A high-efficiency control cabinet shell deburring method and system

Abstract

The present invention relates to the field of processing control technology, specifically to an efficient control cabinet shell deburring method and system, comprising the following steps: based on the control cabinet shell processing production line, the control cabinet shell surface is photographed by an image sensor, the processed shell surface image is captured, the image is subjected to noise reduction processing, the image contrast is adjusted, the visualization of the burrs in the image is optimized, and a burr protrusion image is generated. The present invention optimizes the processing efficiency and ensures the consistency of the processing quality by identifying and determining the position, size and shape data of the burrs, and adjusting the operating parameters including speed, pressure and direction according to the density of the burrs and the material of the control cabinet. The operating parameters are adjusted through real-time quality detection feedback to achieve the predetermined deburring standard, thereby improving the automation level and flexibility of the production line, ensuring the stability of product quality and the significant improvement of production efficiency, and enhancing the intelligence and adaptability of the production process.

Description

Technical Field

[0001] The invention relates to the technical field of processing control, and in particular to a high-efficiency control cabinet shell deburring method and system. Background Art

[0002] The field of machining control technology focuses on improving the accuracy, efficiency and automation of mechanical machining processes. This field covers the design and optimization of control systems from simple mechanical operations to complex automated production lines. The core of the technology is to use modern sensors, control algorithms, software and feedback mechanisms to accurately control the operation of machine tools, robotic arms or other machining equipment to achieve high-quality production output. In addition, machining control technology also includes real-time monitoring and data analysis to predict equipment maintenance needs and optimize production processes. It aims to improve production efficiency, reduce material waste, and enhance product consistency and quality control.

[0003] Among them, the efficient control cabinet housing deburring method mainly involves removing burrs from metal or plastic edges during the production of control cabinet housings. In order to achieve high efficiency in deburring, by using automated or semi-automated equipment to reduce manual operations, the speed and consistency of deburring are improved, the assembly fit and appearance of the final product are improved, and damage or failures caused by burrs in subsequent use are prevented, thereby optimizing the production efficiency and product quality of the control cabinet housing manufacturing industry.

[0004] Traditional methods are deficient in deburring efficiency and consistency. They rely on standardized equipment settings and cannot be adjusted according to the material or burr conditions of different control cabinet shells. This causes quality fluctuations in the processing effect due to improper equipment parameter settings, affecting the assembly fit and appearance of the product. The lack of an effective real-time quality feedback mechanism makes it difficult to adjust the production process in real time, increasing material waste and production costs. When dealing with burrs, it will lead to over-processing or under-processing, affecting the overall efficiency of the production process and product quality. Summary of the invention

[0005] The purpose of the present invention is to solve the shortcomings in the prior art and to propose a high-efficiency control cabinet housing deburring method and system.

[0006] In order to achieve the above object, the present invention adopts the following technical solution: an efficient control cabinet housing deburring method, comprising the following steps:

[0007] S1: Based on the control cabinet shell processing production line, the control cabinet shell surface is photographed by an image sensor, the processed shell surface image is captured, the image is subjected to noise reduction processing, the image contrast is adjusted, the visualization of the burrs in the image is optimized, and the burr protrusion image is generated;

[0008] S2: Based on the burr protrusion image, identify the position, size and shape data of the burr on the control cabinet housing, calculate the burr density, extract key feature information of the burr, including edge sharpness and spatial distribution, and construct a burr feature data set;

[0009] S3: Based on the burr feature data set, according to the material and burr density of the differentiated control cabinet housing, adjusting the operating parameters of the deburring equipment, the operating parameters including speed, pressure and direction, optimizing the deburring processing efficiency, and obtaining adjusted operating parameters;

[0010] S4: Implement the adjusted operating parameters, obtain the quality inspection result of the deburring process, identify the deviation between the deburring effect and the target, adjust the operating parameters of the deburring equipment, and obtain optimized control data.

[0011] The present invention is improved in that the steps of acquiring the burr protrusion image are:

[0012] S111: Based on the control cabinet shell processing production line, the surface of the control cabinet shell is photographed by an image sensor to capture the processed shell surface image to obtain an original image data set.

