision hardware production. Therefore, this method first identifies the external contour features of precision hardware. Since the tolerance requirements of different types of precision hardware in different assembly scenarios are different, it is necessary to obtain the main assembly closed-loop path when the precision hardware is applied to the assembly scenario and the main assembly tolerance source on each main assembly closed-loop path through identification; wherein the main assembly closed-loop path is the path that the precision hardware itself passes through during the assembly process, such as the complete stroke of a screw driven into a certain mechanism; and the main assembly tolerance source is the tolerance position of the precision hardware during the main assembly closed-loop path. Then, by converting the established processing area of ​​the shaping processing equipment into a virtual detection field, the virtual detection field can be divided into sub-detection fields corresponding to its main assembly tolerance source according to the type of precision hardware detected, thereby ensuring that every tolerance requirement position of the precision hardware to be shaped can be detected, avoiding the occurrence of false detection and missed detection. Finally, based on the actual dimensional variable values ​​of each main assembly tolerance source, the cumulative error terms of each sub-inspection field in the corresponding main assembly closed-loop path are accurately decomposed and expressed, and an assembly closed-loop model is constructed through the cumulative closed-loop description of the cumulative error terms. Through this assembly closed-loop model, the dimensional changes of the precision hardware to be shaped when applied to the assembly scenario can be further accumulated, ensuring the dimensional accuracy of the precision hardware applied to the ideal component assembly, and providing a reliable data analysis basis for the subsequent tolerance abnormalities of the main assembly tolerance sources.

[0019] It should be noted that this method can accumulate and describe the actual dimensional changes of the precision hardware to be shaped during the assembly process, thereby improving the accuracy and efficiency of tolerance detection of precision hardware. At the same time, the virtual detection area division effect for the tolerance source can avoid the phenomenon of automated false detection and missed detection of precision hardware, and reduce the re-inspection rate and return inspection rate.

[0020] More specifically, the step S104 includes the following steps: Obtaining the predetermined tolerance range of the precision hardware to be shaped in each main assembly tolerance source through assembly requirements, and setting the tolerance critical extreme value of each main assembly tolerance source based on the predetermined tolerance range; Acquire actual tolerance measurement values ​​of each main assembly tolerance source, and inject the actual tolerance measurement value of each main assembly tolerance source into the assembly closed-loop model of the precision hardware to be shaped in the assembly scene; When the actual tolerance measurement value of the main assembly tolerance source reaches its corresponding tolerance critical extreme value, the cumulative error item of the corresponding sub-detection field in the assembly closed-loop model is subjected to tolerance accumulation calculation to obtain the cumulative tolerance value of the assembly closed-loop model; If the cumulative tolerance value is less than the allowable cumulative tolerance value, it means that there is no assembly tolerance defect on the precision hardware to be shaped, and the precision hardware to be shaped is calibrated as a qualified precision hardware; If the cumulative tolerance value is greater than the allowable cumulative tolerance value, it means that a main assembly tolerance source on the precision hardware to be shaped has an assembly tolerance defect, and the precision hardware to be shaped is calibrated as an unqualified precision hardware; When the precision hardware to be shaped is an unqualified precision hardware, a gain feedback algorithm is introduced, and the gain of the control cumulative tolerance is calculated in the gain feedback algorithm based on the cumulative tolerance value to obtain the control gain coefficient of the assembly closed-loop model; Correcting and updating the actual tolerance measurement value of each main assembly tolerance source in the assembly closed-loop model according to the control gain coefficient, repeating the correction and adjustment steps of the control gain coefficient until the actual tolerance measurement value of each main assembly tolerance source reaches a predetermined tolerance range, and obtaining the tolerance correction amount of each main assembly tolerance source; An abnormal tolerance correction interval is preset for each main assembly tolerance source. If the tolerance correction amount is not within the corresponding abnormal tolerance correction interval, the main assembly tolerance source is ignored; if the tolerance correction amount is within the corresponding abnormal tolerance correction interval, the main assembly tolerance source is marked as a tolerance source to be shaped, and an automatic detection result is generated.

