An automatic boring control system for automobile parts

Abstract

The application discloses an automatic boring control system for automobile parts, and relates to the technical field of machining equipment control.The application solves the technical problems of insufficient automation, lack of intelligent fault diagnosis and early warning function, and unfriendly man-machine interaction interface; through a workpiece state monitoring module and an intelligent parameter adjustment module, workpiece appearance and machining data are collected and analyzed in real time, the feed speed, cutting depth and spindle speed are automatically adjusted, the machining precision is effectively improved, and the production error is reduced; a fault diagnosis and early warning module is combined with a fault mode library, potential faults such as machine tool vibration, tool wear and workpiece deformation are identified in real time, and an alarm and cutting parameters are automatically triggered, so that the equipment fault risk and production loss are reduced; a man-machine interaction feedback module reduces the operation complexity, improves the universality and ease of use of the system, enhances the adaptability of the system among different operators, and further promotes the popularization and application of the system.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of mechanical processing equipment control, in particular to an automatic boring control system for automobile parts. BACKGROUND

[0002] The background technology of the automobile parts boring control system originates from traditional mechanical processing technology. With the continuous development of the automobile industry, the precision requirement is gradually improved, and the boring processing technology emerges as the times require. Boring is a high-precision inner hole processing method, which is widely used in the manufacturing of engines, transmissions and other key automobile parts. Initially, the boring process relies on manual experience and simple mechanical equipment. With the progress of automation and digitization technology, numerical control machine tools gradually replace traditional manual operation, improving processing precision and efficiency; with the complexity and precision requirements of automobile parts improving, the intelligentization of boring control system becomes a research focus. Modern boring control system can improve processing quality and reduce errors caused by tool wear or workpiece deformation by real-time monitoring, data acquisition and feedback adjustment of processing parameters. By introducing artificial intelligence and big data analysis, the boring control system can automatically optimize the processing process according to different working conditions, further improving production efficiency and stability of parts.

[0003] Although the existing technology of automobile parts boring control system has achieved certain results in improving production efficiency and processing precision, there are still some significant technical shortcomings, as follows:

[0004] 1. Insufficient automation: Although modern numerical control machine tools have been widely used in boring processing, some systems still cannot fully automatically adjust processing parameters. For example, some systems cannot automatically adjust the feed rate, cutting depth and other parameters according to real-time monitoring of workpiece characteristics, resulting in large errors in the processing process, especially in variable production environments.

[0005] 2. Lack of intelligent fault diagnosis and warning function: Many current boring control systems lack intelligent fault detection and warning mechanisms, making it difficult to timely detect tool wear, machine tool state abnormalities or workpiece deformation. For example, when the tool is excessively worn, the cutting parameters cannot be adjusted in real time, resulting in a decrease in processing precision, and even causing the workpiece to be scrapped.

[0006] 3. The human-computer interaction interface is not friendly enough: The human-computer interaction interface of some current boring control systems is relatively complex, and operators need high technical level to effectively set up and adjust the system. This limits the universality of the system among different operators, increases the training cost and reduces the popularity of the system. SUMMARY

[0007] In view of the deficiencies of the prior art, the application provides an automatic boring control system for automobile parts, which comprises a workpiece state monitoring module, an intelligent parameter adjustment module, a fault diagnosis and early warning module, a man-machine interaction feedback module and a data feedback and evaluation module.

[0008] The workpiece state monitoring module is used for monitoring the appearance-related data of the workpiece in real time, including geometric dimensions, surface roughness and surface temperature; the appearance-related data of the workpiece is collected in real time through a sensor to generate a first data set of the workpiece.

[0009] The intelligent parameter adjustment module is used for automatically adjusting the feed speed, cutting depth and spindle speed according to the first data set of the workpiece; the workpiece surface quality index Sqi is calculated by extracting the first data set of the workpiece and is evaluated, and the feed speed, cutting depth and spindle speed that need to be adjusted are determined.

[0010] The fault diagnosis and early warning module is used for constructing a second data set of the workpiece during the machining process and simultaneously adjusting the cutting parameters in real time during the machining process; the fault diagnosis and early warning module is used for constructing a fault mode library, identifying faults, comparing faults, automatically triggering an alarm and adjusting the cutting parameters in real time.