[0013] S112: Based on the original image data set, by formula:

[0014] ;

[0015] Perform denoising on the image to obtain a denoised image data set;

[0016] in, is the gray value set of each pixel, is the gray value set of each pixel after noise reduction processing, is the noise reduction intensity coefficient, is the total number of pixels, For the The weighting factor for each pixel, For the Gray value of neighboring pixels;

[0017] S113: Based on the denoised image data set, by formula:

[0018] ;

[0019] Adjust the image contrast to obtain an enhanced image data set;

[0020] in, is the grayscale value set of each pixel after contrast adjustment, is the contrast adjustment coefficient, used to adjust the image contrast. is the brightness adjustment constant, is the gray value set of each pixel after noise reduction processing;

[0021] S114: Based on the enhanced image data set, by formula:

[0022] ;

[0023] Calculate the gray value of each pixel of the highlighted burr, optimize the display of the burr area, and obtain a burr protrusion image;

[0024] in, is the gray value set of each pixel that highlights the burr. The glitch visualization threshold determines the benchmark at which glitches begin to be highlighted. is the adjustment factor of burr protrusion, It is the gray value set of each pixel after contrast adjustment.

[0025] The present invention is improved in that the method for calculating the burr density is:

[0026] S211: Based on the burr protrusion image, identify the position, size and shape data of the burr on the control cabinet housing, and obtain the area and perimeter of each burr;

[0027] S212: Based on the area and perimeter of the burr, the formula:

[0028] ;

[0029] Calculate the density of burrs , get density information;

[0030] in, Indicates The area of ​​the burr, is the total area of ​​the control cabinet housing, is the total number of glitches, is the balance parameter of the burr distribution, It is the density of burrs.

[0031] The present invention is improved in that the step of acquiring the burr feature data set is:

[0032] S221: Based on the burr protrusion image, extract the edge pixels of each burr, using the formula:

[0033] ;

[0034] Calculate the burr edge sharpness and obtain edge sharpness information:

[0035] in, Indicates the sharpness of the burr edge. Indicates The gradient strength of edge pixels, For the The weight of an edge pixel depends on its position in the edge. is the adjustment coefficient, is the total edge length, is the total number of pixels of the burr edge;

[0036] S222: Based on the burr protrusion image, coordinate mapping is performed on the position of each burr on the control cabinet housing, using the formula:

[0037] ;

[0038] Calculate the spatial distance between burrs and identify the distribution pattern of burrs;

[0039] in, Indicates the spatial distance between burrs, and Indicates the coordinate position of the burr, is the distance sensitivity adjustment parameter, is the baseline shift adjustment parameter, and Represents an index;

[0040] S223: Based on the spatial distance between the burrs, the sharpness of the burr edges and the density of the burrs, data is sorted to construct a burr feature data set.

[0041] The present invention is improved in that the step of obtaining the adjusted operating parameters is:

[0042] S311: Based on the burr feature data set, acquiring material data of the control cabinet housing, and identifying material characteristics of the control cabinet housing;

[0043] S312: Based on the material properties of the control cabinet housing, the formula:

[0044] ;

[0045] ;

[0046] ;

[0047] Calculate the adjusted speed, pressure and direction data to obtain the adjusted operating parameters;

[0048] in, , and They are the adjusted speed, pressure and direction, is the edge sharpness of the burr, is the spatial distance between the burrs, , and are the adjustment coefficients for speed, pressure and direction, respectively. is the sharpness adjustment factor, and is the distance adjustment factor, , and are the original velocity, pressure and direction parameters.

[0049] The present invention is improved in that the method for identifying the deviation between the deburring effect and the target is:

[0050] S411: Implementing the adjusted operating parameters, performing a deburring process task through a deburring device, collecting quality inspection data of the deburring process from the device, and obtaining deburring process data;

[0051] S412: Based on the deburring processing data, the deburring quality target is compared with the preset deburring quality target, and the formula is:

[0052] ;

[0053] Calculate the total deviation between the deburring effect and the target , get the target deviation information:

[0054] in, Indicates the first quality parameters, Indicates The target value of the quality parameter, is the total number of quality parameters, Indicates the total deviation of the deburring effect from the target.

