Quality prediction method and system in shot blasting treatment process of brake drum

Abstract

The invention discloses a quality prediction method and system in the shot blasting treatment process of a brake drum, and relates to the technical field of data processing, and the method comprises the steps: obtaining a to-be-treated area and a qualified coverage rate threshold value, obtaining a shot launching number, obtaining a shot blasting time period, and obtaining shot blasting starting time and shot blasting ending time; acquiring a real-time surface image of the surface to be subjected to shot blasting treatment of the brake drum in the shot blasting treatment process, and acquiring a real-time coverage rate according to the real-time surface image; judging whether the real-time coverage rate is smaller than a qualified coverage rate threshold, if so, obtaining a first quality prediction result according to the real-time coverage rate, and if not, obtaining a coverage rate difference value between the qualified coverage rate threshold and the real-time coverage rate; and obtaining a change weight based on the coverage rate degree change model and the coverage rate difference value, and obtaining a second quality prediction result according to the change weight, the shot peening treatment time, the real-time coverage rate and the shot peening end time. The method has the advantages of high prediction efficiency, dynamic prediction, accuracy and reliability.

Description

Technical Field

[0001] The invention relates to the technical field of data processing, and in particular to a quality prediction method and system during a brake drum shot peening process. Background Art

[0002] In the manufacturing and maintenance of the brake system, the performance and quality of the brake drum are crucial. As a key component, the surface condition of the brake drum directly affects the braking effect and durability. In order to improve the hardness and wear resistance of the brake drum surface, shot peening technology is usually used. Shot peening is to hit the surface of the workpiece with high-speed projectiles to achieve the purpose of cleaning, strengthening and changing the surface condition.

[0003] However, the traditional shot peening process of brake drums mostly relies on manual experience and fixed parameters, which leads to instability and unpredictability of the treatment quality. Specifically, the effect of shot peening is affected by many factors, such as the material, size, firing speed, shot peening time, etc. of the projectile. In addition, the initial state of the brake drum surface to be shot peened (such as roughness, degree of contamination, etc.) will also have a significant impact on the treatment results.

[0004] In the prior art, although there are some methods that attempt to monitor and adjust the shot peening process, they can usually only achieve simple on-off control or adjustment based on fixed time intervals. These methods cannot reflect the actual treatment status of the brake drum surface in real time, nor can they dynamically adjust the prediction results according to the changing status during the treatment process. For example, as the shot peening time increases, the efficiency of the shot peening in the early, middle and late stages of shot peening is completely different, and it is impossible to accurately predict the shot peening and ensure the consistency of the shot peening and achieve the expected quality goals. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a method and system for predicting the quality of a brake drum during shot peening.

[0006] A quality prediction method during the shot peening process of a brake drum, the method comprising: obtaining the to-be-treated area and qualified coverage threshold of the to-be-treated surface of the brake drum, and obtaining the number of shot launches based on the crater coverage model, the to-be-treated area and the qualified coverage threshold, and obtaining the shot peening time period according to the shot peening channel flow rate and the number of shot launches, and obtaining the shot peening start time and the shot peening end time according to the shot peening time period; obtaining a real-time surface image of the to-be-treated surface of the brake drum during the shot peening process, and obtaining the real-time coverage based on the real-time surface image; judging whether the real-time coverage is less than the qualified coverage threshold, if so, obtaining a first quality prediction result based on the real-time coverage, and if so, obtaining the coverage difference between the qualified coverage threshold and the real-time coverage; obtaining a change weight based on the coverage degree change model and the coverage difference, and obtaining a second quality prediction result based on the change weight, the shot peening time, the real-time coverage and the shot peening end time.

[0007] Optionally, the crater coverage model in obtaining the number of projectile launches based on the crater coverage model, the area to be processed and the qualified coverage rate threshold is expressed as: ;in, is the number of projectiles fired, is the area to be processed, is the qualified coverage threshold, is the average treatment range of a single projectile, The degree of blur.

[0008] Optionally, the shot peening time period is obtained according to the shot peening channel flow rate and the number of shot shots as follows: ;in, is the shot peening time period, is the shot peening channel flow rate.

[0009] Optionally, obtaining the real-time coverage rate based on the real-time surface image includes: converting the real-time surface image into a grayscale image; performing binarization processing on the grayscale image and forming a comparison image having only the crater area and the non-crater area; and obtaining the real-time coverage rate based on the crater area and the non-crater area in the comparison image.