[0011] The man-machine interaction feedback module is used for providing an interface that is simple and easy to operate for an operator and feeding back the first data set of the workpiece and the second data set of the workpiece to the operator in real time through a mobile device.

[0012] The data feedback and evaluation module is used for calculating and obtaining the cutting precision index Cyz and the fault early warning coefficient Fws according to the first data set of the workpiece and the second data set of the workpiece and evaluating them, and determining whether the tool is excessively worn or the machine tool state is abnormal.

[0013] Preferably, the workpiece state monitoring module collects the geometric dimensions of the workpiece in real time through a sensor installed on the machine tool; at the same time, a surface roughness sensor is used for measuring and recording the surface roughness of the workpiece; in addition, a temperature sensor is used for monitoring the surface temperature of the workpiece; the appearance-related data of the workpiece collected is processed and fused to generate the first data set of the workpiece.

[0014] Preferably, the intelligent parameter adjustment module comprises a workpiece surface quality calculation unit and a workpiece surface evaluation unit.

[0015] The workpiece surface quality calculation unit calculates the workpiece surface quality index Sqi by extracting the first data set of the workpiece and performing dimensionless processing and combining the following formula:

[0016]

[0017] In the formula, Sqx represents the cutting force in the first data set of the workpiece, Syd represents the surface hardness of the workpiece in the first data set of the workpiece, Swd represents the surface temperature of the workpiece in the first data set of the workpiece, Stj represents the volume of the workpiece in the first data set of the workpiece, and Scc represents the surface roughness of the workpiece in the first data set of the workpiece.

[0018] Preferably, the workpiece surface evaluation unit evaluates the workpiece surface quality index Sqi by presetting a first workpiece surface quality threshold S1 and a second workpiece surface quality threshold S2, wherein the first workpiece surface quality threshold S1 is greater than the second workpiece surface quality threshold S2, and the specific content is as follows:

[0019] If the workpiece surface quality index Sqi is greater than or equal to the first workpiece surface quality threshold S1, it indicates that the workpiece surface quality does not meet the requirements, at which time the system will automatically reduce the feed speed by 10%, reduce the cutting depth by 10%, and reduce the spindle speed by 10%;

[0020] If the second workpiece surface quality threshold S2 is less than the workpiece surface quality index Sqi and the first workpiece surface quality threshold S1, it indicates that the surface quality does not meet the requirements, but the current quality is within the acceptable range, at which time the system will automatically increase the feed speed by 10%, increase the cutting depth by 10%, and increase the spindle speed by 10%;

[0021] If the workpiece surface quality index Sqi is less than or equal to the second workpiece surface quality threshold S2, it indicates that the workpiece surface quality meets the requirements, at which time the system will maintain the current feed speed, cutting depth, and spindle speed.

[0022] Preferably, the fault diagnosis and early warning module includes a data acquisition unit and a comparison and identification unit.

[0023] The data acquisition unit is used to monitor the workpiece machining related data in real time during the workpiece machining process, wherein the mechanical changes during the cutting process are monitored through a cutting force sensor installed on the cutting tool or the workpiece, the vibration of the machine tool and the workpiece during the machining process is monitored in real time through a vibration sensor, the change of the feed speed is monitored in real time through a displacement sensor, and the rotational speed of the spindle is monitored in real time through a rotational speed sensor installed on the spindle; the real-time acquired workpiece machining related data is preprocessed, filtered and denoised, and finally a second data set of the workpiece is generated.

[0024] Preferably, the comparison identification unit is used to compare the workpiece second data set with the data in the fault mode library; by real-time acquisition and according to historical processing data, equipment maintenance records and fault cases, a fault mode library covering various processing fault modes is established, including machine tool vibration, workpiece deformation and tool wear; by analyzing the key characteristic parameters of the fault mode library, a multi-dimensional fault data model is constructed, and identification is performed through the multi-dimensional fault data model; when the similarity between the data in the workpiece second data set and the data of a certain fault mode in the fault mode library reaches 90%, an alarm is automatically triggered; at this time, the system automatically adjusts the cutting parameters according to the fault type, including feed speed, cutting depth and spindle speed.