[0055] The present invention is improved in that the step of obtaining the optimization control data is:

[0056] S421: Based on the total deviation between the deburring effect and the target , according to the deviation between the quality parameter and the target value, identify the cause of the deviation and obtain the deviation cause analysis result;

[0057] S422: Based on the deviation cause analysis result, the formula:

[0058] ;

[0059] Calculate the optimized operating parameters to obtain optimized control data;

[0060] in, is the optimized operating parameter, is the current operating parameter, is the adjustment factor, is the total deviation between the deburring effect and the target, is the attenuation factor, is the target value of the operation parameter.

[0061] An efficient control cabinet housing deburring system, the system comprising:

[0062] The image highlighting processing module is based on the control cabinet shell processing production line. It uses an image sensor to capture the processed shell surface image, perform noise reduction on the image, adjust the visualization of burrs in the image, and obtain highlighted image data.

[0063] The feature analysis module identifies the position, size and shape data of the burr on the control cabinet housing based on the highlighted image data, calculates the burr density, identifies the burr edge sharpness and spatial distribution, and obtains a burr feature data set;

[0064] The parameter adjustment module adjusts the speed, pressure and direction operation parameters of the deburring equipment based on the burr feature data set and the material and burr density of the control cabinet housing to optimize the deburring processing efficiency and obtain the adjusted processing parameters;

[0065] The quality deviation analysis module implements the adjusted processing parameters, obtains the quality inspection result of the deburring process, identifies the deviation between the deburring effect and the target, analyzes the factors causing the deviation, and obtains the deviation analysis result;

[0066] The operation optimization module adjusts the operation parameters of the deburring equipment based on the deviation analysis result, optimizes the deburring quality, and obtains optimized control data.

[0067] Compared with the prior art, the advantages and positive effects of the present invention are:

[0068] In the present invention, by identifying and determining the location, size and shape data of the burr, the operating parameters, including speed, pressure and direction, are adjusted according to the density of the burr and the material of the control cabinet, thereby optimizing the processing efficiency and ensuring the consistency of the processing quality. The optimized parameters are fed back through real-time quality detection, and the operating parameters are adjusted through real-time quality detection feedback to achieve the predetermined deburring standards, thereby improving the automation level and flexibility of the production line, reducing dependence on manual operation, ensuring the stability of product quality and a significant improvement in production efficiency, and enhancing the intelligence and adaptability of the production process. BRIEF DESCRIPTION OF THE DRAWINGS

[0069] Figure 1is a flow chart of the method of the present invention;

[0070] Figure 2 A flow chart for obtaining a burr protrusion image according to the present invention;

[0071] Figure 3 A flow chart for calculating burr density of the present invention;

[0072] Figure 4 A flow chart for obtaining a burr feature data set for the present invention;

[0073] Figure 5 A flow chart for obtaining adjusted operating parameters for the present invention;

[0074] Figure 6 A flow chart for identifying the deviation between the deburring effect and the target according to the present invention;

[0075] Figure 7 A flow chart for obtaining optimized control data for the present invention. DETAILED DESCRIPTION

[0076] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0077] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside", etc., indicating positions or positional relationships, are based on the positions or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, the meaning of "multiple" is two or more, unless otherwise clearly and specifically defined. Example

[0078] See also Figure 1 The present invention provides a technical solution: an efficient control cabinet housing deburring method, comprising the following steps:

[0079] S1: Based on the control cabinet shell processing production line, the control cabinet shell surface is photographed by an image sensor, the processed shell surface image is captured, the image is subjected to noise reduction processing, the image contrast is adjusted, the visualization of the burrs in the image is optimized, and the burr protrusion image is generated;

[0080] S2: Based on the burr protrusion image, identify the position, size and shape data of the burr on the control cabinet shell, calculate the burr density, extract the key feature information of the burr, including edge sharpness and spatial distribution, and construct a burr feature data set;

[0081] S3: Based on the burr feature data set, according to the material and burr density of the differentiated control cabinet housing, the operating parameters of the deburring equipment are adjusted, and the operating parameters include speed, pressure and direction, so as to optimize the deburring processing efficiency and obtain the adjusted operating parameters;

[0082] S4: Implement the adjusted operating parameters, obtain the quality inspection results of the deburring process, identify the deviation between the deburring effect and the target, adjust the operating parameters of the deburring equipment, and obtain optimized control data.