[0010] Optionally, the coverage degree change model in obtaining the change weight based on the coverage degree change model and the coverage difference is expressed as: , ;in, is the change weight, is the qualified coverage threshold, For real-time coverage, is the correlation coefficient.

[0011] Optionally, obtaining a second quality prediction result based on the change weight, shot peening time, real-time coverage rate and shot peening end time includes: obtaining a real-time coverage speed based on the shot peening time and the real-time coverage rate; obtaining a predicted coverage rate of the brake drum at the end time of shot peening based on the real-time coverage speed and the change weight; and obtaining a second quality prediction result based on the predicted coverage rate.

[0012] Optionally, the predicted coverage rate of the brake drum at the end of shot peening is obtained according to the real-time coverage speed and the change weight and is expressed as: ;in, To predict coverage, For real-time coverage speed, For the remaining time.

[0013] A quality prediction system during the shot peening process of a brake drum is also provided, the system comprising: a first acquisition and calculation module, for acquiring the area to be treated and the qualified coverage threshold of the surface to be shot peened of the brake drum, and acquiring the number of shot shots based on the crater coverage model, the area to be treated and the qualified coverage threshold, and acquiring the shot peening time period based on the shot peening channel flow rate and the number of shot shots, and acquiring the shot peening start time and the shot peening end time based on the shot peening time period; a second acquisition and calculation module, for acquiring a real-time surface image of the surface to be shot peened of the brake drum during the shot peening process, and acquiring the real-time coverage based on the real-time surface image; a judgment and prediction module, for judging whether the real-time coverage is less than the qualified coverage threshold, if so, acquiring the first quality prediction result based on the real-time coverage, and if less than, acquiring the coverage difference between the qualified coverage threshold and the real-time coverage; a calculation and prediction module, for acquiring the change weight based on the coverage degree change model and the coverage difference, and acquiring the second quality prediction result based on the change weight, the shot peening time, the real-time coverage and the shot peening end time.

[0014] Optionally, the second acquisition and calculation module is also used to: convert the real-time surface image into a grayscale image; perform binarization processing on the grayscale image and form a comparison image containing only the crater area and the non-crater area; and obtain the real-time coverage rate based on the crater area and the non-crater area in the comparison image.

[0015] Optionally, the calculation and prediction module is also used to: obtain the real-time coverage speed according to the shot peening time and the real-time coverage rate; obtain the predicted coverage rate of the brake drum at the end time of shot peening according to the real-time coverage speed and the change weight; and obtain the second quality prediction result according to the predicted coverage rate.

[0016] The beneficial effects of the present invention are embodied in: In the quality prediction method of the entire brake drum shot peening process, by accurately measuring the area of ​​the brake drum surface to be treated and setting a reasonable qualified coverage threshold, combined with an advanced crater coverage model, the required number of shot launches and shot peening time period can be accurately calculated, thereby achieving accurate planning and time management of the shot peening process; further, during the shot peening process, high-precision image acquisition equipment and advanced image processing technology are used to monitor the coverage changes of the brake drum surface in real time and compare them with the preset qualified coverage threshold, so as to promptly discover and adjust deficiencies in the treatment process to ensure treatment quality and consistency; further, by introducing a coverage degree change model Concepts such as type and change weight further improve the ability to predict coverage changes during shot peening. It can comprehensively predict the final shot peening quality and effect based on multiple parameters such as real-time coverage, shot peening time, and shot peening end time. This dynamic adjustment and prediction strategy not only improves the accuracy and stability of shot peening, but also greatly enhances the flexibility and adaptability of the process, making the brake drum shot peening process more intelligent and automated, and effectively solves the problems of unstable and unpredictable processing quality caused by relying on manual experience and fixed parameters in the traditional shot peening process, providing a more reliable and efficient shot peening solution for the manufacture of brake drums and other parts. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0018] Figure 1 A schematic diagram of the steps of the quality prediction method during the shot peening process of the brake drum of the present invention; Figure 2 It is a schematic diagram of some steps of S2 in the quality prediction method during the shot peening process of the brake drum of the present invention; Figure 3 It is a schematic diagram of some steps S4 in the quality prediction method during the shot peening process of the brake drum of the present invention. DETAILED DESCRIPTION