[0025] Preferably, the man-machine interaction feedback module transmits the workpiece first data set and the workpiece second data set in the workpiece processing process to the mobile devices of the operators in real time through wireless communication technology, including smart phones and mobile terminal devices supporting network connection; the operators can view the changes of the parameters in the processing process in real time through the mobile devices, and make corresponding operations and adjustments through the interactive interface; wherein, the interface provides visual charts, data curves and specific alarm information; at the same time, the user interaction is realized through the touch screen and voice instructions, allowing the operators to adjust the processing parameters and trigger the alarm feedback through operation.

[0026] Preferably, the data feedback and evaluation module includes a cutting precision calculation unit, a fault calculation unit and a specific fault evaluation unit.

[0027] The cutting precision calculation unit calculates the cutting precision index Cyz based on the workpiece first data set and the workpiece second data set, and the specific calculation formula is as follows:

[0028]

[0029] In the formula, Tgx represents the workpiece surface stress distribution in the workpiece first data set, Flu represents the cutting force fluctuation in the workpiece second data set, Smh represents the workpiece surface microhardness in the workpiece first data set, and Tgr represents the cutting temperature gradient in the workpiece second data set.

[0030] Preferably, the fault calculation unit is used to calculate the fault warning coefficient Fws, by extracting the workpiece surface microcrack Mcr and the workpiece surface crack density Ctd in the workpiece first data set, and the spindle speed stability Rss and the cutting temperature fluctuation Tfl in the workpiece second data set, and calculating the fault warning coefficient Fws according to the following formula:

[0031]

[0032] Preferably, the specific fault assessment unit is used to preset the cutting accuracy threshold E and the fault warning threshold R, and to evaluate the cutting accuracy index Cyz and the fault warning coefficient Fws respectively, as detailed below:

[0033] If the cutting accuracy index Cyz > the cutting accuracy threshold E, it indicates that there is an abnormality in the machining process. The machining accuracy is unqualified due to the unstable cutting force. At this time, tool inspection or adjustment of machining parameters should be performed.

[0034] If the cutting accuracy index Cyz ≤ the cutting accuracy threshold E, it indicates that the machining process is normal and normal machining should continue.

[0035] If the fault warning coefficient Fws > the fault warning threshold R, it indicates that the machining process is abnormal and there is a potential fault risk caused by machine tool vibration, tool wear or overheating. At this time, the warning is triggered and the machining parameters need to be adjusted or the equipment status needs to be checked.

[0036] If the fault warning coefficient Fws ≤ the fault warning threshold R, it means that the current processing is stable and there is no risk of failure.

[0037] Ultimately, when both the cutting accuracy index Cyz and the fault warning coefficient Fws are abnormal, it indicates that the tool is over-worn; when only the fault warning coefficient Fws is abnormal, it indicates that the machine tool has experienced vibration or mechanical failure, leading to abnormal machining processes.

[0038] This invention provides an automated boring control system for automotive parts. It has the following advantages:

[0039] (1) This automated boring control system for automotive parts integrates a workpiece status monitoring module, an intelligent parameter adjustment module, a fault diagnosis and early warning module, a human-machine interaction feedback module, and a data feedback and evaluation module, achieving a high degree of automated management of the machining process and solving the problem of insufficient automation in the prior art. By collecting relevant workpiece appearance data in real time and generating a first workpiece data set, and combining relevant data from the workpiece machining process to construct a second workpiece data set, the system can automatically adjust cutting parameters such as feed rate, depth of cut, and spindle speed, thereby effectively reducing machining errors and improving machining accuracy. Especially in a variable production environment, the system can automatically respond to real-time changes, improving production efficiency and machining quality.

[0040] (2) The automobile parts automatic boring control system, the intelligent fault diagnosis and early warning module integrated by the system can efficiently identify potential fault risks such as machine tool vibration, tool wear and workpiece deformation by constructing a multi-dimensional fault mode library and comparing the workpiece second data set with the data in the fault mode library in real time. When the system detects that the fault early warning coefficient Fws exceeds the set threshold R, an alarm can be automatically triggered and the cutting parameters can be adjusted, avoiding the shortcomings that the traditional system cannot timely discover faults and make adjustments. Through this intelligent early warning mechanism, the production loss caused by excessive tool wear or abnormal machine tool state can be greatly reduced, and the reliability and production safety of the equipment can be improved;