[0021] It should be noted that after generating an assembly closed-loop model that cumulatively describes the actual dimensional changes of each tolerance source in the precision hardware to be shaped in the assembly scenario, if an abnormal dimensional change occurs in a certain tolerance source of the precision hardware to be shaped during the assembly process, it means that the actual tolerance of the tolerance source does not meet the standard tolerance. Therefore, this method sets a predetermined tolerance range for each tolerance source in the assembly closed-loop model. When the critical tolerance limit in the predetermined tolerance range is reached, it is determined that the tolerance of the tolerance source may have a high error rate, so it needs to be added to the tolerance accumulation calculation. The cumulative tolerance value calculated by the assembly closed-loop model is the cumulative sum of the differences between the actual tolerance of each tolerance source that may have a high error rate and the allowable tolerance, and then the overall pass or fail of the precision hardware can be judged, thereby achieving the systematic automatic detection effect of the precision hardware itself and improving the accuracy of product processing quality detection. When the precision hardware to be shaped is an unqualified precision hardware, it is necessary to locate and distinguish the tolerance source positions that need to be shaped and the tolerance source positions that do not need to be shaped in the unqualified precision hardware. This method adjusts the cumulative error by controlling the gain coefficient, thereby correcting the actual tolerance measurement value of each tolerance source, adjusting the assembly state or input to make the tolerance closer to the standard value, and a tolerance correction amount will be fed back during the correction and update process, which can reflect the error amplitude of each tolerance source compared to the marked value; if the tolerance correction amount is not within the corresponding abnormal tolerance correction interval, it means that the tolerance difference of the tolerance source meets the allowable range, so the main assembly tolerance source is ignored, otherwise it means that the tolerance difference of the tolerance source is large and has exceeded the allowable range, so the tolerance source needs to be shaped. Through this method, the tolerance accumulation operation of the assembly closed-loop model can be used to analyze the overall qualification of the precision hardware and locate the position that needs to be shaped, optimize the precision and reliability of the automatic detection effect, and improve the shaping accuracy of subsequent precision hardware.

[0022] More specifically, the step S106 is as follows: Figure 2 As shown, the specific steps include: S202: extracting the external contour features of the tolerance source to be shaped from the external contour features of the precision hardware to be shaped, defining them as suspicious contour features, and constructing the suspicious contour features by using PROE model design software to obtain a suspicious contour model that can be shaped on the tolerance source to be shaped; S204: obtaining a tolerance correction amount of a tolerance source to be shaped in the automatic detection result, defining it as an abnormal tolerance correction amount, presetting a tolerance abnormal constraint limit according to the abnormal tolerance correction amount, and obtaining an external contour point of a suspicious contour model; S206: Taking the external contour point as the starting planning point, constraining the planning of the region range where the abnormal tolerance is generated on the suspicious contour model based on the abnormal tolerance correction amount, until the constrained region range meets the maximum constraint limit in the tolerance abnormal constraint limit, and finally obtaining the necessary shaping region model; S208: Obtaining an ideal automatic working condition strategy formulated by the shaping processing equipment for the precision hardware to be shaped, setting a uniform ideal working condition timing according to the ideal automatic working condition strategy, performing ideal timing shaping decomposition on the necessary shaping region model based on the uniform ideal working condition timing, and obtaining a sub-region model corresponding to each working condition timing; S210: For each sub-region model, the Lagrangian equation is established and solved iteratively by drawing a distribution map of the necessary shaping texture pattern of the sub-region model and calculating the curvature energy functional after variation, and finally a shaping equation solution set of the necessary shaping texture of the model in the sub-region model is obtained by the shaping processing mechanism, and the shaping processing equipment is controlled based on the shaping equation solution to perform automatic shaping on the precision hardware to be shaped.