[0019] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0022] like Figure 1 As shown, a method for predicting the quality of a brake drum during shot peening is provided, the method comprising: S1. Obtain the area to be treated and the qualified coverage threshold of the surface to be shot peened of the brake drum, and obtain the number of shot launches based on the crater coverage model, the area to be treated and the qualified coverage threshold, and obtain the shot peening time period according to the shot peening flow channel flow rate and the number of shot launches, and obtain the shot peening start time and shot peening end time according to the shot peening time period; S2. acquiring a real-time surface image of the surface of the brake drum to be shot peened during the shot peening process, and acquiring a real-time coverage rate according to the real-time surface image; S3, determining whether the real-time coverage is less than a qualified coverage threshold, if so, obtaining a first quality prediction result according to the real-time coverage, if so, obtaining a coverage difference between the qualified coverage threshold and the real-time coverage; S4. Obtain a change weight based on the coverage degree change model and the coverage difference, and obtain a second quality prediction result according to the change weight, shot peening time, real-time coverage and shot peening end time.

[0023] In this embodiment, it should be noted that in S1, first, the total area of ​​the brake drum surface to be shot peened is determined, which is the basis for calculating the number of shot shots and the shot peening time period; a laser rangefinder, a three-dimensional scanner or image processing technology is usually used to accurately measure the area of ​​the brake drum surface to be treated. These devices or technologies can capture the detailed dimensions of the surface to calculate the accurate area. Then, a coverage standard is set to ensure that the surface after shot peening meets the expected coverage requirements to meet the quality and performance standards; the qualified coverage threshold is usually determined according to industry standards, customer requirements or internal quality control standards. For example, in some applications, it may be necessary to achieve a coverage of 99% or more to ensure the durability and performance of the brake drum. Finally, the number of shot shots is obtained based on the crater coverage model, the area to be treated and the qualified coverage threshold; the crater coverage model is a mathematical model or algorithm used to describe the distribution and coverage of craters formed by the projectile on the brake drum surface. It takes into account factors such as the size of the shot peening flow channel and the projectile, the treatment area and the qualified coverage threshold; the crater coverage model will calculate the number of shot shots required to achieve the required coverage based on these parameters. Then, the shot peening time period is obtained according to the shot peening channel flow rate and the number of shot shots fired, and the number of shot shots fired is divided by the shot peening channel flow rate to obtain the required shot peening time period, which represents the minimum time required to complete the entire shot peening process. Finally, the shot peening start time and shot peening end time are obtained according to the shot peening time period for time management and scheduling; specifically, the start and end time of the shot peening process are determined according to the production plan and the shot peening time period, which helps ensure that the shot peening process is carried out within the predetermined time window to meet production requirements.

[0024] Assume that there is a brake drum with a surface area of ​​100 square centimeters to be shot peened, and the qualified coverage threshold is set to 99%. According to the crater coverage model, the crater coverage area formed by each shot on the brake drum surface is 1 square centimeter. The shot peening flow rate is 1000 shots per minute. The number of shots fired is calculated by the crater coverage model as 1900. Calculate the shot peening time period: 1900 shots / 1000 shots / minute = 1.9 minutes. Determine the start and end time of shot peening: Assuming that the production plan is scheduled to start shot peening at 10 am, the start time is 10:00:00 and the end time is 10:01:54.

[0025] In S2, the real-time monitoring and analysis of the surface of the brake drum to be shot peened during the shot peening process is involved. The purpose of this step is to obtain the real-time coverage of the brake drum surface so as to compare it with the preset qualified coverage threshold and adjust the shot peening process accordingly to ensure the quality and consistency of the treatment. First, in order to achieve real-time monitoring, it is necessary to use high-precision and high-speed image acquisition equipment, such as industrial cameras, high-speed cameras or specific machine vision systems. These devices should have sufficient resolution and frame rate to capture the subtle changes and dynamic processes on the brake drum surface. Then, the contour and position information of the crater (or the trace left by the shot peening) are extracted from the pre-processed image using existing image processing methods, such as edge detection, morphological processing, threshold segmentation, etc. Finally, based on the extracted crater information, the coverage of the current treated surface is calculated, which involves calculating the proportion of the crater area to the total area.

[0026] Assume that there is a brake drum surface to be shot peened, with an area of ​​100 square centimeters and a qualified coverage threshold set at 99%. In step S2, a high-precision industrial camera is used for real-time monitoring. Initial state: Before the shot peening process begins, the camera takes an initial image of the brake drum surface. Since the shot peening process has not yet been performed, the coverage is 0%. As the shot peening process proceeds, the camera continuously captures new images. In the early stage of the shot peening process, the coverage is low due to the small number of projectiles; as the number of projectiles increases, the coverage gradually increases; for example, at 1 minute into the process, the coverage may reach 80%; at 1.5 minutes into the process, the coverage may rise to 90%; and finally, before the end of the process, the coverage should reach or exceed the qualified threshold of 99%.