[0041] (3) The automobile parts automatic boring control system provides a simple and easy-to-use interface for the operator through the man-machine interaction feedback module, so that the operator can efficiently operate the system without having to have a high technical level. Through wireless communication technology, the system feeds back the workpiece first data set and the second data set to the mobile device of the operator in real time, provides visual charts, data curves and alarm information, and the operator can interact and operate through the touch screen and voice instructions. This friendly interaction design reduces the complexity of system operation, reduces the training cost of operators, improves the universality and ease of use of the system, enhances the adaptability between different operators, and thus greatly improves the popularity and market application value of the system. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 The figure is a schematic diagram of the frame structure of the automobile parts automatic boring control system. DETAILED DESCRIPTION

[0043] The technical solutions in the embodiments of the present application will be described in detail below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0044] Embodiment 1

[0045] Please refer to Figure 1 The present application provides an automobile parts automatic boring control system, which comprises a workpiece state monitoring module, an intelligent parameter adjustment module, a fault diagnosis and early warning module, a man-machine interaction feedback module and a data feedback and evaluation module.

[0046] The workpiece state monitoring module is used for real-time monitoring of workpiece appearance related data, including geometric size, surface roughness and surface temperature; the workpiece appearance related data is collected by sensors in real time to generate a workpiece first data set.

[0047] The intelligent parameter adjustment module is used for automatically adjusting the feed speed, cutting depth and spindle speed according to the first data set of the workpiece; the workpiece surface quality index Sqi is calculated by extracting the first data set of the workpiece and evaluated, and the specific feed speed, cutting depth and spindle speed to be adjusted are determined;

[0048] The fault diagnosis and early warning module is used for constructing the second data set of the workpiece during the machining process, and simultaneously adjusting the cutting parameters in real time; by constructing a fault mode library, the fault is identified and compared, and the alarm is automatically triggered and the cutting parameters are adjusted in real time;

[0049] The man-machine interaction feedback module is used for providing an easy-to-operate interface for the operator, and feeding back the first data set of the workpiece and the second data set of the workpiece to the operator in real time through a mobile device;

[0050] The data feedback and evaluation module is used for calculating and evaluating the cutting precision index Cyz and the fault early warning coefficient Fws according to the first data set of the workpiece and the second data set of the workpiece, respectively, to determine whether the tool is excessively worn or the machine tool state is abnormal.

[0051] In this embodiment, through the cooperative work of each module, the machining precision and efficiency can be significantly improved; the workpiece state monitoring module ensures that the appearance data of the workpiece in the machining process is always in the latest state by real-time monitoring of the geometric size, surface roughness and surface temperature of the workpiece, avoiding the machining error caused by the change of the workpiece state; the intelligent parameter adjustment module realizes the accurate control of the machining parameters by automatically adjusting the feed speed, cutting depth and spindle speed according to the evaluation of the workpiece surface quality index Sqi, and improves the machining surface quality; the fault diagnosis and early warning module can identify potential faults such as machine tool vibration and workpiece deformation in time by comparing the workpiece machining related data with the fault mode library, automatically trigger the alarm and adjust the cutting parameters, thereby reducing the risk of equipment failure and production interruption; the man-machine interaction feedback module provides a simple and easy-to-use interface, so that the operator can view the first data set of the workpiece and the second data set of the workpiece in real time through a mobile device, and feedback and adjustment are convenient and timely, improving the operation convenience and the universality of the system; the data feedback and evaluation module can effectively determine whether the tool is excessively worn or the machine tool state is abnormal by calculating and evaluating the cutting precision index Cyz and the fault early warning coefficient Fws, thereby ensuring the stability and precision of the production process.

[0052] Embodiment 2

[0053] The workpiece state monitoring module collects the geometric size of the workpiece in real time through the sensor installed on the machine tool; at the same time, the surface roughness sensor is used for precision measurement and recording of the surface roughness of the workpiece; in addition, the temperature sensor is used for monitoring the surface temperature of the workpiece; the collected appearance related data of the workpiece is processed and fused to generate the first data set of the workpiece.

