Multi-station parallel machining collaborative control system for couplings based on machine vision

Abstract

The application discloses a machine vision-based coupling multi-station parallel machining cooperative control system and relates to the field of coupling machining control. The system mainly aims at the problems that the cutting fluid supply opportunity is prone to deviation and the cutting fluid supply of adjacent or non-adjacent stations is out of synchronization during multi-station parallel machining of a coupling. Specifically, the system takes machine vision technology as a core support and runs through three key links of cutting fluid supply opportunity detection, machining surface dynamic film thickness detection and interference analysis and tool fixture micro-displacement accumulation identification. Through multi-unit cooperative linkage, the system collects key data such as temperature, dust and smoke concentration, cutting fluid splashing trajectory, film thickness deviation and micro-displacement accumulation amount in the machining process in real time, compares and analyzes the key data with preset threshold values, accurately identifies various risks in the machining process and synchronously generates targeted adjustment and compensation instructions, so as to solve the problems of poor cooperativeness, lagging risk prediction and insufficient machining precision in the multi-station parallel machining of the coupling.

Description

Technical Field

[0001] This invention relates to the field of coupling processing control technology, specifically a machine vision-based multi-station parallel processing collaborative control system for couplings. Background Technology

[0002] As a core component of mechanical transmission systems, couplings are widely used in machine tools, automobiles, aerospace, construction machinery and other fields. The machining accuracy and surface quality directly determine the stability and service life of the transmission system. As the manufacturing industry transforms towards intelligence and automation, coupling processing is gradually adopting a multi-station parallel processing mode to improve production efficiency and meet the needs of large-scale mass production. However, this mode also faces many industry pain points.

[0003] Currently, in the cutting fluid usage stage, the timing of cutting fluid supply is prone to deviation when multi-station parallel machining is carried out. The cutting fluid supply to adjacent or non-adjacent stations is not synchronized, which can easily lead to abnormal cutting surface temperature, excessive dust and smoke concentration, and in turn cause problems such as tool wear, workpiece surface scratches, and reduced machining accuracy. Moreover, traditional detection methods are mostly manual inspections, which are inefficient, have large errors, and cannot achieve real-time monitoring and risk prediction.

[0004] In the dynamic film thickness detection process, the cutting fluid splash trajectory is messy and not cleaned in time during multi-station machining, which can easily form irregular oil film or oxide film on the coupling surface, affecting the surface quality of the workpiece and the subsequent assembly accuracy. At the same time, environmental interference and cutting fluid splash interference during the machining process can cause deviations in the film thickness detection results, making it impossible to accurately judge the risk of film formation, which in turn leads to the outflow of defective products.

[0005] In the tooling and fixture operation process, when multiple stations are processing in parallel, the tooling and fixture need to continuously clamp a large number of couplings. During long-term operation, micro-displacement will accumulate, and environmental factors such as the temperature difference between day and night in the workshop and the heat generated by cutting will further aggravate the micro-displacement deviation, resulting in a decrease in the positioning accuracy of the fixture, which in turn affects the dimensional accuracy of the coupling processing. In the traditional way, the fixture calibration is mostly fixed-cycle calibration, which cannot dynamically adjust the calibration cycle according to the micro-displacement accumulation, making it difficult to achieve accurate compensation and easily causing batch processing errors.

[0006] To address the aforementioned technical shortcomings, a machine vision-based multi-station parallel machining collaborative control system for couplings is proposed. This system aims to achieve full-process collaborative management and control of multi-station parallel machining, enabling data sharing among various detection units, timely risk prediction, and meeting the requirements for high-precision and high-efficiency coupling machining. Summary of the Invention

[0007] The purpose of this invention is to solve the problems mentioned above by proposing a machine vision-based multi-station parallel processing collaborative control system for couplings.