[0023] It should be noted that the tolerance sources to be shaped detected by automatic detection often have some common tolerance defect characteristics. For example, there is an excess material or a part of the material is missing on a certain order of the screw thread, which causes tolerance phenomenon in the actual assembly process. The shaping of the precision hardware needs to repair the excess or missing material. However, the existing automatic shaping technology usually over-shapes the detected tolerance area, that is, too much or too little material is processed in the tolerance area, making it difficult to achieve standardized and accurate shaping processing control effects based on automation technology, resulting in unsatisfactory automatic shaping effects of precision hardware. The huge automatic shaping error makes the shaping of precision hardware always fail to meet the actual assembly requirements. Therefore, this method constructs a suspicious contour model that can be shaped on the tolerance source to be shaped, so as to plan the area range that causes abnormal tolerance on the suspicious contour model. The area range that causes abnormal tolerance is the model area that needs to be shaped, and for the shaping of the tolerance source, it only needs to be kept within the allowable tolerance range. At this time, it is necessary to impose a reasonable constraint on the planning operation of the model area range. Therefore, this method presets the abnormal tolerance correction amount generated in the previous step as a tolerance abnormal constraint limit, so that the constraint planning on the suspicious contour model can reach the allowable tolerance range, and finally a necessary shaping area model that needs to be shaped can be obtained. The model constraint planning effect of this method can accurately control the shaping processing equipment to process the area part that needs to be shaped on the tolerance source, so that it meets the allowable tolerance requirements, eliminates the error phenomenon of excessive shaping and repair, and improves the precision of automated shaping of precision hardware.

[0024] It should be noted that when the shaping area of ​​certain tolerance sources is large, the shaping range of the processing tools in the shaping equipment for precision hardware is limited, and it needs to be shaped in batches. The existing shaping technology also has batch processing errors for the tolerance batch shaping of precision hardware. For example, when a certain tolerance source needs to be cut in batches, the cutting tool on the equipment produces a control deviation in the process of feeding from the current position to the next position after shaping, resulting in material shaping errors at different feeding stages. In view of this, this method can idealize the time-series shaping decomposition of the necessary shaping area model according to the setting working condition strategy of the processing tool for the tolerance source, and then convert the texture required to be shaped in the decomposed sub-model area into a variational curve, and then iterate and solve it in the form of a solution set of the shaping equation, so that the batch shaping control of the shaping equipment is more accurate and reliable, reducing the control error rate in the batch processing transfer process, and effectively improving the product quality of the segmented shaping of precision hardware.

[0025] More specifically, for each sub-region model in step S106, by drawing a distribution map of the necessary shaping texture pattern of the sub-region model and calculating the curvature energy functional after variation to establish the Lagrangian equation and iteratively solve it, finally a shaping equation solution set of the necessary shaping texture of the model in the sub-region model is obtained by the shaping processing mechanism, and the shaping processing equipment is controlled based on the shaping equation solution to perform automatic shaping on the precision hardware to be shaped, such as Figure 3 As shown, the specific steps include: S302: Obtaining the necessary shaping texture of the model of the sub-region model in the ideal time series shaping decomposition process, and obtaining the position information of the necessary shaping texture of the model on the necessary shaping region model to draw a distribution map of the necessary shaping texture pattern of the model; S304: setting a shaping space termination vector of a shaping mechanism for a sub-region model according to the necessary shaping texture pattern distribution map of the model, introducing a variational curve method to convert the necessary shaping texture of the model into a variational curve, obtaining a variational curve, and calculating the smoothness of the variational curve to obtain a curvature energy functional; S306: using a Lagrangian function, integrating the curvature energy functional and the shaping space termination vector into the Lagrangian function for calculation, and generating a Lagrangian equation of the necessary shaping texture of the model in the sub-region model; S308: Preset a minimum value of the curvature energy functional, and discretize the derivative of the variational curve corresponding to the necessary shaping texture of the model based on the curvature energy functional to obtain discrete equation points on the variational curve; S310: Iteratively update and solve the discrete equation points in the Lagrangian equation to control the curvature energy of the variational curve to reach the minimum value of the curvature energy functional, and finally obtain the shaping equation solution set of the shaping mechanism for the necessary shaping texture of the model in the sub-region model, and upload the shaping equation solution set to the control terminal of the shaping mechanism.

