Automatic boring control system for automobile parts

Abstract

The invention discloses an automatic boring control system for automobile parts, relates to the technical field of machining equipment control, and solves the technical problems of insufficient automation degree, lack of intelligent fault diagnosis and early warning functions and unfriendly human-computer interaction interface. Through the workpiece state monitoring module and the intelligent parameter adjusting module, workpiece appearance and machining data are collected and analyzed in real time, the feeding speed, the cutting depth and the rotating speed of a main shaft are automatically adjusted, the machining precision is effectively improved, and production errors are reduced; the fault diagnosis and early warning module is combined with a fault mode library, potential faults such as machine tool vibration, tool abrasion and workpiece deformation are recognized in real time, an alarm is automatically triggered, cutting parameters are automatically adjusted, and the equipment fault risk and the production loss are reduced; the man-machine interaction feedback module reduces the operation complexity, improves the universality and usability 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 invention relates to the technical field of machining equipment control, and particularly to an automatic boring control system for automotive parts. Background Art

[0002] The background art of the boring control system for automotive parts originated from traditional machining techniques. With the continuous development of the automotive industry, the accuracy requirements have gradually increased, and the boring machining technology has emerged as the times require. Boring is a high-precision internal hole machining method, which is widely used in the manufacturing of engines, transmissions, and other key automotive components. Initially, the boring process relied on manual experience and simple mechanical equipment. With the progress of automation and digital technologies, numerically controlled machine tools have gradually replaced traditional manual operations, improving the machining accuracy and efficiency. As the complexity and precision requirements of automotive parts have increased, the intelligence of the boring control system has become the research focus. Modern boring control systems improve the machining quality and reduce errors caused by tool wear or workpiece deformation by real-time monitoring, data collection, and feedback adjustment of machining parameters. By introducing artificial intelligence and big data analysis, the boring control system can automatically optimize the machining process according to different working conditions, further improving production efficiency and the stability of components.

[0003] Although the existing technologies of the boring control system for automotive parts have achieved certain results in improving production efficiency and machining accuracy, there are still some significant technical drawbacks, which are specifically as follows:

[0004] 1. Insufficient automation: Although modern numerically controlled machine tools have been widely used in boring machining, there are still some systems that cannot fully automate the adjustment of machining parameters. For example, some systems cannot automatically adjust parameters such as feed rate and cutting depth according to the workpiece characteristics monitored in real time, resulting in large errors during the machining process, especially in a changing production environment.

[0005] 2. Lack of intelligent fault diagnosis and warning functions: Many current boring control systems lack intelligent fault detection and warning mechanisms, making it difficult to detect problems such as tool wear, abnormal machine tool status, or workpiece deformation in a timely manner. For example, when the tool is excessively worn, the cutting parameters cannot be adjusted in real time, resulting in a decrease in machining accuracy and even workpiece scrapping.

[0006] 3. The human-machine interaction interface is not user-friendly enough: The human-machine interaction interfaces of some current boring control systems are relatively complex, and operators need a relatively high technical level to perform effective system settings and adjustments. This limits the universality of the system among different operators, increases the training cost, and reduces the popularity of the system. Summary of the Invention

[0007] In view of the deficiencies of the prior art, the present invention provides an automated boring control system for automotive parts, including a workpiece state monitoring module, an intelligent parameter adjustment module, a fault diagnosis and warning module, a human-machine interaction feedback module, and a data feedback and evaluation module;

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

[0009] The intelligent parameter adjustment module is used to automatically adjust the feed speed, cutting depth, and spindle speed according to the first workpiece data set; by extracting the first workpiece data set, calculate and evaluate the workpiece surface quality index Sqi, and at the same time judge the specific feed speed, cutting depth, and spindle speed that need to be adjusted;

[0010] The fault diagnosis and warning module is used to construct the second workpiece data set during the machining process and at the same time adjust the cutting parameters in the machining process in real time; by constructing a fault mode library, identify and compare faults, and automatically trigger an alarm and adjust the cutting parameters in real time;

[0011] The human-machine interaction feedback module is used to provide a simple and easy-to-operate interface for the operator, and feedback the first workpiece data set and the second workpiece data set to the operator in real time through a mobile device;

[0012] The data feedback and evaluation module is used to calculate and obtain the cutting accuracy index Cyz and the fault warning coefficient Fws respectively according to the first workpiece data set and the second workpiece data set, and evaluate to judge tool overwear or abnormal machine tool status.