[0008] The objective of this invention can be achieved through the following technical solution: a machine vision-based multi-station parallel machining collaborative control system for couplings, including a machining collaborative control platform, wherein the machining collaborative control platform has the following communication connections: The cutting fluid usage detection unit performs cutting fluid usage detection during the coupling machining stage, and infers whether there are any risks in the current machining process based on the cutting fluid usage detection. The dynamic film thickness detection interference analysis unit performs dynamic film thickness detection on the coupling machining and performs interference analysis based on the detection results. The micro-displacement accumulation identification unit identifies the micro-displacement accumulation characteristics of tooling fixtures on the coupling processing production line and performs targeted compensation.

[0009] Furthermore, the cutting fluid usage detection process in the cutting fluid usage detection unit is as follows: Extract the cutting execution phase of the coupling and mark it as the cutting period. Obtain the temperature deviation of the cutting surface when the cutting fluid is supplied during the cutting period when adjacent couplings execute successively. At the same time, obtain the peak deviation of dust and smoke concentration in the cutting area when non-adjacent couplings receive the cutting fluid supply successively during the cutting period.

[0010] Furthermore, if the temperature deviation of the cutting surface exceeds the temperature deviation threshold when the cutting fluid is supplied during the cutting period when adjacent couplings are executed successively, or if the peak deviation of the dust and smoke concentration in the cutting area exceeds the peak concentration deviation threshold when non-adjacent couplings are supplied with cutting fluid successively during the cutting period, a timing deviation signal is generated and sent to the machining collaborative control platform. After receiving the signal, the machining collaborative control platform re-inspects the couplings of the current batch and adjusts the current process flow. If the temperature deviation of the cutting surface does not exceed the temperature deviation threshold when adjacent couplings are executed successively during the cutting period and the peak deviation of dust and smoke concentration in the cutting area does not exceed the peak concentration deviation threshold when non-adjacent couplings receive cutting fluid successively during the cutting period, then a timing fit signal is generated and sent to the machining collaborative control platform.

[0011] Furthermore, the dynamic film thickness detection process in the dynamic film thickness detection interference analysis unit is as follows: The timing deviation period and the timing alignment period are extracted and uniformly marked as the dynamic detection period; that is, the period is marked according to the signal generation period. The splash trajectory of the cutting fluid during the dynamic detection period is obtained, and the landing point of the splash trajectory is extracted. If the distribution of the landing point of the cutting fluid during the dynamic detection period is irregular, it is marked as an irregular machining landing point; if the distribution of the landing point of the cutting fluid during the dynamic detection period is regular, it is marked as a regular machining landing point.

[0012] Furthermore, the frequency of the coupling being covered by irregular machining points during the dynamic detection period is collected. At the same time, when there are regular machining points on the surface of the coupling during the dynamic detection period, the deviation time between the stopping time of the regular machining points and the time of splash cleaning is obtained. If, during the dynamic detection period, the frequency of the coupling being covered by irregular processing landing points exceeds the coverage frequency threshold, or the duration of the deviation between the landing time of regular processing landing points and the splash cleaning time exceeds the deviation duration threshold, it is inferred that the coupling has a risk of film formation, and the current batch of couplings is marked as a high-probability film-forming entity. If, during the dynamic detection period, the frequency of the coupling being covered by irregular processing landing points does not exceed the coverage frequency threshold, and the duration of the deviation between the landing time of regular processing landing points and the splash cleaning time exceeds the deviation duration threshold, it is inferred that the coupling does not have a risk of film formation, and the current batch of couplings is marked as a low-probability film-forming entity.

[0013] Furthermore, the interference analysis process is as follows: The surface film thickness of the coupling is detected, and the deviation between the actual size and the measured size of the coupling is recorded after the surface film is detected. If the deviation exceeds the set red line value, it is inferred that the visual inspection is high-risk, a visual inspection high-risk signal is generated and sent to the machining collaborative control platform; if the deviation does not exceed the set red line value, it is inferred that the visual inspection is low-risk, a visual inspection low-risk signal is generated and sent to the machining collaborative control platform. After receiving a high-risk signal from visual inspection, the machining collaborative control platform re-acquires and re-inspects the visual data collected for the current batch of couplings. At the same time, if the current batch of couplings is a low-probability film-forming entity, the cutting fluid splash trajectory during the dynamic inspection period is re-identified and analyzed.