[0026] It should be noted that the termination vector of the shaping space includes the termination position, the termination length and the termination direction. Since the necessary shaping area model is usually irregular in shape, the shaping of the necessary shaping area model by the shaping mechanism will inevitably produce uneven texture processing, and the shaping of uneven textures often requires the shaping mechanism to act on the necessary shaping area More accurate control positioning, but the existing shaping of precision hardware is programmed control, which has low control accuracy for shaping texture processing, and requires a large amount of manpower to coordinate adjustment and modification later, which reduces the degree of automation, so that there is still a certain control feasibility when completing the above requirements; in addition, based on the automation effect, the processing tool produces excessive shaping when shaping the irregular necessary shaping area, which increases the tolerance range of precision hardware and is not conducive to the assembly quality of precision hardware. This method performs variational transformation on the necessary shaping texture of the model in the sub-model area after the decomposition of the time series shaping, so that its texture variation is a curve form that is easier to calculate and process, reducing the control operation steps and improving the shaping control efficiency; then calculates the curvature energy functional of its variational curve, which is the bending energy of the variational curve, and can measure the smoothness of the variational curve, providing a solution basis for the optimal solution of each subsequent shaping control. Then, based on the curvature energy functional and the Lagrangian equation of the necessary shaping texture of the model in the sub-region model of the termination vector of the shaping space, it is discretized into a series of discrete points and then iteratively updated and solved, so as to numerically approximately solve the equations of each order of shaping control, and further improve the accuracy of each order of shaping control. Through this method, the corresponding control equation solution can be solved according to the shaping texture for the step-by-step control operation of the shaping processing mechanism, so that the shaping processing equipment can automatically control the processing tool to perform step-by-step shaping according to the shape of the necessary shaping area, reducing the automatic control deviation phenomenon in the step-by-step shaping process, and the reliability is high.

[0027] More specifically, the step S108 includes the following steps: Obtain the historical shaping path and historical shaping stress monitoring data of the necessary shaping texture of the model after the shaping processing mechanism runs the shaping equation solution set under the ideal automatic working condition strategy, and construct a standard three-dimensional model of the necessary shaping area model in the precision hardware to be shaped through PROE model design software; Taking the historical shaping path as the constraint benchmark, the standard three-dimensional model of the precision hardware to be shaped is divided into a number of grid units; within the constraint benchmark, the historical shaping stress monitoring data is applied to each grid unit through PROE model design software for simulation, and a shaping stress distribution cloud map is generated; Obtain one or more working condition posture retention areas of the shaping mechanism for the historical shaping path, divide the standard three-dimensional model of the necessary shaping area model into a plurality of sub-model area blocks according to the one or more working condition posture retention areas, preset the stress abnormality index interval, and obtain the stress index displayed in the stress distribution cloud map of each sub-model area block based on the shaping stress distribution cloud map; Only the sub-model area blocks whose stress index is within the stress anomaly index interval are extracted and marked as pending model area blocks, the stress color coding of the pending model area blocks displayed in the stress distribution cloud map is obtained, and the missing coding between the stress color coding and the ideal stress color coding is calculated; Constructing a bubble graph, fitting the ideal stress chromaticity code through the bubble graph to generate an ideal stress bubble graph, updating the chromaticity of the ideal stress bubble graph according to the lost code, and stopping updating after the chromaticity code in the ideal stress bubble graph only has the stress chromaticity code of the block in the to-be-determined model region, and obtaining the bubble volume value after stopping updating; If the bubble volume value after stopping updating is less than the preset bubble volume value, the pending model area block is calibrated as a reasonable stress model area block; if the bubble volume value after updating is greater than the preset bubble volume value, the pending model area block is calibrated as an unreasonable stress model area block, and the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area block are adjusted to eliminate the stress concentration phenomenon in the shaping process of precision hardware.

[0028] It should be noted that when processing the necessary shaping area on precision hardware, there may be stress concentration, and the stress concentration during shaping will cause the defects on the necessary shaping area to become more severe or the overall material performance of the precision hardware to deteriorate. For example, when shaping a crack on a screw, the stress concentration causes the crack to further expand. Therefore, it is necessary to eliminate the concentrated stress of the shaping equipment when processing precision hardware, so that the shaping stress is evenly distributed. This method first meshes the standard three-dimensional model of the necessary shaping area model in precision hardware, so that the subsequent simulation of the actual shaping stress is more accurate, ensuring the accurate visualization of the shaping stress distribution cloud map. And because the shaping mechanism does not continuously process some necessary shaping areas, there are posture retentions in the middle, which cause uneven stress distribution. Therefore, this method extracts the sub-model area block whose stress index is in the stress abnormality index interval in the necessary shaping area model based on the working condition posture retention area. The extracted sub-model area block is very likely to be a component of stress concentration, so it is marked as a pending model area block. Then, the color of the ideal stress bubble diagram is updated through the missing code between the stress color coding and the ideal stress color coding in the sub-area model block. Since the volume of the bubble diagram changes when its color changes, the value represented by the final bubble volume can determine whether the stress of the pending model area block is reasonable. Through this method, the stress concentration phenomenon generated during the shaping process can be rationally eliminated, the shaping scrap rate of precision hardware can be guaranteed, and the quality and efficiency of automated shaping of precision hardware can be optimized.