[0054] The intelligent parameter adjustment module comprises a workpiece surface quality calculation unit and a workpiece surface evaluation unit;

[0055] The workpiece surface quality calculation unit calculates the workpiece surface quality index Sqi by extracting the workpiece first data set and performing dimensionless processing, and combining the following formula:

[0056]

[0057] In the formula, Sqx represents the cutting force in the workpiece first data set, Syd represents the workpiece surface hardness in the workpiece first data set, Swd represents the workpiece surface temperature in the workpiece first data set, Stj represents the workpiece volume in the workpiece first data set, and Scc represents the workpiece surface roughness in the workpiece first data set.

[0058] The workpiece surface evaluation unit evaluates the workpiece surface quality index Sqi by presetting a first workpiece surface quality threshold S1 and a second workpiece surface quality threshold S2, wherein the first workpiece surface quality threshold S1 is greater than the second workpiece surface quality threshold S2, and the specific content is as follows:

[0059] If the workpiece surface quality index Sqi is greater than or equal to the first workpiece surface quality threshold S1, it indicates that the workpiece surface quality does not meet the requirements, at which time the system will automatically reduce the feed speed by 10%, reduce the cutting depth by 10%, and reduce the spindle speed by 10%;

[0060] If the second workpiece surface quality threshold S2 is less than the workpiece surface quality index Sqi and the workpiece surface quality index Sqi is less than the first workpiece surface quality threshold S1, it indicates that the surface quality does not meet the requirements, but the current quality is within the acceptable range, at which time the system will automatically increase the feed speed by 10%, increase the cutting depth by 10%, and increase the spindle speed by 10%;

[0061] If the workpiece surface quality index Sqi is less than or equal to the second workpiece surface quality threshold S2, it indicates that the workpiece surface quality meets the requirements, at which time the system will maintain the current feed speed, cutting depth, and spindle speed.

[0062] The fault diagnosis and early warning module comprises a data acquisition unit and a comparison and identification unit;

[0063] The data acquisition unit is used to monitor the workpiece machining related data in real time during workpiece machining, wherein the mechanical changes in the cutting process are monitored by a cutting force sensor installed on the cutting tool or the workpiece, the vibration of the machine tool and the workpiece during machining is monitored in real time by a vibration sensor, the change of the feed speed is monitored in real time by a displacement sensor, and the speed of the spindle is monitored in real time by a speed sensor installed on the spindle; the workpiece machining related data acquired in real time is preprocessed, filtered and denoised, and finally a workpiece second data set is generated.

[0064] The comparative identification unit is used for comparative analysis of the workpiece second data set and the data in the fault mode library; through real-time acquisition and according to historical processing data, equipment maintenance records and fault cases, a fault mode library covering various processing fault modes is established, including machine tool vibration, workpiece deformation and tool wear; by analyzing the key characteristic parameters of the fault mode library, a multi-dimensional fault data model is constructed, and identification is performed through the multi-dimensional fault data model; when the similarity of the data in the workpiece second data set and the data of a certain fault mode in the fault mode library reaches 90%, an automatic alarm is triggered; at this time, the system automatically adjusts the cutting parameters according to the fault type, including feed speed, cutting depth and spindle speed.

[0065] The man-machine interaction feedback module transmits the workpiece first data set and the workpiece second data set in the workpiece processing process to the mobile device of the operator in real time through wireless communication technology, including a smart phone and a mobile terminal device supporting network connection; the operator can view the parameter changes in the processing process in real time through the mobile device, and make corresponding operation and adjustment through the interactive interface; wherein the interface provides visual charts, data curves and specific alarm information; at the same time, the user interaction is realized through the touch screen and voice instruction, allowing the operator to adjust the processing parameters and trigger the alarm feedback through operation.

[0066] In this embodiment, the cooperation of each module realizes accurate control and fault early warning of the workpiece processing process, which significantly improves the production efficiency and product quality; the workpiece state monitoring module acquires the key data of the workpiece in real time, such as the geometric size, surface roughness and surface temperature, to ensure the accurate acquisition of the workpiece appearance data, and further provides basic data for subsequent intelligent adjustment and fault diagnosis; the intelligent parameter adjustment module adjusts the feed speed, cutting depth and spindle speed by calculating the workpiece surface quality index Sqi and comparing it with the preset quality threshold, to ensure that the workpiece surface quality meets the requirements, and automatically adjusts the processing parameters when the quality does not meet the standard to avoid quality problems; the fault diagnosis and early warning module can effectively identify machine tool vibration, workpiece deformation and tool wear by real-time acquisition of data such as cutting force, vibration, displacement and speed, construction of a fault mode library and comparison with workpiece processing data, real-time identification through a fault data model and automatic adjustment of parameters to reduce the risk of equipment failure; the man-machine interaction feedback module feeds back the workpiece data to the mobile device of the operator in real time through wireless communication, and the operator can view the processing data curve and alarm information through the visual interface, and adjust the parameters through the touch screen and voice instruction, to realize convenient real-time operation and adjustment, and ensure the flexibility and response speed of the system.