[0014] Furthermore, the process for recognizing the cumulative micro-displacement features of the tooling fixture in the micro-displacement accumulation recognition unit is as follows: The timing of fixture positioning calibration is recorded before the production line starts operating; and the timing is synchronized after the production line starts operating to obtain the operating period. Based on the visual image comparison of the relative displacement between the fixture reference point and the coupling positioning point, the cumulative amount of micro displacement of the tooling fixture during the operating period is obtained. At the same time, based on the increase of the operating period, combined with the processing batch and the displacement change, the continuous accumulation rate of the micro displacement is obtained. If the cumulative amount of micro-displacement of the tooling fixture exceeds the cumulative amount threshold during the operation period, or if the continuous accumulation rate of the micro-displacement exceeds the accumulation rate threshold, it is inferred that the tooling fixture of the current production line is operating abnormally, and a displacement accumulation interference signal is generated and sent to the processing collaborative control platform. If the cumulative amount of micro-displacement of the tooling fixture during the operation period does not exceed the cumulative amount threshold, and the continuous accumulation rate of the micro-displacement does not exceed the accumulation rate threshold, it is inferred that the tooling fixture of the current production line is operating normally, and a high-efficiency fixture signal is generated and sent to the processing collaborative control platform.

[0015] Furthermore, the targeted compensation process in the micro-displacement accumulation and identification unit is as follows: After receiving the displacement cumulative interference signal, the processing collaborative control platform performs production line compensation. This involves obtaining the calibration cycle based on the positioning calibration time, and setting a floating cycle using any parameter, combined with the cumulative value or cumulative rate. The calibration cycle is then combined with the floating cycle. Simultaneously, during the production line operation period, environmental data is extracted, and tooling interference is determined based on the fluctuation range of the environmental data. Targeted environmental data control is then implemented. The environmental data includes the day-night temperature difference in the workshop and the heat generated during cutting.

[0016] Compared with the prior art, the beneficial effects of the present invention are: 1. By collecting the temperature deviation of the cutting surface and the peak deviation of dust and smoke concentration of adjacent and non-adjacent workstations in real time during the cutting period, and comparing and analyzing them with preset thresholds, the system can accurately identify the deviation of the cutting fluid supply timing, realize the real-time prediction of processing risks, break through the limitations of traditional manual inspection, realize the automation and real-time monitoring of the cutting fluid supply status, promptly detect problems such as abnormal temperature and excessive dust caused by improper cutting fluid supply, avoid quality defects such as tool wear and workpiece surface scratches, and reduce the defect rate; Meanwhile, through the coordinated adjustment of the processing control platform, the current batch of couplings can be re-inspected and the process flow can be optimized to ensure the stability of subsequent processing, improve processing accuracy and efficiency. In addition, real-time monitoring of dust and smoke concentration can ensure that the processing environment meets environmental protection standards, protect the health of operators, and balance processing quality and production safety.

[0017] 2. Using machine vision technology as the core, by analyzing the cutting fluid splash trajectory, landing point distribution, and cleaning deviation time, combined with surface film thickness detection, the risk of film formation on the coupling surface can be accurately identified. This effectively solves the interference problem in dynamic film thickness detection, accurately distinguishes between regular and irregular machining landing points, quantifies the frequency of the coupling being covered by irregular landing points and the cleaning deviation of regular landing points, and realizes the graded judgment of film formation risk (high probability, low probability film formation subjects), which facilitates targeted treatment and reduces workpiece scrap due to surface film formation. Meanwhile, by assessing risks through visual inspection, dimensional errors caused by film thickness deviations can be detected in a timely manner. By re-collecting visual data and re-inspecting, the inspection accuracy can be improved, preventing defective products from flowing out. It can effectively resist environmental interference such as light and dust, improve the stability and reliability of inspection, adapt to complex working conditions of multi-station parallel processing, and provide strong protection for the surface quality of couplings.