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

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

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

[0016]

[0017] Wherein, 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 > the second workpiece surface quality threshold S2, and the specific content is as follows:

[0019] If the workpiece surface quality index Sqi ≥ 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 rate by 10%, reduce the cutting depth by 10%, and reduce the spindle speed by 10%;

[0020] If the second workpiece surface quality threshold S2 < the workpiece surface quality index Sqi < 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 this time, the system will automatically increase the feed rate by 10%, increase the cutting depth by 10%, and increase the spindle speed by 10%;

[0021] If the workpiece surface quality index Sqi ≤ the second workpiece surface quality threshold S2, it indicates that the workpiece surface quality meets the requirements. At this time, the system will maintain the current feed rate, 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 processing related data during the workpiece processing in real time. Among them, the mechanical changes during the cutting process are monitored through a cutting force sensor installed on the cutting tool or the workpiece, the vibration conditions generated by the machine tool and the workpiece during the processing are monitored in real time through a vibration sensor, the change of the feed rate is monitored in real time through a displacement sensor, and the spindle speed is monitored in real time through a speed sensor installed on the spindle; the workpiece processing related data obtained in real time is preprocessed, filtered, and denoised, and finally a second data set of the workpiece is generated.

[0024] Preferably, the comparison and recognition unit is used to compare and analyze the second workpiece data set with the data in the fault mode library; a fault mode library covering various machining fault modes is established by collecting in real time and based on historical machining 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, and recognition is performed through the multi-dimensional fault data model. 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 rate, cutting depth, and spindle speed.

[0025] Preferably, the human-machine interaction feedback module transmits the first workpiece data set and the second workpiece data set during the workpiece machining process to the operator's mobile device in real time through wireless communication technology, including smartphones and mobile terminal devices supporting network connection; the operator can view the changes in parameters during the machining process in real time through the mobile device and perform corresponding operations and adjustments through the interaction interface; among them, the interface provides visual charts, data curves, and specific alarm information; at the same time, user interaction is carried out in the way of touch screen and voice commands, allowing the operator to adjust the machining parameters and trigger alarm feedback through operations.

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

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

[0028]

[0029] 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 micro-hardness in the first workpiece data set, and Tgr represents the cutting temperature gradient in the second workpiece data set.

[0030] Preferably, the fault calculation unit is used to calculate the fault warning coefficient Fws. By extracting the workpiece surface micro-cracks Mcr and workpiece surface crack density Ctd in the first workpiece data set, and the spindle speed stability Rss and cutting temperature fluctuation Tfl in the second workpiece data set, the fault warning coefficient Fws is calculated and obtained by combining 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 respectively evaluate the cutting accuracy index Cyz and the fault warning coefficient Fws. The specific content is as follows:

[0033] If the cutting accuracy index Cyz > the cutting accuracy threshold E, it indicates that there is an abnormality in the machining process, and the machining accuracy is unqualified due to unstable cutting force. At this time, check the tool or adjust the machining parameters;

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

[0035] If the fault warning coefficient Fws > the fault warning threshold R, it indicates that there is an abnormality in the machining process, and there is a potential fault risk caused by machine tool vibration, tool wear or overheating. At this time, trigger a warning and adjust the machining parameters or check the equipment status;

[0036] If the fault warning coefficient Fws ≤ the fault warning threshold R, it indicates that the current machining process is stable and there is no fault risk;

[0037] Finally, when both the cutting accuracy index Cyz and the fault warning coefficient Fws are 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 an abnormal machining process.

[0038] The present invention provides an automatic boring control system for automotive parts. It has the following beneficial effects:

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

[0040] (2) The automated boring control system for automotive parts integrates an intelligent fault diagnosis and warning module. By constructing a multi-dimensional fault mode library and comparing the second data set of the workpiece with the data in the fault mode library in real time, it can efficiently identify potential fault risks such as machine tool vibration, tool wear, and workpiece deformation. When the system detects that the fault warning coefficient Fws exceeds the set threshold R, it can automatically trigger an alarm and adjust the cutting parameters, avoiding the deficiency of the traditional system that cannot detect and adjust faults in time. Through this intelligent warning mechanism, it can greatly reduce production losses caused by excessive tool wear or abnormal machine tool conditions, and improve the reliability and production safety of the equipment;