[0027] In S3, this real-time coverage needs to be compared with the preset qualified coverage threshold. The qualified coverage threshold is determined according to industry standards, customer requirements or internal quality control standards. It represents the coverage requirement that the surface should achieve after shot peening to ensure quality and performance; for example, in some applications, 99% or higher coverage may be required to ensure the durability and performance of the brake drum. By comparing the real-time coverage with the qualified coverage threshold, it can be determined whether the current shot peening process meets the quality requirements. If the real-time coverage is greater than or equal to the qualified coverage threshold, it means that the current shot peening process is effective enough to meet the expected coverage requirements; in this case, the first quality prediction result can be obtained based on the real-time coverage; this prediction result may include quality indicators such as absolute completion of shot peening, uniformity of shot peening, and stability of coverage distribution, which are used to evaluate the quality level of the final shot peening of the brake drum. If the real-time coverage is less than the qualified coverage threshold, it means that the current shot peening process has not yet reached the expected coverage requirement and further prediction pre-processing is required. In this case, it is necessary to obtain the coverage difference between the qualified coverage threshold and the real-time coverage. This difference is related to the subsequent shot peening coverage efficiency.

[0028] Specifically, when the coverage difference is large, it means that there are still a large number of surface areas that have not been covered by the projectiles. Therefore, the projectiles have more opportunities to cover these untreated areas in the subsequent shot peening process. Since the projectiles are randomly distributed, but in the case of a large area not covered, the probability of the projectile hitting the untreated area is relatively high, so the number of repeated coverage will be relatively small, and the coverage efficiency is high. When the coverage difference is small, it means that most of the surface area has been covered by the projectiles, and the remaining uncovered areas are relatively small and scattered. In this case, the probability of the projectile hitting the untreated area will be significantly reduced, because the projectile is more likely to hit the covered area, resulting in an increase in the number of repeated coverage and a decrease in coverage efficiency. Therefore, the prediction strategy is adjusted for different coverage differences.

[0029] In S4, first, the coverage degree variation model is used to analyze the coverage difference. The coverage degree variation model is a mathematical model that describes the coverage variation over time. It takes into account multiple factors, such as the projectile launch speed, shot peening time, the initial state of the brake drum surface, etc. These factors jointly determine the coverage variation trend. By inputting parameters such as coverage difference and time, the coverage degree variation model can output a variation weight. This weight reflects the speed and difficulty of the coverage change in the subsequent shot peening process under the current coverage difference. Finally, the second quality prediction result is obtained based on the variation weight, shot peening time, real-time coverage and shot peening end time. After obtaining the variation weight, other parameters (such as shot peening time, real-time coverage and shot peening end time) can be combined to predict the final shot peening quality and effect. One or more quality indicators, such as the final coverage distribution, can be calculated based on the input data and parameters.

[0030] Assume that there is a brake drum surface to be shot peened with an area of ​​100 square centimeters and the qualified coverage threshold is set to 99%. During the shot peening process, the changes in coverage are monitored in real time and the following data are obtained: Within 1 minute after the start of shot peening, the real-time coverage rate is 80%.

[0031] Within 1.5 minutes after the start of shot peening, the real-time coverage rate was 90%.

[0032] At a certain point before the end of the shot peening process, the real-time coverage was 98%.

[0033] At this time, the coverage difference is calculated to be 1% (99%-98%=1%), and is input into the coverage degree change model to obtain a change weight. Assuming this weight is 0.05, it means that under the current coverage difference, the coverage efficiency in the subsequent shot peening process is extremely low.

[0034] Next, the parameters such as shot peening time (assumed to be 1.8 minutes), real-time coverage (98%) and shot peening end time (assumed to be 10:02:00) are input into the quality prediction algorithm to obtain the final coverage value. The second quality prediction result is obtained based on the final coverage value. For example, it is predicted that the final coverage value exceeds the qualified threshold of 99% and meets the quality requirements.