[0083] The burr protrusion images include images with noise reduction, contrast enhancement and images with burr brightness adjustment. The burr feature data set includes burr position information, burr size data and burr type classification. The adjusted operating parameters include adjusted grinding speed, polishing pressure and operating direction. The optimized control data includes optimized deburring path and adjusted operating parameters.

[0084] See also Figure 2 , the steps to obtain the burr protrusion image are:

[0085] S111: Based on the control cabinet shell processing production line, the surface of the control cabinet shell is photographed by an image sensor to capture the processed shell surface image to obtain an original image data set.

[0086] S112: Based on the original image data set, through the formula:

[0087] ;

[0088] Perform denoising on the image to obtain a denoised image data set;

[0089] in, is the gray value set of each pixel, is the gray value set of each pixel after noise reduction processing, is the noise reduction intensity coefficient, is the total number of pixels, For the The weighting factor for each pixel, For the Gray value of neighboring pixels;

[0090] S113: Based on the denoised image dataset, the formula:

[0091] ;

[0092] Adjust the image contrast to obtain an enhanced image data set;

[0093] in, is the grayscale value set of each pixel after contrast adjustment, is the contrast adjustment coefficient, used to adjust the image contrast. is the brightness adjustment constant, is the gray value set of each pixel after noise reduction processing;

[0094] S114: Based on the enhanced image dataset, the formula is:

[0095] ;

[0096] Calculate the gray value of each pixel of the highlighted burr, optimize the display of the burr area, and obtain a burr protrusion image;

[0097] in, is the gray value set of each pixel that highlights the burr. The glitch visualization threshold determines the benchmark at which glitches begin to be highlighted. is the adjustment factor of burr protrusion, It is the gray value set of each pixel after contrast adjustment.

[0098] formula:

[0099] ;

[0100] ;

[0101] ;

[0102] Parameter meaning and acquisition method:

[0103] Original image dataset : A set of grayscale values ​​of the control cabinet housing surface image captured directly by the image sensor. Obtained directly from the sensor, usually between 0 and 255, representing the brightness of each pixel in the image.

[0104] Noise reduction intensity coefficient : is a tuning parameter used to determine the strength of the influence of neighborhood pixels in the noise reduction process. The coefficient is adjusted through experience or experimentation to achieve the best image quality.

[0105] Weight Factor : The contribution weight of each neighborhood pixel in the denoising process. The weight factor is usually reduced according to the distance of the pixel from the center pixel and can be calculated by a Gaussian function or other similar functions.

[0106] Contrast adjustment factor and brightness adjustment constant : It is a parameter used to adjust image contrast and brightness in image processing. Responsible for adjusting the contrast of the image; Responsible for adjusting brightness.

[0107] Glitch Visualization Threshold and burr protrusion adjustment factor : It is a set threshold used to distinguish burr and non-burr areas in an image, and is often determined by grayscale histogram analysis of the image. is a factor used to adjust the degree of glitch highlighting, which can be preset by experts.

[0108] Calculation example:

[0109] Assume that the original image data set obtained by the image sensor The gray value is .

[0110] Image preprocessing:

[0111] set up , , , .

[0112] ;

[0113] ;

[0114] ;

[0115] ;

[0116] ;

[0117] ;

[0118] Image enhancement processing:

[0119] set up , .calculate ,in .

[0120]

[0121] ;

[0122] ;

[0123] Glitch Visualization and Generation:

[0124] set up , , .

[0125]

[0126] ;

[0127] ;

[0128] Through the calculation process, the specific results of each step were obtained, showing how the parameters are gradually generated into the final burr protrusion image through formula calculation and logical processing.

[0129] See also Figure 3 , the method for calculating the burr density is:

[0130] S211: Based on the burr protrusion image, identify the position, size and shape data of the burr on the control cabinet housing, and obtain the area and perimeter of each burr;

[0131] S212: Based on the area and perimeter of the burr, the formula:

[0132] ;

[0133] Calculate the density of burrs , get density information;

[0134] in, Indicates The area of ​​the burr, is the total area of ​​the control cabinet housing, is the total number of glitches, is the balance parameter of the burr distribution, It is the density of burrs.