[0041] (3) The automated boring control system for automotive parts provides a simple and easy-to-use interface for operators through the human-machine interaction feedback module, enabling operators to efficiently operate the system without requiring a high level of technical expertise. Through wireless communication technology, the system real-time feeds back the first and second data sets of the workpiece to the operator's mobile device, providing visual charts, data curves, and alarm information. Operators can interact through the touch screen and voice commands. This friendly interaction design reduces the complexity of system operation, reduces the training cost of operators, improves the universality and usability of the system, enhances the adaptability among different operators, and thus greatly improves the popularity and market application value of the system. Brief Description of the Drawings

[0042] Figure 1 It is a schematic diagram of the framework structure of an automated boring control system for automotive parts according to the present invention. Detailed Embodiments

[0043] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0044] Embodiment 1

[0045] Please refer to Figure 1 , the present invention provides an automated boring control system for automotive parts, including a workpiece state monitoring module, an intelligent parameter adjustment module, a fault diagnosis and warning module, a human-machine interaction feedback module, and a data feedback and evaluation module;

[0046] The workpiece state monitoring module is used to monitor the data related to the appearance of the workpiece in real time, including geometric dimensions, surface roughness, and surface temperature; the data related to the appearance of the workpiece is collected in real time through sensors to generate the first data set of the workpiece;

[0047] The intelligent parameter adjustment module is used to automatically adjust the feed rate, cutting depth, and spindle speed according to the first workpiece data set; calculate and evaluate the workpiece surface quality index Sqi by extracting the first workpiece data set, and at the same time judge the specific feed rate, cutting depth, and spindle speed that need to be adjusted;

[0048] The fault diagnosis and early warning module is used to construct the second workpiece data set during the machining process and at the same time adjust the cutting parameters in the machining process in real time; by constructing a fault mode library, identify and compare faults, and automatically trigger an alarm and adjust the cutting parameters in real time;

[0049] The human-machine interaction feedback module is used to provide an interface that is simple and easy to operate for the operator, and feedback the first workpiece data set and the second workpiece data set to the operator in real time through a mobile device;

[0050] The data feedback and evaluation module is used to calculate and obtain the cutting accuracy index Cyz and the fault early warning coefficient Fws respectively according to the first workpiece data set and the second workpiece data set, and evaluate them to judge tool overwear or abnormal machine tool status.

[0051] In this embodiment, through the collaborative work of each module, the machining accuracy and efficiency can be significantly improved; the workpiece status monitoring module ensures that the workpiece appearance data during the machining process is always in the latest state by real-time monitoring of the workpiece geometric dimensions, surface roughness, and surface temperature, avoiding machining errors caused by workpiece status changes; the intelligent parameter adjustment module realizes precise control of machining parameters and improves the machining surface quality by automatically adjusting the feed rate, cutting depth, and spindle speed based on the evaluation of the workpiece surface quality index Sqi; the fault diagnosis and early warning module can timely identify potential faults such as machine tool vibration and workpiece deformation by real-time collecting workpiece machining-related data and comparing them with the fault mode library, automatically trigger an alarm and adjust the cutting parameters, thereby reducing the risk of equipment failure and production interruption; the human-machine interaction feedback module provides a simple and easy-to-use interface, enabling the operator to view the first workpiece data set and the second workpiece data set in real time through a mobile device, facilitating timely feedback and adjustment, and improving the operation convenience and system universality; the data feedback and evaluation module can effectively judge tool overwear or abnormal machine tool status by calculating the cutting accuracy index Cyz and the fault early warning coefficient Fws and evaluating them, ensuring the stability and accuracy of the production process.

[0052] Embodiment 2

[0053] The workpiece status monitoring module real-time collects the geometric dimensions of the workpiece through sensors installed on the machine tool; at the same time, a surface roughness sensor is used to accurately measure and record the surface roughness of the workpiece; in addition, a 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 first workpiece data set.

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

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

[0056]

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

[0058] 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, where the first workpiece surface quality threshold S1 > the second workpiece surface quality threshold S2. The specific content is as follows:

[0059] If the workpiece surface quality index Sqi ≥ the first workpiece surface quality threshold S1, it means 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%;

[0060] If the second workpiece surface quality threshold S2 < the workpiece surface quality index Sqi < the first workpiece surface quality threshold S1, it means 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%;

[0061] If the workpiece surface quality index Sqi ≤ the second workpiece surface quality threshold S2, it means that the workpiece surface quality meets the requirements. At this time, the system will maintain the current feed speed, cutting depth, and spindle speed.

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

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

[0064] The comparison and recognition unit is used to compare and analyze the second workpiece data set with the data in the fault mode library; by collecting in real time and based on 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 recognition is carried out through the multi-dimensional fault data model. 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 feed speed, cutting depth, and spindle speed.