[0035] In summary, in the entire quality prediction method of the brake drum shot peening process, by accurately measuring the area of ​​the brake drum surface to be treated and setting a reasonable qualified coverage threshold, combined with the advanced crater coverage model, the required number of shot launches and shot peening time period can be accurately calculated, thereby realizing the precise planning and time management of the shot peening process; further, in the shot peening process, high-precision image acquisition equipment and advanced image processing technology are used to monitor the coverage changes of the brake drum surface in real time, and compare them with the preset qualified coverage threshold, so as to timely discover and adjust the deficiencies in the treatment process to ensure the treatment quality and consistency; further, by introducing the coverage degree Concepts such as change model and change weight further improve the ability to predict coverage changes during shot peening. It can comprehensively predict the final shot peening quality and effect based on multiple parameters such as real-time coverage, shot peening time, and shot peening end time. This dynamic adjustment and prediction strategy not only improves the accuracy and stability of shot peening, but also greatly enhances the flexibility and adaptability of the process, making the brake drum shot peening process more intelligent and automated, and effectively solving the problems of unstable and unpredictable processing quality caused by reliance on manual experience and fixed parameters in traditional shot peening processes, providing a more reliable and efficient shot peening solution for the manufacture of brake drums and other components.

[0036] In one embodiment, the crater coverage model in obtaining the number of projectile launches based on the crater coverage model, the area to be processed and the qualified coverage rate threshold in S1 is expressed as: ;in, is the number of projectiles fired, is the area to be processed, is the qualified coverage threshold, is the average treatment range of a single projectile, The degree of blur.

[0037] In this embodiment, it should be noted that N is the number of projectiles fired, that is, the total number of projectiles that need to be fired in order to achieve a preset coverage threshold. S is the area to be treated, that is, the area of ​​the brake drum surface that needs to be shot peened. is the qualified coverage threshold, that is, the coverage requirement that the surface should achieve after shot peening to ensure quality and performance. is the average treatment range of a single shot, that is, the average radius or impact range of the crater formed by a shot on the brake drum surface, which determines the area that each shot can cover. a is the degree of blur, which is an adjustment factor used to take into account the uncertainty or randomness in the shot peening process, such as the impact of factors such as the direction of the shot, speed changes, and the unevenness of the brake drum surface on the coverage rate.

[0038] S and Directly related to the total area to be covered and the desired level of coverage, It represents the average area that a single projectile can cover, which is the basis for calculating the number of projectiles required. , reflecting the increase in coverage from 0 to The number of pellets required varies with the As the coverage increases, the number of pellets required to achieve that coverage increases nonlinearly. When the coverage approaches 100%, even a small increase requires a large number of pellets. It can reflect the trend that as the coverage rate increases, the number of required projectiles increases nonlinearly. The smaller part is more sensitive than the larger part, and can more accurately reflect the small differences in the number of shots. The fuzzy degree a is introduced to take into account various uncertainties in the shot peening process. In actual operation, even if the theoretically calculated number of shots is sufficient, the actual coverage may still be lower than expected due to various random factors. Therefore, by adjusting a, these uncertainties can be compensated to a certain extent to ensure that the final coverage meets the requirements.

[0039] In one embodiment, the shot peening time period is obtained according to the shot peening channel flow rate and the number of shot shots in S1 and is expressed as: ;in, is the shot peening time period, is the shot peening channel flow rate.

[0040] In this embodiment, it should be noted that the expression directly reflects the relationship between the shot peening time period (T), the number of shot launches (N), and the shot peening channel flow rate (Q). That is, the time required to complete all shot launches is equal to the total number of shots divided by the launch volume per unit time.

[0041] In one embodiment, obtaining the real-time coverage rate according to the real-time surface image in S2 includes: S21, converting the real-time surface image into a grayscale image; S22, performing binarization processing on the grayscale image and forming a comparison image of only the crater area and the non-crater area; S23, obtaining a real-time coverage rate according to the crater area and the non-crater area in the comparison image.

[0042] In this embodiment, it should be noted that in S21, first, a real-time surface image is read from an image acquisition device (such as a camera) or a storage medium. This image may contain multiple colors, reflecting the surface state after shot peening. Then, the read color image is converted from the original color space (such as RGB) to a grayscale color space. This process can be implemented by a variety of algorithms, among which the most commonly used is the weighted average method. The weighted average method assigns different weights to the red, green and blue channels according to the sensitivity of the human eye to different colors, and then calculates the weighted average as the grayscale value. Finally, a grayscale image is generated based on the calculated grayscale value. This image only contains brightness information and no color information, so it is simpler and more efficient to process. In summary, the purpose of converting a real-time surface image into a grayscale image is to simplify the image processing process, increase the processing speed, and reduce the interference of color information on subsequent processing steps. Grayscale images are more suitable for binarization because binarization only needs to distinguish between bright and dark areas without considering color information.