[0135] formula:

[0136] ;

[0137] The meaning and acquisition method of parameters:

[0138] :Indicates the The area of ​​a burr can be obtained by identifying the burr boundary and calculating the area of ​​its enclosed area through image processing algorithm.

[0139] : The total area of ​​the control cabinet housing. The data can be obtained directly from the design data or by analyzing the image of the control cabinet housing and calculating its size.

[0140] : The total number of burrs is obtained by counting the number of burrs in the image through the image recognition algorithm.

[0141] : The balance parameter of burr distribution, which is an empirical value, usually obtained through historical data analysis and experimental testing. It is used to adjust the model of the effect of burr number on density.

[0142] Calculation example:

[0143] Assume the parameters are set as follows:

[0144] Total area of ​​the control cabinet housing .

[0145] Total number of glitches .

[0146] The area of ​​each burr .

[0147] Balance parameters .

[0148] Calculate the total burr area:

[0149]

[0150] Computational Intensity :

[0151]

[0152]

[0153]

[0154]

[0155]

[0156]

[0157] Calculation examples showing density using real numbers Calculation.

[0158] See also Figure 4 , the steps to obtain the burr feature data set are:

[0159] S221: Based on the burr protrusion image, extract the edge pixels of each burr, using the formula:

[0160]

[0161] Calculate the burr edge sharpness and obtain edge sharpness information:

[0162] in, Indicates the sharpness of the burr edge. Indicates The gradient strength of edge pixels, For the The weight of an edge pixel depends on its position in the edge. is the adjustment coefficient, is the total edge length, is the total number of pixels of the burr edge;

[0163] S222: Based on the burr protrusion image, coordinate mapping is performed on the position of each burr on the control cabinet housing, using the formula:

[0164]

[0165] Calculate the spatial distance between burrs and identify the distribution pattern of burrs;

[0166] in, Indicates the spatial distance between burrs, and Indicates the coordinate position of the burr, is the distance sensitivity adjustment parameter, is the baseline shift adjustment parameter, and Represents an index;

[0167] S223: Based on the spatial distance between the burrs, the sharpness of the burr edges and the density of the burrs, data is sorted to construct a burr feature data set.

[0168] formula:

[0169]

[0170]

[0171] Parameter details and how to obtain them:

[0172] : No. The gradient strength of the edge pixels is obtained by calculating the gradient strength of the image through the edge detection algorithm.

[0173] : No. The weight of edge pixels is assigned different weights according to the pixel position or edge strength. For example, the pixel weight at the edge center is higher than that at the edge end.

[0174] : Adjustment coefficient, used to adjust the effect of edge length on sharpness calculation, obtained through experimental optimization.

[0175] : The total edge length of the burr can be obtained by counting the number of pixels after edge detection.

[0176] and :glitch and The coordinate position is obtained from the image by marking the center position of the burr.

[0177] and : Adjustment parameters, Adjust distance sensitivity, Provides a baseline offset that can be set based on the data distribution characteristics.

[0178] Calculation example:

[0179] Assume that the edge of the burr image consists of 10 pixels and the gradient intensity of each pixel is They are {10, 12, 15, 20, 25, 20, 15, 12, 10, 8} respectively.

[0180] The weight of each pixel All are set to 1 to simplify the calculation.

[0181] Adjustment coefficient Set to 2.

[0182] Edge length is 10 pixels.

[0183] Sharpness calculation :

[0184]

[0185]

[0186]

[0187]

[0188] Assume glitch and The coordinates are (3, 4) and (7, 8) respectively:

[0189] Set to 1.0, Set to 0.

[0190] Spatial distribution :

[0191]

[0192]

[0193]

[0194]

[0195]

[0196] The calculation display uses image analysis data and set parameters to calculate the burr characteristics in detail, ensuring that the parameter settings are consistent with the actual situation to obtain accurate and practical results.

[0197] See also Figure 5 , the steps to obtain the adjusted operating parameters are:

[0198] S311: Based on the burr feature data set, material data of the control cabinet housing is acquired to identify material characteristics of the control cabinet housing;

[0199] S312: Based on the material properties of the control cabinet housing, the formula:

[0200]

[0201]

[0202]

[0203] Calculate the adjusted speed, pressure and direction data to obtain the adjusted operating parameters;

[0204] in, , and They are the adjusted speed, pressure and direction, is the edge sharpness of the burr, is the spatial distance between the burrs, , and are the adjustment coefficients for speed, pressure and direction, respectively. is the sharpness adjustment factor, and is the distance adjustment factor, , and are the original velocity, pressure and direction parameters.