[0029] It should be noted that, if the bubble volume value after stopping the update is less than the preset bubble volume value, it means that the stress in the undetermined model area block does not exceed the ideal stress range, so the undetermined model area block is calibrated as a reasonable stress model area block; if the bubble volume value after the update is greater than the preset bubble volume value, it means that the stress in the undetermined model area block has exceeded the ideal stress range, so it is calibrated as an unreasonable stress model area block.

[0030] In addition, the automatic shaping and automatic detection method for precision hardware also includes the following steps: The necessary processing area of ​​the precision hardware to be shaped is detected and obtained through the automatic detection function of the shaping processing equipment, and the optimal shaping processing program for the necessary processing area of ​​the shaping processing mechanism is obtained; Control the shaping mechanism to execute the best shaping program to perform real-time processing on the necessary processing area of ​​the precision hardware to be shaped, so as to generate a number of shaping paths in which the shaping mechanism is in the necessary processing area; A backtracking algorithm is introduced to analyze a plurality of shaping processing paths of the shaping processing mechanism in a necessary processing area, a blank search tree and an expected shortest path time consumption rate are constructed, and a node is selected in the blank search tree for expansion based on the expected shortest path time consumption rate to generate a plurality of child nodes; Calculate the actual shortest path time consumption rate of each child node, perform backtracking search on the child nodes of the blank search tree according to the actual shortest path time consumption rate, and continuously update the current optimal solution until the entire blank search tree is explored and the shortest time-consuming shaping processing path is obtained; A shaping control strategy for a shaping mechanism to run the shortest time-consuming shaping path is obtained, and the shaping control strategy is uploaded to a control terminal of the shaping mechanism to optimize the shaping efficiency of precision hardware.

[0031] It should be noted that the shaping mechanism usually forms a path trend for the necessary processing areas in precision hardware, but the existing shaping technology cannot make the best and reasonable planning for the shaping path of the shaping mechanism, which greatly reduces the shaping efficiency based on the random path, and it is difficult to ensure the best shaping production quality of precision hardware. Therefore, this method can perform a blank search tree and a global screening of the shortest path time for the shaping path of the necessary processing area of ​​the precision hardware by the shaping mechanism, so as to ensure that the selected shaping path can shorten the shaping time, maximize the shaping efficiency, and improve the control accuracy of the shaping equipment.

[0032] In addition, the automatic shaping and automatic detection method for precision hardware also includes the following steps: Obtaining an image recognition log of a shaping processing device, and extracting a number of real-time defect feature vectors of the hardware to be shaped through the image recognition log; Based on the big data network, different processing attributes of each shaping tool in the shaping processing equipment are obtained, and at the same time, the established processing specifications and criteria to be followed when the different processing attributes of each shaping tool are used to shape different defect feature types; Introducing the K-means clustering algorithm, establishing K clustering centers in the K-means clustering algorithm through different processing attributes, updating and iterating the membership between each real-time defect feature vector and each clustering center based on the established processing specification criteria, repeating the above updating and iterating process until the real-time iteration frequency reaches the preset iteration frequency, and obtaining the clustering result; Based on the clustering results, the shaping tools corresponding to the processing attributes required for each real-time defect feature vector of the shaping process are obtained, which are defined as the preliminary shaping tools, and the detection order of each precision hardware to be shaped in the shaping process equipment is obtained at the same time; The switching weight value of each predetermined shaping tool is calculated according to the detection order, and a scheduling algorithm is introduced. Based on the switching weight value, the switching scheduling order of each predetermined shaping tool in the shaping processing equipment is analyzed and planned in the scheduling algorithm to generate a switching scheduling decision for the predetermined shaping tool.