[0043] In S22, first, a suitable threshold is selected, which is used to distinguish the crater area (usually darker) from the non-crater area (usually brighter). The selection of the threshold can be achieved through the existing trial and error method, histogram analysis or automatic threshold algorithm. Next, each pixel value in the grayscale image is compared with the threshold. If the pixel value is less than or equal to the threshold, it is set to 0 (black), indicating the crater area; if the pixel value is greater than the threshold, it is set to 255 (white), indicating the non-crater area. Finally, based on the result of the binarization process, a comparison image with only the crater area and the non-crater area is generated. In this image, the black area represents the crater and the white area represents the non-crater area, forming a clear contrast.

[0044] In S23, first, the areas of the black region (crater) and the white region (non-crater) in the comparison image are calculated. Through the processing of S22, the boundary of each region can be easily identified and its area can be calculated. Then, according to the calculated crater area and the total area (i.e., the actual area obtained by dividing the total number of pixels of the comparison image by the pixel size), the real-time coverage is calculated. The coverage is equal to the crater area divided by the total area.

[0045] In one embodiment, the coverage degree change model in obtaining the change weight based on the coverage degree change model and the coverage difference in S4 is expressed as: , ;in, is the change weight, is the qualified coverage threshold, For real-time coverage, is the correlation coefficient.

[0046] In this embodiment, it should be noted that: It is a change weight, which is used to measure the degree of difference between the real-time coverage and the qualified coverage threshold, and adjust the importance of subsequent processing or decision accordingly. It is the qualified coverage threshold, that is, the minimum coverage that the shot peening process meets the qualified standard. It is the real-time coverage, that is, the actual coverage of the shot peening surface obtained by image processing and other technical means at the current moment. is the correlation coefficient, which is used to adjust the sensitivity of the change weight. The larger the correlation coefficient, the more sensitive the change weight is to the coverage difference.

[0047] Specifically, by introducing the correlation coefficient k, the sensitivity of the change weight to the coverage difference can be adjusted. Different k values ​​will lead to different weight distributions, allowing flexible adjustment according to actual needs. In general, k=1. The part reflects the relative difference between the real-time coverage and the qualified coverage threshold. This difference is standardized to between 0 and 1. This is because in predicting the subsequent coverage efficiency, due to the problem of repeated coverage, the coverage efficiency is necessarily lower than that of the covered area. It is also between 0 and 1, and is used to complete the prediction calculation and processing of the final coverage rate by predicting the subsequent coverage efficiency.

[0048] Among them, when The larger the value, the larger the gap between the current coverage rate and the qualified coverage rate threshold, that is, there are still many areas that are not covered. Since shot peening is performed randomly, a large coverage rate difference means that there are many uncovered areas, and shot peening has more space to cover these areas. Therefore, the number of repeated coverage is relatively low, and the subsequent shot peening coverage efficiency will inevitably be higher, because each shot peening has a high probability of covering a new area. According to the weight calculation formula, The closer the value of is to 1, reflecting that the reduction in coverage efficiency during the prediction process is relatively small.

[0049] Dangdang The smaller it is, the closer the current coverage rate is to the qualified coverage rate threshold, that is, most of the area has been covered. At this time, there are fewer uncovered areas, and it is easier for shot peening to repeatedly cover the covered areas during the treatment process. The subsequent shot peening coverage efficiency will inevitably be lower, because the probability of shot peening covering a new area each time is small. According to the weight calculation formula, The closer the value of is to 0, it reflects that the reduction in coverage efficiency during the prediction process is relatively large.

[0050] In summary, it helps to make more reasonable decisions during the shot peening process and improve processing efficiency and accuracy.

[0051] In one embodiment, obtaining the second quality prediction result according to the change weight, the shot peening time, the real-time coverage rate and the shot peening end time in S4 includes: S41, obtaining a real-time coverage speed according to the shot peening time and the real-time coverage rate; S42, obtaining a predicted coverage rate of the brake drum at the end time of shot peening according to the real-time coverage speed and the change weight; S43. Obtain a second quality prediction result according to the predicted coverage.

[0052] In this embodiment, it should be noted that in S41, two key data need to be collected: shot peening time and real-time coverage rate. Next, these two data are used to calculate the real-time coverage speed. The real-time coverage speed here can be simplified as real-time coverage speed = real-time coverage rate / shot peening time.