[0018] 3. By monitoring the cumulative amount and rate of micro-displacement of the tooling fixture in real time and combining it with environmental data for targeted compensation, the problem of decreased positioning accuracy caused by the accumulation of micro-displacement of the fixture is effectively solved. Real-time identification and dynamic compensation of micro-displacement accumulation are achieved, breaking the limitations of traditional fixed-cycle calibration. The calibration cycle is dynamically adjusted according to the micro-displacement accumulation and environmental factors, which avoids machining errors caused by untimely calibration and avoids over-calibration affecting machining efficiency. Meanwhile, by analyzing the impact of environmental data (workshop day-night temperature difference, cutting heat) on micro-displacement, targeted environmental control can be implemented to further improve the positioning accuracy of the fixture, ensure the consistency of the machining dimensions of the coupling, and also promptly identify abnormalities in the operation of the tooling fixture. Through the linkage processing of the machining collaborative control platform, batch machining errors can be avoided, the service life of the fixture can be extended, production costs can be reduced, and the stability and intelligence level of the production line can be improved. Attached Figure Description

[0019] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0020] Figure 1 This is a system principle block diagram of the present invention; Figure 2 This is a flowchart of the method for using the cutting fluid detection unit in this invention. Detailed Implementation

[0021] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0023] Please see Figures 1-2 As shown, the machine vision-based multi-station parallel machining collaborative control system for couplings includes a machining collaborative control platform, which is communicatively connected to a cutting fluid usage detection unit, a dynamic film thickness detection interference analysis unit, and a micro-displacement accumulation recognition unit. The machining collaborative control platform generates a cutting fluid usage detection signal and sends it to the cutting fluid usage detection unit. After receiving the cutting fluid usage detection signal, the cutting fluid usage detection unit performs cutting fluid usage detection during the coupling machining stage, and infers whether there is any risk in the current machining process through the cutting fluid usage detection; Extract the cutting execution phase of the coupling and mark it as the cutting period. Obtain the temperature deviation of the cutting surface when the cutting fluid is supplied when adjacent couplings execute successively within the cutting period. At the same time, obtain the peak deviation of dust and smoke concentration in the cutting area when non-adjacent couplings receive the cutting fluid supply successively within the cutting period. The temperature deviation of the cutting surface when cutting fluid is supplied during the successive operation of adjacent couplings within the cutting period, and the peak deviation of dust and smoke concentration in the cutting area when non-adjacent couplings receive cutting fluid supply during the cutting period, are compared with the temperature deviation threshold and the peak concentration deviation threshold, respectively. If the temperature deviation of the cutting surface exceeds the temperature deviation threshold when adjacent couplings are executed successively during the cutting period, or if the peak deviation of dust and smoke concentration in the cutting area exceeds the peak concentration deviation threshold when non-adjacent couplings are successively supplied with cutting fluid during the cutting period, it is inferred that there is a timing deviation in the cutting fluid supply during coupling processing. A timing deviation signal is generated and sent to the machining collaborative control platform. After receiving the signal, the machining collaborative control platform re-inspects the current batch of couplings and adjusts the current process flow. If the temperature deviation of the cutting surface during the cutting fluid supply does not exceed the temperature deviation threshold when adjacent couplings are executed successively during the cutting period, and the peak deviation of dust and smoke concentration in the cutting area does not exceed the peak concentration deviation threshold when non-adjacent couplings receive the cutting fluid supply successively during the cutting period, it is inferred that there is no timing deviation in the cutting fluid supply during the coupling processing, and a timing matching signal is generated and sent to the processing collaborative control platform. Temperature deviation threshold: It is derived from the cutting temperature tolerance range of the coupling machining material (such as carbon steel and alloy steel), combined with the heat resistance limit of the cutting tool, to avoid tool wear and workpiece surface quality degradation due to excessive temperature deviation; The acquisition method combines experimental and empirical approaches. Coupling materials, cutting tools, and machining parameters (cutting speed, feed rate) consistent with actual production are selected. Multiple sets of parallel machining tests are conducted, collecting temperature data of the cutting surfaces of adjacent workstations under different cutting fluid supply conditions. Temperature deviations for each set are calculated, and a reasonable temperature deviation range is selected based on workpiece machining accuracy requirements and tool life. The upper limit of this range is taken as the temperature deviation threshold. Simultaneously, the threshold is periodically calibrated through testing as the machining material and tool type change to ensure compatibility.