[0033] It should be noted that processing attributes include dynamic processing attributes and static processing attributes. For different types of precision hardware, shaping equipment is often equipped with shaping tools with multiple processing attributes to cope with the shaping of different precision hardware; among them, some shaping tools can meet the relative dynamic shaping of specific defects, such as milling or boring, etc.; while some shaping tools can meet the relative static shaping of specific defects, such as turning, etc. The existing automatic shaping technology has mismatching and delay in switching shaping tools with different attributes, resulting in excessive or incorrect shaping of precision hardware. Therefore, this method can correspond and cluster several real-time defect feature vectors with various shaping tools with different processing attributes based on the established processing specification criteria, so as to make reasonable switching scheduling decisions on the shaping order of the shaping tools required for the detected precision hardware, thereby optimizing the reasonable degree of switching of shaping tools, reducing the excessive or incorrect shaping of precision hardware by mismatched shaping tools, and greatly improving the matching rate and compatibility of shaping tools in the process of precision hardware.

[0034] The second aspect of the present invention provides an automatic shaping and automatic detection system for precision hardware, such as Figure 4 As shown, the automatic shaping and automatic detection system includes a memory 41 and a processor 42. The memory 41 stores an automatic shaping and automatic detection method program for precision hardware. When the automatic shaping and automatic detection method program is executed by the processor 42, any one of the steps of the automatic shaping and automatic detection method is implemented.

[0035] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art who is familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. An automatic shaping and automatic detection method for precision hardware, characterized in that: The following steps are involved: S102: Obtaining a main assembly closed-loop path and a corresponding main assembly tolerance source by identifying the external contour features of the precision hardware to be shaped, accumulating the actual size variables of the main assembly tolerance source on the main assembly closed-loop path by closed-loop description, and generating an assembly closed-loop model of the precision hardware to be shaped in the assembly scene; S104: using the assembly closed-loop model to perform tolerance accumulation calculation on the accumulated error items of the main assembly tolerance source, outputting the accumulated tolerance value of the assembly closed-loop model, performing a qualified analysis based on the accumulated tolerance value and controlling the gain coefficient of the accumulated tolerance to correct and update the abnormal main assembly tolerance source that needs to be shaped, and generating an automatic detection result; S106: Obtaining the abnormal tolerance correction amount of the tolerance source to be shaped in the automatic detection result, and constraining and planning a sub-region model on multiple working condition time series that produces abnormal tolerances on the suspicious contour model that can be shaped of the tolerance source to be shaped based on the abnormal tolerance correction amount, and iteratively solving the equation of the variational curve for the necessary shaping texture of the model in the sub-region model to obtain a shaping equation solution set, so as to perform automatic shaping on the precision hardware to be shaped; S108: After obtaining the solution set of the running shaping equation, the historical shaping path and historical shaping stress monitoring data of the necessary shaping texture of the model are obtained, the grid unit simulation of the model is performed based on the historical shaping path and the historical shaping stress monitoring data, a shaping stress distribution cloud map is generated, a bubble map is constructed, and a chromaticity update analysis is performed based on the shaping stress distribution cloud map, so as to adjust the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area blocks.

2. The automatic shaping and automatic detection method for precision hardware according to claim 1, characterized in that: The step S102 specifically includes the following steps: Acquire the precision hardware to be shaped, and photograph the precision hardware to be shaped by controlling the industrial camera on the shaping processing equipment to obtain multiple frames of image data of the precision hardware to be shaped; The image contour feature information contained in the multi-frame image data is extracted by using the Hough transform method to obtain the external contour features of the precision hardware to be shaped, and at the same time, the assembly requirements of the precision hardware to be shaped are obtained; Acquire the assembly scene of the precision hardware to be shaped according to the assembly requirements, construct an assembly knowledge graph of the precision hardware to be shaped for the assembly scene based on the assembly requirements, and identify the external contour features through the assembly knowledge graph; After the recognition is completed, the main assembly closed-loop path of the precision hardware to be shaped applied to the assembly scene and the main assembly tolerance source on each main assembly closed-loop path are output, and the established processing area for the precision hardware to be shaped in the shaping processing equipment is obtained; Construct a virtual detection field for a given processing area, divide the virtual detection field into several sub-detection fields based on the main assembly tolerance source, obtain the actual size variable value of each main assembly tolerance source on the precision hardware to be shaped in the corresponding main assembly closed-loop path, and express the cumulative error term of each sub-detection field in the corresponding main assembly closed-loop path by decomposition according to the actual size variable value; A vector dimension chain equation is established, in which a cumulative closed-loop description is performed on the cumulative error terms of each sub-detection field in the corresponding main assembly closed-loop path, and an assembly closed-loop model of the precision hardware to be shaped in the assembly scene is generated.