[0205] formula:

[0206]

[0207]

[0208]

[0209] Parameter details and how to obtain them:

[0210] Original parameters , and : Default or previously set operating parameters obtained from the equipment manufacturer or equipment operating manual.

[0211] Burr characteristic parameters : Calculated by the previous steps.

[0212] Adjustment factor , , , : , and They are the adjustment coefficients of speed, pressure and direction, which can be preset by experts based on the material properties of the control cabinet shell and historical data. is the sharpness adjustment factor, and is the distance adjustment factor, which can be obtained through experiments or historical data analysis to adjust the response of operating parameters to the sensitivity of burr distribution.

[0213] Calculation example:

[0214] Assuming the original speed .

[0215] Original pressure .

[0216] Original Direction .

[0217] Burr density .

[0218] The edge sharpness of each burr .

[0219] Spatial distance between burrs .

[0220] Adjustment factor , .

[0221] Adjusted speed :

[0222]

[0223]

[0224]

[0225]

[0226] Adjusted pressure :

[0227]

[0228]

[0229]

[0230]

[0231] Adjusted direction :

[0232]

[0233]

[0234]

[0235]

[0236] The calculation steps show how to use specific numbers to determine the adjusted operating parameters, ensuring that the adjustment of the operating parameters is flexibly adjusted according to the specific conditions of the control cabinet housing and the burr density, thereby optimizing the deburring process efficiency.

[0237] See also Figure 6 , the method to identify the deviation between the deburring effect and the target is:

[0238] S411: Implement the adjusted operating parameters, perform a deburring task through the deburring equipment, collect quality inspection data of the deburring from the equipment, and obtain deburring data;

[0239] S412: Based on the deburring processing data, the deburring quality target is compared with the preset deburring quality target, and the formula is:

[0240]

[0241] Calculate the total deviation between the deburring effect and the target , get the target deviation information:

[0242] in, Indicates the first quality parameters, Indicates The target value of the quality parameter, is the total number of quality parameters, Indicates the total deviation of the deburring effect from the target.

[0243] formula:

[0244]

[0245] Parameter details and how to obtain them:

[0246] :Indicates the actual deburring process The parameters are collected by the deburring equipment's built-in inspection system, such as laser scanning or visual inspection system.

[0247] : Indicates the target value for each quality parameter, which is preset according to product design specifications and quality control standards.

[0248] : It indicates the total number of quality parameters involved in the calculation. The value is determined according to the complexity of the product and the inspection standard, reflecting the coverage of processing quality control.

[0249] Calculation example:

[0250] Assume the following situation:

[0251] Quality parameters of actual deburring process is {0.8, 0.9, 0.85}, and the corresponding target value is {1.0, 1.0, 1.0}, the total number of quality parameters .

[0252] Calculate the deviation ratio for each parameter:

[0253] For the first parameter:

[0254]

[0255] For the second parameter:

[0256]

[0257] For the third parameter:

[0258]

[0259] Calculate the squared deviation:

[0260]

[0261] Calculate the RMS deviation:

[0262]

[0263] Root mean square of deviation:

[0264]

[0265] Normalized total deviation of deburring performance from target It is about 0.15546, indicating that the overall deburring effect is about 15.5% lower than the target standard.

[0266] See also Figure 7 , the steps for obtaining optimized control data are:

[0267] S421: Based on the total deviation between the deburring effect and the target , according to the deviation between the quality parameter and the target value, identify the cause of the deviation and obtain the deviation cause analysis result;

[0268] S422: Based on the deviation cause analysis results, the formula:

[0269]

[0270] Calculate the optimized operating parameters to obtain optimized control data;

[0271] in, is the optimized operating parameter, is the current operating parameter, is the adjustment factor, is the total deviation between the deburring effect and the target, is the attenuation factor, is the target value of the operation parameter.

[0272] formula:

[0273]

[0274] Parameter details and how to obtain them:

[0275] : Current operating parameters, obtained directly from the device's control system or operation records.