[0065] The human-machine interaction feedback module transmits the first workpiece data set and the second workpiece data set during the workpiece processing process to the operator's mobile device in real time through wireless communication technology, including smartphones and mobile terminal devices supporting network connection; the operator can view the changes in parameters during the processing process in real time through the mobile device and perform corresponding operations and adjustments through the interaction interface; among them, the interface provides visual charts, data curves, and specific alarm information; at the same time, user interaction is carried out in the way of touch screen and voice commands, allowing the operator to adjust the processing parameters and trigger alarm feedback through operations.

[0066] In this embodiment, through the cooperation of each module, precise control and fault early warning of the workpiece processing process are realized, significantly improving production efficiency and product quality; the workpiece status monitoring module ensures the accurate acquisition of workpiece appearance data by collecting key data such as the geometric dimensions, surface roughness, and surface temperature of the workpiece in real time, and thus provides basic data for subsequent intelligent adjustment and fault diagnosis; the intelligent parameter adjustment module automatically 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, ensuring that the workpiece surface quality meets the requirements and automatically adjusting 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 faults such as machine tool vibration, workpiece deformation, and tool wear by collecting data such as cutting force, vibration, displacement, and speed in real time, constructing a fault mode library and comparing it with the workpiece processing data, and performing real-time identification and automatic parameter adjustment through the fault data model, reducing the risk of equipment failure; the human-machine interaction feedback module feeds back the workpiece data to the operator's mobile device in real time through wireless communication. 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 commands, realizing convenient real-time operation and adjustment, and ensuring 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] Based on the first workpiece data set and the second workpiece data set, the cutting accuracy calculation unit calculates the cutting accuracy index Cyz. The specific calculation formula is as follows:

[0070]

[0071] 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.

[0072] 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 first workpiece data set, and the spindle speed stability Rss and the cutting temperature fluctuation Tfl in the second workpiece data set, the fault warning coefficient Fws is calculated according to the following formula:

[0073]

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

[0075] If the cutting accuracy index Cyz > the cutting accuracy threshold E, it means that there is an abnormality in the machining process, and the machining accuracy is unqualified due to unstable cutting force. At this time, check the tool or adjust the machining parameters;

[0076] If the cutting accuracy index Cyz ≤ the cutting accuracy threshold E, it means that the machining process is normal, and continue with normal machining;

[0077] If the fault warning coefficient Fws > the fault warning threshold R, it means 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, trigger a warning and adjust the machining parameters or check the equipment status;

[0078] If the fault warning coefficient Fws ≤ the fault warning threshold R, it means that the current machining process is stable and there is no fault risk;

[0079] Finally, when both the cutting accuracy index Cyz and the fault warning coefficient Fws are abnormal, it means 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 an abnormal machining process.

[0080] In this embodiment, the cutting precision and fault warning feedback module effectively improves the machining precision and fault prediction ability through precise calculation and real-time evaluation; the cutting precision calculation unit calculates the cutting precision index Cyz based on the workpiece surface stress distribution Tgx, workpiece surface micro-hardness Smh in the first workpiece data group, the cutting force fluctuation Flu and the cutting temperature gradient Tgr in the second workpiece data group, so as to reflect the machining precision in real time and provide a basis for the adjustment of the machining process; the fault calculation unit calculates the fault warning coefficient Fws by extracting the workpiece surface crack density Ctd, spindle speed stability Rss, workpiece surface micro-cracks Mcr and cutting temperature fluctuation Tfl in the first and second workpiece data groups, helping to identify potential fault risks such as machine tool vibration, tool wear and abnormal cutting temperature in advance; the specific fault evaluation unit conducts real-time evaluation by comparing the cutting precision index Cyz and the fault warning coefficient Fws with the preset thresholds E and R, ensuring timely adjustment of the machining process in case of abnormalities. If the cutting precision is unqualified, tool inspection or parameter adjustment is triggered. If the fault warning coefficient exceeds the standard, a warning is triggered in time and measures are taken; through precise data calculation and intelligent feedback mechanism, this module realizes the comprehensive monitoring and optimization of the machining process, minimizing the potential risks and the production of unqualified products in the machining process.