[0024] Peak concentration deviation threshold: This is derived from workshop environmental protection standards and coupling surface quality requirements, to avoid excessive dust and smoke concentrations that pollute the environment and adhere to the workpiece surface, affecting processing accuracy. The method of obtaining the data involves combining national dust emission standards for machining workshops with the actual working conditions of multi-station parallel processing. The peak dust and smoke concentrations in the cutting areas of non-adjacent workstations are collected through experiments under different cutting fluid supply conditions. The peak concentration deviations of each group are calculated. Combined with workpiece surface roughness requirements and environmental protection requirements, a reasonable range of peak concentration deviations is determined, and the upper limit of this range is taken as the peak concentration deviation threshold. At the same time, the threshold is periodically tested and calibrated according to changes in cutting materials and cutting fluid types.

[0025] The processing collaborative control platform generates dynamic film thickness detection interference analysis signals and sends them to the dynamic film thickness detection interference analysis unit. After receiving the dynamic film thickness detection interference analysis signal, the dynamic film thickness detection interference analysis unit performs dynamic film thickness detection on the coupling machining and performs interference analysis based on the detection results. The timing deviation period and the timing alignment period are extracted and uniformly marked as the dynamic detection period, that is, the period is marked according to the signal generation period; The splash trajectory of the cutting fluid during the dynamic detection period is obtained, and the landing point of the splash trajectory is extracted. If the distribution of the landing point of the cutting fluid during the dynamic detection period is irregular, it is marked as an irregular machining landing point. If the distribution of the landing point of the cutting fluid during the dynamic detection period is regular, it is marked as a regular machining landing point. The frequency of the coupling being covered by irregular machining points during the dynamic detection period is collected. At the same time, when there are regular machining points on the surface of the coupling during the dynamic detection period, the deviation time between the stopping time of the regular machining points and the time of splash cleaning is obtained. The frequency of the coupling being covered by irregular machining landing points and the duration of the deviation between the landing time and the splash cleaning time of the regular machining landing points during the dynamic detection period were compared with the coverage frequency threshold and the deviation duration threshold, respectively. If, during the dynamic detection period, the frequency of the coupling being covered by irregular processing landing points exceeds the coverage frequency threshold, or the duration of the deviation between the landing time of regular processing landing points and the splash cleaning time exceeds the deviation duration threshold, it is inferred that the coupling has a risk of film formation, and the current batch of couplings is marked as a high-probability film-forming entity; if, during the dynamic detection period, the frequency of the coupling being covered by irregular processing landing points does not exceed the coverage frequency threshold, and the duration of the deviation between the landing time of regular processing landing points and the splash cleaning time exceeds the deviation duration threshold, it is inferred that the coupling does not have a risk of film formation, and the current batch of couplings is marked as a low-probability film-forming entity; The surface film thickness of the coupling is detected, and the deviation between the actual size and the measured size of the coupling is recorded after the surface film is detected. If the deviation exceeds the set red line value, it is inferred that the visual inspection is high-risk, a visual inspection high-risk signal is generated and sent to the processing collaborative control platform. If the deviation does not exceed the set red line value, it is inferred that the visual inspection is low-risk, a visual inspection low-risk signal is generated and sent to the processing collaborative control platform. Coverage frequency threshold: This is derived from the critical value of the risk of film formation on the coupling surface, to avoid the surface film being uneven and the thickness exceeding the standard due to frequent coverage of the coupling surface by irregular processing points; The method of obtaining the data is to simulate the distribution of landing points under different cutting fluid splash trajectories through experiments, collect the coverage frequency of the coupling in the irregular machining landing space, combine the surface film thickness detection results, analyze the correlation between coverage frequency and film formation risk, and determine the maximum coverage frequency that will not cause film formation risk as the coverage frequency threshold; at the same time, the threshold is updated periodically through experiments according to the workstation layout and the adjustment of cutting fluid spray parameters.