[0053] In S42, the predicted coverage rate of the brake drum at the end of shot peening is obtained according to the real-time coverage speed and the change weight. First, the remaining time from the current moment to the end of shot peening needs to be determined; next, the predicted coverage rate of the brake drum at the end of shot peening is calculated using the real-time coverage speed and the change weight. Here, the real-time coverage speed is directly reduced by the change weight (reflecting the trend of the subsequent coverage rate change). Generally, the change weight can be directly multiplied by the real-time coverage speed; then, the real-time coverage speed after the change weight processing is multiplied by the remaining time from the current moment to the end of shot peening to obtain the coverage rate increment in the remaining time; finally, the coverage rate increment is added to the real-time coverage rate to obtain the predicted coverage rate of the brake drum at the end of shot peening.

[0054] In S43, the predicted coverage is compared with the qualified coverage threshold. If the predicted coverage is greater than or equal to the qualified coverage threshold, it is considered that the brake drum will meet or exceed the quality standard at the end of shot peening; if the predicted coverage is less than the qualified coverage threshold, it is considered that the brake drum may not meet the quality standard at the end of shot peening. Finally, based on the above comparison results, the second quality prediction result can be determined. For example, if the predicted coverage meets the standard, the second quality prediction result may be positive (such as "qualified", "expected to meet the standard", etc.); if the predicted coverage does not meet the standard, the second quality prediction result may be negative (such as "unqualified", "may not meet the standard", etc.).

[0055] In one embodiment, the predicted coverage rate of the brake drum at the end time of shot peening is obtained according to the real-time coverage speed and the change weight in S42 and is expressed as: ;in, To predict coverage, For real-time coverage speed, For the remaining time.

[0056] In this embodiment, it should be noted that by introducing the change weight , which can take into account the dynamics of coverage changes during shot peening. This weight is calculated based on the difference between the real-time coverage and the qualified coverage threshold, which can reflect the gap between the current processing state and the expected state and adjust the prediction results accordingly. Real-time coverage speed It reflects the efficiency of shot peening, that is, the rate at which coverage increases per unit time. By monitoring this rate, we can understand the progress and efficiency of the current treatment, and thus more accurately predict the level of coverage that may be achieved in the remaining time. It is an important factor in the forecasting process, which can determine how much opportunity there is to improve coverage in the remaining time, thereby making a more reasonable forecast.

[0057] A quality prediction system for a brake drum during shot peening is also provided, the system comprising: The first acquisition and calculation module is used to obtain the area to be treated and the qualified coverage threshold of the surface to be shot peened of the brake drum, and obtain the number of shot launches based on the crater coverage model, the area to be treated and the qualified coverage threshold, and obtain the shot peening time period according to the shot peening flow channel flow rate and the number of shot launches, and obtain the shot peening start time and shot peening end time according to the shot peening time period; A second acquisition and calculation module is used to acquire a real-time surface image of the surface of the brake drum to be shot peened during the shot peening process, and to acquire a real-time coverage rate according to the real-time surface image; A judgment and prediction module, used to judge whether the real-time coverage is less than a qualified coverage threshold, if it is greater, obtain a first quality prediction result according to the real-time coverage, if it is less than, obtain a coverage difference between the qualified coverage threshold and the real-time coverage; The calculation and prediction module is used to obtain the change weight based on the coverage degree change model and the coverage difference, and obtain the second quality prediction result according to the change weight, the shot peening time, the real-time coverage and the shot peening end time.

[0058] In one embodiment, the second acquisition and calculation module is also used to: convert the real-time surface image into a grayscale image; perform binarization processing on the grayscale image and form a comparison image containing only the crater area and the non-crater area; and obtain the real-time coverage rate based on the crater area and the non-crater area in the comparison image.

[0059] In one embodiment, the calculation and prediction module is also used to: obtain the real-time coverage speed based on the shot peening time and the real-time coverage rate; obtain the predicted coverage rate of the brake drum at the end time of shot peening based on the real-time coverage speed and the change weight; and obtain the second quality prediction result based on the predicted coverage rate.

[0060] In this embodiment, it should be noted that regarding the quality prediction system during the above-mentioned brake drum shot peening process, the specific method of performing the operation has been described in detail in the implementation method of the quality prediction method during the brake drum shot peening process, and will not be elaborated here.