[0276] : Adjustment coefficient, determined through historical data analysis or pre-set experiments, used to adjust the sensitivity of the adjustment.

[0277] : The total deviation between the deburring effect and the target, obtained in the previous steps.

[0278] : Attenuation factor, obtained through experiments and optimization, used to smooth the adjustment amplitude and prevent over-adjustment.

[0279] : The ideal target value of an operating parameter, based on the design criteria or historical best performance of the equipment.

[0280] Calculation example:

[0281] Assuming current operating parameters .

[0282] Target Operation Parameters .

[0283] Calculated Deviation (Here it refers to a 15% deviation).

[0284] Adjustment factor .

[0285] Attenuation Factor .

[0286] Calculate the impact of the difference:

[0287]

[0288] Calculate the adjustment:

[0289]

[0290] Calculate new operating parameters:

[0291]

[0292] Fine-tune operating parameters by considering the difference between target value and actual value, ensuring that the adjusted parameters are closer to the ideal state, improving the equipment's working efficiency and processing quality.

[0293] An efficient control cabinet housing deburring system, the system comprising:

[0294] The image highlighting processing module is based on the control cabinet shell processing production line. It uses an image sensor to capture the processed shell surface image, perform noise reduction on the image, adjust the visualization of burrs in the image, and obtain highlighted image data.

[0295] The feature analysis module identifies the position, size and shape data of the burr on the control cabinet shell based on the highlighted image data, calculates the burr density, identifies the burr edge sharpness and spatial distribution, and obtains the burr feature data set;

[0296] The parameter adjustment module adjusts the speed, pressure and direction operating parameters of the deburring equipment based on the burr feature data set, combined with the material and burr density of the control cabinet shell, optimizes the deburring processing efficiency, and obtains the adjusted processing parameters;

[0297] The quality deviation analysis module implements the adjusted processing parameters, obtains the quality inspection results of the deburring process, identifies the deviation between the deburring effect and the target, analyzes the factors causing the deviation, and obtains the deviation analysis results;

[0298] The operation optimization module adjusts the operation parameters of the deburring equipment based on the deviation analysis results, optimizes the deburring quality, and obtains optimized control data.

[0299] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. An efficient control cabinet housing deburring method, characterized in that: The following steps are involved: Based on the control cabinet shell processing production line, the control cabinet shell surface is photographed by an image sensor to capture the processed shell surface image, perform noise reduction on the image, adjust the image contrast, optimize the visualization of burrs in the image, and generate a burr protrusion image; Based on the burr protrusion image, identify the position, size and shape data of the burr on the control cabinet housing, calculate the burr density, extract key feature information of the burr, including edge sharpness and spatial distribution, and construct a burr feature data set; Based on the burr feature data set, according to the material and burr density of the differentiated control cabinet housing, the operating parameters of the deburring equipment are adjusted, the operating parameters including speed, pressure and direction, the processing efficiency of the deburring is optimized, and the adjusted operating parameters are obtained; Implement the adjusted operating parameters, obtain the quality inspection results of the deburring process, identify the deviation between the deburring effect and the target, adjust the operating parameters of the deburring equipment, and obtain optimized control data; The method for calculating the burr density is: Based on the burr protrusion image, identify the position, size and shape data of the burr on the control cabinet housing, and obtain the area and perimeter of each burr; Based on the area and perimeter of the burr, the formula: ； Calculate the density of burrs , get density information; in, Indicates The area of ​​the burr, is the total area of ​​the control cabinet housing, is the total number of glitches, is the balance parameter of the burr distribution, is the density of burrs; The steps for obtaining the burr feature data set are: Based on the burr protrusion image, the edge pixels of each burr are extracted by the formula: ； Calculate the burr edge sharpness and obtain edge sharpness information: in, Indicates the sharpness of the burr edge. Indicates The gradient strength of edge pixels, For the The weight of an edge pixel depends on its position in the edge. is the adjustment coefficient, is the total edge length, is the total number of pixels of the burr edge; Based on the burr protrusion image, coordinate mapping is performed on the position of each burr on the control cabinet housing, using the formula: ； Calculate the spatial distance between burrs and identify the distribution pattern of burrs; in, Indicates the spatial distance between burrs, and Indicates the coordinate position of the burr, is the distance sensitivity adjustment parameter, is the baseline shift adjustment parameter, and Represents an index; Based on the spatial distance between the burrs, the sharpness of the burr edges and the density of the burrs, data is sorted to construct a burr feature data set; The steps for obtaining the adjusted operating parameters are: Based on the burr feature data set, material data of the control cabinet housing is acquired to identify material properties of the control cabinet housing; Based on the material properties of the control cabinet housing, the formula: ； ； ； Calculate the adjusted speed, pressure and direction data to obtain the adjusted operating parameters; in, , and They are the adjusted speed, pressure and direction. is the edge sharpness of the burr, is the spatial distance between the burrs, , and are the adjustment coefficients for speed, pressure and direction, respectively. is the sharpness adjustment factor, and is the distance adjustment factor, , and are the original velocity, pressure and direction parameters; The method for identifying the deviation between the deburring effect and the target is: Implementing the adjusted operating parameters, performing a deburring process task through a deburring device, collecting quality inspection data of the deburring process from the device, and obtaining deburring process data; Based on the deburring processing data, compared with the preset deburring quality target, the formula is: ； Calculate the total deviation between the deburring effect and the target , get the target deviation information: in, Indicates the first quality parameters, Indicates The target value of the quality parameter, is the total number of quality parameters, Indicates the total deviation between the deburring effect and the target; The steps for obtaining the optimization control data are: Based on the total deviation between the deburring effect and the target , according to the deviation between the quality parameter and the target value, identify the cause of the deviation and obtain the deviation cause analysis result; Based on the analysis results of the deviation causes, the formula is: ； Calculate the optimized operating parameters to obtain optimized control data; in, is the optimized operating parameter, is the current operating parameter, is the adjustment factor, is the total deviation between the deburring effect and the target, is the attenuation factor, is the target value of the operation parameter.