[0026] Deviation time threshold: This is derived from the critical time that the cutting fluid stays on the coupling surface, to avoid the formation of oxide film and oil film due to excessive residence time; The method of obtaining the data involves selecting commonly used cutting fluid types in actual production, combining them with the oxidation resistance of coupling materials, conducting cutting fluid residence tests, collecting surface film thickness data under different residence times, determining the maximum residence time that will not form an excessive surface film, and using it as the deviation time threshold; at the same time, the threshold is calibrated periodically according to changes in cutting fluid type and ambient humidity.

[0027] After receiving a high-risk signal from visual inspection, the processing collaborative control platform re-acquires and re-inspects the visual data collected for the current batch of couplings, such as images and photos. At the same time, if the current batch of couplings is a low-probability film-forming entity, the cutting fluid splash trajectory during the dynamic inspection period is re-identified and analyzed. After completing the visual collaborative control of the coupling, the machining collaborative control platform generates a micro-displacement accumulation recognition signal and sends it to the micro-displacement accumulation recognition unit. After receiving the micro-displacement accumulation recognition signal, the micro-displacement accumulation recognition unit identifies the tooling fixture micro-displacement accumulation characteristics of the coupling processing production line and performs targeted compensation. The timing of fixture positioning calibration is recorded before the production line starts operating; the time is synchronized after the production line starts operating to obtain the operating period. Based on the visual image comparison of the relative displacement between the fixture reference point and the coupling positioning point, the cumulative amount of micro displacement of the tooling fixture during the operating period is obtained. At the same time, based on the increase of the operating period, combined with the processing batch and the displacement change, the continuous accumulation rate of the micro displacement is obtained. The cumulative amount of micro-displacement during continuous clamping of the tooling fixture and the continuous accumulation rate of micro-displacement during the operation period are compared with the accumulation amount threshold and the accumulation rate threshold, respectively: If the cumulative micro-displacement of the tooling fixture exceeds the cumulative threshold during the operation period, or if the continuous accumulation rate of the micro-displacement exceeds the accumulation rate threshold, it is inferred that the tooling fixture is operating abnormally on the current production line. A displacement accumulation interference signal is generated and sent to the machining collaborative control platform. After receiving the signal, the machining collaborative control platform performs production line compensation. This involves obtaining the calibration cycle based on the positioning calibration time, and setting a floating cycle using either the cumulative value or the cumulative rate. The calibration cycle is then combined with the floating cycle. Simultaneously, during the production line operation period, environmental data is extracted, and tooling fixture interference is determined based on the fluctuation range of the environmental data. Targeted environmental data control is then implemented. The environmental data includes the day-night temperature difference in the workshop and the heat generated during cutting. If the cumulative amount of micro-displacement of the tooling fixture during the operation period does not exceed the cumulative amount threshold, and the continuous accumulation rate of the micro-displacement does not exceed the accumulation rate threshold, it is inferred that the tooling fixture of the current production line is operating normally, and a high-efficiency fixture signal is generated and sent to the processing collaborative control platform.