[0067] Embodiment 3

[0068] The data feedback and evaluation module includes a cutting accuracy calculation unit, a fault calculation unit, and a specific fault evaluation unit.

[0069] The cutting accuracy calculation unit calculates a cutting accuracy index Cyz based on the first data set of the workpiece and the second data set of the workpiece, and the specific calculation formula is as follows:

[0070]

[0071] In the formula, Tgx represents the workpiece surface stress distribution in the first data set of the workpiece, Flu represents the cutting force fluctuation in the second data set of the workpiece, Smh represents the workpiece surface microhardness in the first data set of the workpiece, and Tgr represents the cutting temperature gradient in the second data set of the workpiece.

[0072] The fault calculation unit is used to calculate a fault warning coefficient Fws, which is obtained by extracting the workpiece surface microcracks Mcr and the workpiece surface crack density Ctd in the first data set of the workpiece, and combining the spindle speed stability Rss and the cutting temperature fluctuation Tfl in the second data set of the workpiece according to the following formula:

[0073]

[0074] The specific fault evaluation unit is used to preset a cutting accuracy threshold E and a fault warning threshold R, and evaluate the cutting accuracy index Cyz and the fault warning coefficient Fws, respectively, and the specific content is as follows:

[0075] If the cutting accuracy index Cyz is greater than the cutting accuracy threshold E, it indicates that the machining process is abnormal, and the machining accuracy is unqualified due to unstable cutting force. At this time, tool inspection or adjustment of machining parameters is performed.

[0076] If the cutting accuracy index Cyz is less than or equal to the cutting accuracy threshold E, it indicates that the machining process is normal, and normal machining is continued.

[0077] If the fault warning coefficient Fws is greater than the fault warning threshold R, it indicates that the machining process is abnormal, and potential fault risk is caused by machine tool vibration, tool wear or overheating. At this time, an early warning is triggered and the machining parameters need to be adjusted or the equipment state needs to be checked.

[0078] If the fault warning coefficient Fws is less than or equal to the fault warning threshold R, it indicates that the current machining process is stable and no fault risk occurs.

[0079] Finally, when the cutting accuracy index Cyz is abnormal and the fault warning coefficient Fws is also abnormal, it indicates that the tool has been excessively worn. When only the fault warning coefficient Fws is abnormal, it indicates that the machine tool has vibration or mechanical failure, causing the machining process to be abnormal.

[0080] In the embodiment, the cutting precision and fault early warning feedback module effectively improves the machining precision and fault prediction ability through accurate calculation and real-time evaluation; the cutting precision calculation unit calculates the cutting precision index Cyz based on the workpiece surface stress distribution Tgx, the workpiece surface micro hardness Smh in the workpiece first data group and the cutting force fluctuation Flu and the cutting temperature gradient Tgr in the workpiece second data group, so as to reflect the machining precision in real time and provide basis for machining process adjustment; the fault calculation unit calculates the fault early warning coefficient Fws by extracting the workpiece surface crack density Ctd, the main shaft speed stability Rss, the workpiece surface micro crack Mcr and the cutting temperature fluctuation Tfl in the workpiece first data group and the second data group, so as to help identify potential fault risks such as machine tool vibration, tool wear and cutting temperature anomaly in advance; the specific fault evaluation unit compares the cutting precision index Cyz and the fault early warning coefficient Fws with the preset threshold E and R for real-time evaluation, so as to ensure that the machining process is adjusted in time when abnormal, if the cutting precision is unqualified, tool inspection or parameter adjustment is triggered, if the fault early warning coefficient exceeds the standard, early warning is triggered in time and measures are taken; through accurate data calculation and intelligent feedback mechanism, the module realizes comprehensive monitoring and optimization of the machining process, and maximally reduces the potential risks in the machining process and the generation of unqualified products.