3. The automatic shaping and automatic detection method for precision hardware according to claim 1, characterized in that: The step S104 specifically includes the following steps: Obtaining the predetermined tolerance range of the precision hardware to be shaped in each main assembly tolerance source through assembly requirements, and setting the tolerance critical extreme value of each main assembly tolerance source based on the predetermined tolerance range; Acquire actual tolerance measurement values ​​of each main assembly tolerance source, and inject the actual tolerance measurement value of each main assembly tolerance source into the assembly closed-loop model of the precision hardware to be shaped in the assembly scene; When the actual tolerance measurement value of the main assembly tolerance source reaches its corresponding tolerance critical extreme value, the cumulative error item of the corresponding sub-detection field in the assembly closed-loop model is subjected to tolerance accumulation calculation to obtain the cumulative tolerance value of the assembly closed-loop model; If the cumulative tolerance value is less than the allowable cumulative tolerance value, it means that there is no assembly tolerance defect on the precision hardware to be shaped, and the precision hardware to be shaped is calibrated as a qualified precision hardware; If the cumulative tolerance value is greater than the allowable cumulative tolerance value, it means that a main assembly tolerance source on the precision hardware to be shaped has an assembly tolerance defect, and the precision hardware to be shaped is calibrated as an unqualified precision hardware; When the precision hardware to be shaped is an unqualified precision hardware, a gain feedback algorithm is introduced, and the gain of the control cumulative tolerance is calculated in the gain feedback algorithm based on the cumulative tolerance value to obtain the control gain coefficient of the assembly closed-loop model; Correcting and updating the actual tolerance measurement value of each main assembly tolerance source in the assembly closed-loop model according to the control gain coefficient, repeating the correction and adjustment steps of the control gain coefficient until the actual tolerance measurement value of each main assembly tolerance source reaches a predetermined tolerance range, and obtaining the tolerance correction amount of each main assembly tolerance source; An abnormal tolerance correction interval is preset for each main assembly tolerance source. If the tolerance correction amount is not within the corresponding abnormal tolerance correction interval, the main assembly tolerance source is ignored; if the tolerance correction amount is within the corresponding abnormal tolerance correction interval, the main assembly tolerance source is marked as a tolerance source to be shaped, and an automatic detection result is generated.

4. The automatic shaping and automatic detection method for precision hardware according to claim 1, characterized in that: The step S106 specifically includes the following steps: The external contour features of the tolerance source to be shaped are separated from the external contour features of the precision hardware to be shaped, which are defined as suspicious contour features. The suspicious contour features are constructed by PROE model design software to obtain a suspicious contour model that can be shaped on the tolerance source to be shaped; Obtaining the tolerance correction amount of the tolerance source to be shaped in the automatic detection result, defining it as the abnormal tolerance correction amount, presetting the tolerance abnormal constraint limit according to the abnormal tolerance correction amount, and obtaining the external contour points of the suspicious contour model; Taking the external contour point as the starting planning point, based on the abnormal tolerance correction amount, constraining the planning of the area range where the abnormal tolerance is generated on the suspicious contour model, until the constrained area range meets the maximum constraint limit in the tolerance abnormal constraint limit, and finally obtaining the necessary shaping area model; Obtaining the ideal automatic working condition strategy formulated by the shaping processing equipment for the precision hardware to be shaped, setting a uniform ideal working condition timing according to the ideal automatic working condition strategy, performing ideal timing shaping decomposition on the necessary shaping area model based on the uniform ideal working condition timing, and obtaining a sub-area model corresponding to each working condition timing; For each sub-region model, the necessary shaping texture pattern distribution map of the sub-region model is drawn and the curvature energy functional after variation is calculated to establish the Lagrangian equation and solve it iteratively, and finally the shaping equation solution set of the necessary shaping texture of the model in the sub-region model is obtained by the shaping processing mechanism. Based on the shaping equation solution, the shaping processing equipment is controlled to perform automatic shaping on the precision hardware to be shaped.