2. The high-efficiency control cabinet housing deburring method according to claim 1, characterized in that: The steps of acquiring the burr protrusion image are as follows: Based on the control cabinet shell processing production line, the surface of the control cabinet shell is photographed by an image sensor to capture the processed shell surface image and obtain the original image data set; Based on the original image data set, by formula: ； Perform denoising on the image to obtain a denoised image data set; in, is the gray value set of each pixel, is the gray value set of each pixel after noise reduction processing, is the noise reduction intensity coefficient, is the total number of pixels, For the The weighting factor of pixels, For the Gray value of neighboring pixels; Based on the denoised image data set, the formula: ； Adjust the image contrast to obtain an enhanced image data set; in, is the grayscale value set of each pixel after contrast adjustment, is the contrast adjustment coefficient, used to adjust the image contrast. is the brightness adjustment constant, is the gray value set of each pixel after noise reduction processing; Based on the enhanced image dataset, the formula: ； Calculate the gray value of each pixel of the highlighted burr, optimize the display of the burr area, and obtain a burr protrusion image; in, is the gray value set of each pixel that highlights the burr. The glitch visualization threshold determines the benchmark at which glitches begin to be highlighted. is the adjustment factor of burr protrusion, It is the gray value set of each pixel after contrast adjustment.

3. High-efficiency control cabinet housing deburring system, characterized by: According to the method for deburring a housing of an efficient control cabinet according to any one of claims 1 to 2, the system comprises: The image highlighting processing module is based on the control cabinet shell processing production line. It uses an image sensor to capture the processed shell surface image, perform noise reduction on the image, adjust the visualization of burrs in the image, and obtain highlighted image data. The feature analysis module identifies the position, size and shape data of the burr on the control cabinet housing based on the highlighted image data, calculates the burr density, identifies the burr edge sharpness and spatial distribution, and obtains a burr feature data set; The parameter adjustment module adjusts the speed, pressure and direction operation parameters of the deburring equipment based on the burr feature data set and the material and burr density of the control cabinet housing to optimize the deburring processing efficiency and obtain the adjusted processing parameters; The quality deviation analysis module implements the adjusted processing parameters, obtains the quality inspection result of the deburring process, identifies the deviation between the deburring effect and the target, analyzes the factors causing the deviation, and obtains the deviation analysis result; The operation optimization module adjusts the operation parameters of the deburring equipment based on the deviation analysis result, optimizes the deburring quality, and obtains optimized control data.

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