[0081] Although the embodiments of the present application have been shown and described, it is to be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present application, the scope of which is defined by the appended claims and their equivalents.

Claims

1. An automotive parts automated boring control system, characterized by: The workpiece state monitoring module, the intelligent parameter adjustment module, the fault diagnosis and early warning module, the man-machine interaction feedback module and the data feedback and evaluation module are comprised. The workpiece state monitoring module is used for monitoring the workpiece appearance related data in real time, including geometric size, surface roughness and surface temperature; the workpiece appearance related data is collected by the sensor in real time to generate the workpiece first data set; The intelligent parameter adjustment module is used for automatically adjusting the feed speed, the cutting depth and the spindle speed according to the workpiece first data set; the workpiece surface quality index Sqi is calculated by extracting the workpiece first data set and evaluated, and the specific feed speed, cutting depth and spindle speed to be adjusted are determined; The fault diagnosis and early warning module is used for constructing the workpiece second data set in the machining process, and simultaneously adjusting the cutting parameters in real time; the fault mode library is constructed to identify the fault comparison, and the alarm is automatically triggered and the cutting parameters are adjusted in real time; The man-machine interaction feedback module is used for providing a simple and easy-to-operate interface for the operator, and feeding back the workpiece first data set and the workpiece second data set to the operator in real time through the mobile device; The data feedback and evaluation module is used for calculating and obtaining the cutting precision index Cyz and the fault early warning coefficient Fws according to the workpiece first data set and the workpiece second data set, and evaluating the tool excessive wear or the abnormal machine tool state.

2. The automatic boring control system for automobile parts according to claim 1, characterized in that: The workpiece state monitoring module collects the geometric size of the workpiece in real time through the sensor installed on the machine tool; at the same time, the surface roughness sensor is used to measure and record the surface roughness of the workpiece; in addition, the temperature sensor is used to monitor the surface temperature of the workpiece; the collected workpiece appearance related data is processed and fused to generate the workpiece first data set.

3. The automatic boring control system for automobile parts according to claim 1, characterized in that: The intelligent parameter adjustment module comprises a workpiece surface quality calculation unit and a workpiece surface evaluation unit; The workpiece surface quality calculation unit calculates the workpiece surface quality index Sqi by extracting the workpiece first data set and performing dimensionless processing, and combining the following formula: In the formula, Sqx represents the cutting force in the workpiece first data set, Syd represents the workpiece surface hardness in the workpiece first data set, Swd represents the workpiece surface temperature in the workpiece first data set, Stj represents the workpiece volume in the workpiece first data set, and Scc represents the workpiece surface roughness in the workpiece first data set.

4. The automatic boring control system for automobile parts according to claim 3, characterized in that: The workpiece surface evaluation unit evaluates the workpiece surface quality index Sqi by presetting the first workpiece surface quality threshold S1 and the second workpiece surface quality threshold S2, wherein the first workpiece surface quality threshold S1 is greater than the second workpiece surface quality threshold S2, and the specific content is as follows: If the workpiece surface quality index Sqi is greater than or equal to the first workpiece surface quality threshold S1, it indicates that the workpiece surface quality does not meet the requirements, at this time the system will automatically reduce the feed speed by 10%, reduce the cutting depth by 10% and reduce the spindle speed by 10%; If the second workpiece surface quality threshold S2 is less than the workpiece surface quality index Sqi and the first workpiece surface quality threshold S1 is less than the workpiece surface quality index Sqi, it indicates that the surface quality does not meet the requirements, but the current quality is within the acceptable range, at this time the system will automatically increase the feed speed by 10%, increase the cutting depth by 10% and increase the spindle speed by 10%. If the workpiece surface quality index Sqi is less than or equal to the second workpiece surface quality threshold S2, it indicates that the workpiece surface quality meets the requirements, at this time the system will keep the current feed speed, cutting depth and spindle speed.