5. The automatic shaping and automatic detection method for precision hardware according to claim 4, characterized in that: In the step S106, for each sub-region model, by drawing a distribution map of the necessary shaping texture pattern of the sub-region model and calculating the curvature energy functional after variation to establish the Lagrangian equation and iteratively solve it, finally a shaping equation solution set of the necessary shaping texture of the model in the sub-region model by the shaping processing mechanism is obtained, and based on the shaping equation solution, the shaping processing equipment is controlled to perform automatic shaping on the precision hardware to be shaped, which specifically includes the following steps: Obtaining the necessary shaping texture of the model of the sub-region model in the ideal time series shaping decomposition process, and obtaining the position information of the necessary shaping texture of the model on the necessary shaping region model, so as to draw a distribution map of the necessary shaping texture pattern of the model; According to the necessary shaping texture pattern distribution diagram of the model, a shaping space termination vector of the shaping processing mechanism for the sub-region model is set, and a variational curve method is introduced to convert the necessary shaping texture of the model into a variational curve to obtain a variational curve, and the smoothness of the variational curve is calculated to obtain a curvature energy functional; By using the Lagrangian function, the curvature energy functional and the shaping space termination vector are integrated into the Lagrangian function for calculation, so as to generate the Lagrangian equation of the necessary shaping texture of the model in the sub-region model; Preset a minimum value of the curvature energy functional, and discretize the derivative of the variational curve corresponding to the necessary shaping texture of the model based on the curvature energy functional to obtain discrete equation points on the variational curve; Iteratively update and solve the discrete equation points in the Lagrangian equation to control the curvature energy of the variational curve to reach the minimum value of the curvature energy functional, and finally obtain the solution set of the shaping equations for the necessary shaping texture of the model in the sub-region model by the shaping processing mechanism, and upload the solution set of the shaping equations to the control terminal of the shaping processing mechanism.

6. The automatic shaping and automatic detection method for precision hardware according to claim 1, characterized in that: The step S108 specifically includes the following steps: Obtain the historical shaping path and historical shaping stress monitoring data of the necessary shaping texture of the model after the shaping processing mechanism runs the shaping equation solution set under the ideal automatic working condition strategy, and construct a standard three-dimensional model of the necessary shaping area model in the precision hardware to be shaped through PROE model design software; Taking the historical shaping path as the constraint benchmark, the standard three-dimensional model of the precision hardware to be shaped is divided into a number of grid units; within the constraint benchmark, the historical shaping stress monitoring data is applied to each grid unit through PROE model design software for simulation, and a shaping stress distribution cloud map is generated; Obtain one or more working condition posture retention areas of the shaping mechanism for the historical shaping path, divide the standard three-dimensional model of the necessary shaping area model into a plurality of sub-model area blocks according to the one or more working condition posture retention areas, preset the stress abnormality index interval, and obtain the stress index displayed in the stress distribution cloud map of each sub-model area block based on the shaping stress distribution cloud map; Only the sub-model area blocks whose stress index is within the stress anomaly index interval are extracted and marked as pending model area blocks, the stress color coding of the pending model area blocks displayed in the stress distribution cloud map is obtained, and the missing coding between the stress color coding and the ideal stress color coding is calculated; Constructing a bubble graph, fitting the ideal stress chromaticity code through the bubble graph to generate an ideal stress bubble graph, updating the chromaticity of the ideal stress bubble graph according to the lost code, and stopping updating after the chromaticity code in the ideal stress bubble graph only has the stress chromaticity code of the block in the to-be-determined model region, and obtaining the bubble volume value after stopping updating; If the bubble volume value after stopping updating is less than the preset bubble volume value, the pending model area block is calibrated as a reasonable stress model area block; if the bubble volume value after updating is greater than the preset bubble volume value, the pending model area block is calibrated as an unreasonable stress model area block, and the shaping processing parameters of the shaping processing mechanism for the unreasonable stress model area block are adjusted to eliminate the stress concentration phenomenon in the shaping process of precision hardware.

7. An automatic shaping and automatic detection system for precision hardware, characterized in that: The automatic shaping and automatic detection system includes a memory and a processor. The memory stores an automatic shaping and automatic detection method program for precision hardware. When the automatic shaping and automatic detection method program is executed by the processor, the automatic shaping and automatic detection method steps as described in any one of claims 1-6 are implemented.

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