[0028] Cumulative amount threshold: This is derived from the critical deviation value of the positioning accuracy of tooling fixtures, to avoid the coupling machining dimensions from exceeding the tolerance range due to excessive cumulative micro-displacement. The method of obtaining the data is based on the machining dimensional tolerance requirements of the coupling and the positioning accuracy level of the tooling fixture. The cumulative micro-displacement of the tooling fixture is collected through experiments under different operating times. The correlation between the cumulative amount and the machining dimensional deviation is analyzed to determine the maximum cumulative micro-displacement that will not affect the machining accuracy, which is used as the cumulative amount threshold. At the same time, the threshold is updated periodically through calibration tests according to the wear condition of the fixture and the changes in the machining accuracy requirements of the coupling.

[0029] Accumulation rate threshold: This is derived from the safe rate of micro-displacement accumulation, to avoid the fixture positioning accuracy from rapidly decreasing due to excessively fast accumulation rate, which would prevent timely calibration. The acquisition method involves long-term tracking of the tooling fixture's operating status, collecting micro-displacement cumulative rate data under different processing batches and environmental conditions, and combining the fixture calibration cycle and processing efficiency requirements to determine a safe cumulative rate range. The upper limit of this range is then taken as the cumulative rate threshold. At the same time, the threshold is periodically optimized based on changes in processing batch and ambient temperature. In summary, the machining collaborative control platform integrates the cutting fluid usage detection unit, the dynamic film thickness detection interference analysis unit, and the micro-displacement accumulation identification unit to achieve accurate monitoring, risk prediction, and dynamic compensation for the entire process of multi-station parallel machining of couplings.

[0030] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A machine vision-based multi-station parallel processing collaborative control system for couplings, characterized in that, This includes a processing collaborative control platform, whose communication connections include: The cutting fluid usage detection unit performs cutting fluid usage detection during the coupling machining stage, and infers whether there are any risks in the current machining process based on the cutting fluid usage detection. The dynamic film thickness detection interference analysis unit performs dynamic film thickness detection on the coupling machining and performs interference analysis based on the detection results. The micro-displacement accumulation identification unit identifies the micro-displacement accumulation characteristics of tooling fixtures on the coupling processing production line and performs targeted compensation.

2. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 1, characterized in that, The cutting fluid usage detection process in the cutting fluid usage detection unit is as follows: Extract the cutting execution phase of the coupling and mark it as the cutting period. Obtain the temperature deviation of the cutting surface when the cutting fluid is supplied during the cutting period when adjacent couplings execute successively. At the same time, obtain the peak deviation of dust and smoke concentration in the cutting area when non-adjacent couplings receive the cutting fluid supply successively during the cutting period.

3. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 2, characterized in that, If the temperature deviation of the cutting surface exceeds the temperature deviation threshold when the cutting fluid is supplied during the cutting period when adjacent couplings are executed successively, or if the peak deviation of the dust and smoke concentration in the cutting area exceeds the peak concentration deviation threshold when non-adjacent couplings are supplied with cutting fluid successively during the cutting period, a timing deviation signal is generated and sent to the machining collaborative control platform. After receiving the signal, the machining collaborative control platform re-inspects the couplings of the current batch and adjusts the current process flow. If the temperature deviation of the cutting surface does not exceed the temperature deviation threshold when adjacent couplings are executed successively during the cutting period and the peak deviation of dust and smoke concentration in the cutting area does not exceed the peak concentration deviation threshold when non-adjacent couplings receive cutting fluid successively during the cutting period, then a timing fit signal is generated and sent to the machining collaborative control platform.

4. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 1, characterized in that, The dynamic film thickness detection process in the dynamic film thickness detection interference analysis unit is as follows: The timing deviation period and the timing alignment period are extracted and uniformly marked as the dynamic detection period; that is, the period is marked according to the signal generation period. The splash trajectory of the cutting fluid during the dynamic detection period is obtained, and the landing point of the splash trajectory is extracted. If the distribution of the landing point of the cutting fluid during the dynamic detection period is irregular, it is marked as an irregular machining landing point; if the distribution of the landing point of the cutting fluid during the dynamic detection period is regular, it is marked as a regular machining landing point.

5. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 4, characterized in that, The frequency of the coupling being covered by irregular machining points during the dynamic detection period is collected. At the same time, when there are regular machining points on the surface of the coupling during the dynamic detection period, the deviation time between the stopping time of the regular machining points and the time of splash cleaning is obtained. If, during the dynamic detection period, the frequency of the coupling being covered by irregular processing landing points exceeds the coverage frequency threshold, or the duration of the deviation between the landing time of regular processing landing points and the splash cleaning time exceeds the deviation duration threshold, it is inferred that the coupling has a risk of film formation, and the current batch of couplings is marked as a high-probability film-forming entity. If, during the dynamic detection period, the frequency of the coupling being covered by irregular processing landing points does not exceed the coverage frequency threshold, and the duration of the deviation between the landing time of regular processing landing points and the splash cleaning time exceeds the deviation duration threshold, it is inferred that the coupling does not have a risk of film formation, and the current batch of couplings is marked as a low-probability film-forming entity.

6. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 1, characterized in that, The interference analysis process is as follows: The surface film thickness of the coupling is detected, and the deviation between the actual size and the measured size of the coupling is recorded after the surface film is detected. If the deviation exceeds the set red line value, it is inferred that the visual inspection is high-risk, a visual inspection high-risk signal is generated and sent to the processing collaborative control platform. If the deviation value does not exceed the set red line value, it is inferred that the visual inspection is low-risk, a visual inspection low-risk signal is generated and sent to the processing collaborative control platform; After receiving a high-risk signal from visual inspection, the machining collaborative control platform re-acquires and re-inspects the visual data collected for the current batch of couplings. At the same time, if the current batch of couplings is a low-probability film-forming entity, the cutting fluid splash trajectory during the dynamic inspection period is re-identified and analyzed.

7. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 1, characterized in that, The process for recognizing the cumulative micro-displacement features of the tooling fixture in the micro-displacement accumulation recognition unit is as follows: The timing of fixture positioning calibration is recorded before the production line starts operating; and the timing is synchronized after the production line starts operating to obtain the operating period. Based on the visual image comparison of the relative displacement between the fixture reference point and the coupling positioning point, the cumulative amount of micro displacement of the tooling fixture during the operating period is obtained. At the same time, based on the increase of the operating period, combined with the processing batch and the displacement change, the continuous accumulation rate of the micro displacement is obtained. If the cumulative amount of micro-displacement of the tooling fixture exceeds the cumulative amount threshold during the operation period, or if the continuous accumulation rate of the micro-displacement exceeds the accumulation rate threshold, it is inferred that the tooling fixture of the current production line is operating abnormally, and a displacement accumulation interference signal is generated and sent to the processing collaborative control platform. If the cumulative amount of micro-displacement of the tooling fixture during the operation period does not exceed the cumulative amount threshold, and the continuous accumulation rate of the micro-displacement does not exceed the accumulation rate threshold, it is inferred that the tooling fixture of the current production line is operating normally, and a high-efficiency fixture signal is generated and sent to the processing collaborative control platform.

8. The machine vision-based multi-station parallel processing collaborative control system for couplings according to claim 1, characterized in that, The targeted compensation process in the micro-displacement accumulation and identification unit is as follows: After receiving the displacement cumulative interference signal, the processing collaborative control platform performs production line compensation. This involves obtaining the calibration cycle based on the positioning calibration time, and setting a floating cycle using any parameter, combined with the cumulative value or cumulative rate. The calibration cycle is then combined with the floating cycle. Simultaneously, during the production line operation period, environmental data is extracted, and tooling interference is determined based on the fluctuation range of the environmental data. Targeted environmental data control is then implemented. The environmental data includes the day-night temperature difference in the workshop and the heat generated during cutting.

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