5. The automatic boring control system for automobile parts as claimed in claim 1 wherein: The fault diagnosis and early warning module comprises a data acquisition unit and a comparison and identification unit. The data acquisition unit is used for real-time monitoring and acquiring workpiece processing related data in the workpiece processing process. The mechanical changes in the cutting process are monitored through a cutting force sensor installed on the cutting tool or the workpiece. The vibration sensor is used to monitor the vibration of the machine tool and the workpiece in the processing process in real time. The displacement sensor is used to monitor the change of the feed speed in real time. The rotational speed sensor installed on the spindle is used to monitor the rotational speed of the spindle in real time. The real-time acquired workpiece processing related data is preprocessed, filtered and denoised, and finally a second workpiece data set is generated.

6. An automatic boring control system for automotive parts as claimed in claim 5 wherein: The comparison and identification unit is used for comparing and analyzing the second workpiece data set with the data in the fault mode library. The fault mode library covering multiple processing fault modes is established by real-time acquisition and according to historical processing data, equipment maintenance records and fault cases, including machine tool vibration, workpiece deformation and tool wear. By analyzing the key characteristic parameters of the fault mode library, a multi-dimensional fault data model is constructed. When the similarity between the data in the second workpiece data set and the data of a certain fault mode in the fault mode library reaches 90%, an alarm is automatically triggered. At this time, the system will automatically adjust the cutting parameters according to the fault type, including the feed speed, cutting depth and spindle speed.

7. The automatic boring control system for automobile parts as claimed in claim 1 wherein: The man-machine interaction feedback module transmits the first workpiece data set and the second workpiece data set in the workpiece processing process to the mobile devices of the operators in real time through wireless communication technology, including smart phones and mobile terminal devices supporting network connection. The operators can view the changes of the parameters in the processing process in real time through the mobile devices and make corresponding operations and adjustments through the interactive interface. The interface provides visual charts, data curves and specific alarm information. At the same time, the user interaction is realized through the touch screen and voice instructions, allowing the operators to adjust the processing parameters and trigger the alarm feedback by operation.

8. The automatic boring control system for automotive parts of claim 1, wherein: The data feedback and evaluation module comprises a cutting accuracy calculation unit, a fault calculation unit and a specific fault evaluation unit. The cutting accuracy calculation unit calculates the cutting accuracy index Cyz based on the first workpiece data set and the second workpiece data set, and the specific calculation formula is as follows: In the formula, Tgx represents the workpiece surface stress distribution in the first workpiece data set, Flu represents the cutting force fluctuation in the second workpiece data set, Smh represents the workpiece surface microhardness in the first workpiece data set, and Tgr represents the cutting temperature gradient in the second workpiece data set.

9. The automatic boring control system for automotive parts as claimed in claim 8 wherein: The fault calculation unit is used for calculating the fault warning coefficient Fws. The workpiece surface microcracks Mcr and the workpiece surface crack density Ctd in the first workpiece data set are extracted, and the spindle speed stability Rss and the cutting temperature fluctuation Tfl in the second workpiece data set are combined to calculate the fault warning coefficient Fws according to the following formula:

10. The automatic boring control system for automotive parts as claimed in claim 9 wherein: The specific fault evaluation unit is used to preset a cutting accuracy threshold E and a fault warning threshold R, and evaluate the cutting accuracy index Cyz and the fault warning coefficient Fws, respectively, and the specific content is as follows: If the cutting accuracy index Cyz is greater than the cutting accuracy threshold E, it indicates that the machining process is abnormal, and the machining accuracy is unqualified due to the unstable cutting force, at which time the tool is checked or the machining parameter is adjusted; If the cutting accuracy index Cyz is less than or equal to the cutting accuracy threshold E, it indicates that the machining process is normal, and the normal machining is continued; If the fault warning coefficient Fws is greater than the fault warning threshold R, it indicates that the machining process is abnormal, and the potential fault risk caused by the vibration of the machine tool, the wear or overheating of the tool, at which time the warning is triggered and the machining parameter is adjusted or the equipment state is checked; If the fault warning coefficient Fws is less than or equal to the fault warning threshold R, it indicates that the current machining process is stable and no fault risk occurs; Finally, when the cutting accuracy index Cyz is abnormal and the fault warning coefficient Fws is also abnormal, it indicates that the tool has been excessively worn; When only the fault warning coefficient Fws is abnormal, it indicates that the machine tool has vibration or mechanical failure, resulting in abnormal machining process.

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