[0061] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0062] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0063] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0064] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A method for predicting the quality of a brake drum during shot peening, characterized in that: The method comprises: Obtain the area to be treated and the qualified coverage threshold of the surface to be shot peened of the brake drum, and obtain the number of shot launches based on the crater coverage model, the area to be treated and the qualified coverage threshold, and obtain the shot peening time period according to the shot peening flow channel flow rate and the number of shot launches, and obtain the shot peening start time and shot peening end time according to the shot peening time period; Acquire a real-time surface image of the surface of the brake drum to be shot peened during the shot peening process, and acquire a real-time coverage rate according to the real-time surface image; Determine whether the real-time coverage is less than a qualified coverage threshold, if so, obtain a first quality prediction result according to the real-time coverage, if so, obtain a coverage difference between the qualified coverage threshold and the real-time coverage; The change weight is obtained based on the coverage degree change model and the coverage difference, and the second quality prediction result is obtained according to the change weight, the shot peening time, the real-time coverage and the shot peening end time.

2. The quality prediction method of the brake drum during shot peening according to claim 1, characterized in that: The crater coverage model in obtaining the number of projectile launches based on the crater coverage model, the area to be processed and the qualified coverage rate threshold is expressed as: ;in, is the number of projectiles fired, is the area to be processed, is the qualified coverage threshold, is the average treatment range of a single projectile, The degree of blur.

3. The quality prediction method of the brake drum during shot peening according to claim 2, characterized in that: The shot peening time period obtained according to the shot peening channel flow rate and the number of shot shots is expressed as: ;in, is the shot peening time period, is the shot peening flow rate.

4. The quality prediction method of the brake drum during shot peening according to claim 3, characterized in that: The obtaining of real-time coverage rate according to the real-time surface image comprises: Convert the real-time surface image to a grayscale image; Binarization is performed on the grayscale image to form a comparison image of only the crater area and the non-crater area; Get the real-time coverage rate based on the crater area and non-crater area in the comparison image.

5. The quality prediction method of the brake drum during shot peening according to claim 4, characterized in that: The coverage degree change model in obtaining the change weight based on the coverage degree change model and the coverage difference is expressed as: , ;in, is the change weight, is the qualified coverage threshold, For real-time coverage, is the correlation coefficient.

6. The quality prediction method of the brake drum during shot peening according to claim 5, characterized in that: The obtaining of the second quality prediction result according to the change weight, the shot peening time, the real-time coverage rate and the shot peening end time comprises: Get the real-time coverage speed based on the shot peening time and real-time coverage rate; The predicted coverage rate of the brake drum at the end time of shot peening is obtained according to the real-time coverage speed and the change weight; A second quality prediction result is obtained according to the prediction coverage.

7. The quality prediction method of the brake drum during shot peening according to claim 6, characterized in that: The predicted coverage rate of the brake drum at the end time of shot peening obtained according to the real-time coverage speed and the change weight is expressed as: ;in, To predict coverage, For real-time coverage speed, For the remaining time.

8. A quality prediction system for a brake drum during shot peening, characterized in that: The system comprises: The first acquisition and calculation module is used to obtain the area to be treated and the qualified coverage threshold of the surface to be shot peened of the brake drum, and obtain the number of shot launches based on the crater coverage model, the area to be treated and the qualified coverage threshold, and obtain the shot peening time period according to the shot peening flow channel flow rate and the number of shot launches, and obtain the shot peening start time and shot peening end time according to the shot peening time period; A second acquisition and calculation module is used to acquire a real-time surface image of the surface of the brake drum to be shot peened during the shot peening process, and to acquire a real-time coverage rate according to the real-time surface image; A judgment and prediction module, used to judge whether the real-time coverage is less than a qualified coverage threshold, if it is greater, obtain a first quality prediction result according to the real-time coverage, if it is less than, obtain a coverage difference between the qualified coverage threshold and the real-time coverage; The calculation and prediction module is used to obtain the change weight based on the coverage degree change model and the coverage difference, and obtain the second quality prediction result according to the change weight, the shot peening time, the real-time coverage and the shot peening end time.

9. The quality prediction system for brake drum shot peening process according to claim 8, characterized in that: The second acquisition and calculation module is also used for: Convert the real-time surface image to a grayscale image; Binarization is performed on the grayscale image to form a comparison image of only the crater area and the non-crater area; Get the real-time coverage rate based on the crater area and non-crater area in the comparison image.

10. The quality prediction system during the shot peening process of the brake drum according to claim 9, characterized in that: The calculation and prediction module is also used for: Get the real-time coverage speed based on the shot peening time and real-time coverage rate; The predicted coverage rate of the brake drum at the end time of shot peening is obtained according to the real-time coverage speed and the change weight; A second quality prediction result is obtained according to the prediction coverage.

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