[0081] Although the embodiments of the present invention have been shown and described, those of ordinary skill in the art can understand that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. An automated boring control system for automotive parts, characterized in that: It includes 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; The workpiece status monitoring module is used to monitor the data related to the appearance of the workpiece in real time, including geometric dimensions, surface roughness, and surface temperature; it collects the data related to the appearance of the workpiece through sensors in real time and generates the first data set of the workpiece; The intelligent parameter adjustment module is used to automatically adjust the feed speed, cutting depth, and spindle speed according to the first data set of the workpiece; it extracts the first data set of the workpiece to calculate and evaluate the surface quality index Sqi of the workpiece, and at the same time determines the specific feed speed, cutting depth, and spindle speed that need to be adjusted; The fault diagnosis and early warning module is used to construct the second data set of the workpiece during the machining process and adjust the cutting parameters during the machining process in real time; by constructing a fault mode library, it conducts fault identification comparison, automatically triggers an alarm, and adjusts the cutting parameters in real time; The human-machine interaction feedback module is used to provide a simple and easy-to-operate interface for the operator and real-time feedback the first data set of the workpiece and the second data set of the workpiece to the operator through a mobile device; The data feedback and evaluation module is used to calculate and obtain the cutting accuracy 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, and evaluate them to judge tool overwear or abnormal machine tool status.

2. The automated boring control system for an automotive part according to claim 1, characterized in that: The workpiece status monitoring module collects the geometric dimensions of the workpiece in real time through sensors installed on the machine tool; at the same time, a surface roughness sensor is used to accurately measure and record the surface roughness of the workpiece; in addition, a temperature sensor is used to monitor the surface temperature of the workpiece; the collected data related to the appearance of the workpiece undergoes data processing and fusion to generate the first data set of the workpiece.

3. The automated boring control system for an automotive part according to claim 1, characterized in that: The intelligent parameter adjustment module includes a workpiece surface quality calculation unit and a workpiece surface evaluation unit; The workpiece surface quality calculation unit calculates and obtains the workpiece surface quality index Sqi by extracting the first data set of the workpiece, performing dimensionless processing, and combining the following formula: 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.

4. The automated boring control system for an automotive part 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, where the first workpiece surface quality threshold S1 > the second workpiece surface quality threshold S2, and the specific content is as follows: If the workpiece surface quality index Sqi ≥ the first workpiece surface quality threshold S1, it means 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 < the workpiece surface quality index Sqi < the first workpiece surface quality threshold S1, it means 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 ≤ the second workpiece surface quality threshold S2, it indicates that the workpiece surface quality meets the requirements. At this time, the system will maintain the current feed rate, cutting depth, and spindle speed.

5. The automated boring control system for an automotive part according to claim 1, characterized in that: The fault diagnosis and early warning module includes a data acquisition unit and a comparison and identification unit; The data acquisition unit is used to monitor and collect workpiece processing-related data during the workpiece processing in real time. Among them, the mechanical changes during the cutting process are monitored through a cutting force sensor installed on the cutting tool or the workpiece, the vibration conditions generated by the machine tool and the workpiece during the processing are monitored in real time through a vibration sensor, the change of the feed rate is monitored in real time through a displacement sensor, and the spindle speed is monitored in real time through a speed sensor installed on the spindle; the workpiece processing-related data obtained in real time is preprocessed, filtered, and denoised, and finally a second workpiece data set is generated.

6. The automated boring control system for an automotive part according to claim 5, wherein: The comparison and identification unit is used to compare and analyze the second workpiece data set with the data in the fault mode library; a fault mode library covering a variety of processing fault modes is established by collecting in real time and based on 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, and identification is carried out through the multi-dimensional fault data model. 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 rate, cutting depth, and spindle speed.

7. An automated boring control system for automotive parts according to claim 1, characterized in that: The human-machine interaction feedback module transmits the first workpiece data set and the second workpiece data set during the workpiece processing to the operator's mobile device in real time through wireless communication technology, including smartphones and mobile terminal devices supporting network connection; the operator can view the change of parameters during the processing in real time through the mobile device and perform corresponding operations and adjustments through the interaction interface; among them, the interface provides visual charts, data curves, and specific alarm information; at the same time, user interaction is carried out in the way of touch screen and voice commands, allowing the operator to adjust the processing parameters and trigger alarm feedback through operations.

8. An automated boring control system for automotive parts according to claim 1, characterized in that: The data feedback and evaluation module includes 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. 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. An automated boring control system for automotive parts according to claim 8, characterized in that: The fault calculation unit is used to calculate the fault early warning coefficient Fws. By extracting the workpiece surface microcracks Mcr and the workpiece surface crack density Ctd in the first workpiece data set, and the spindle speed stability Rss and the cutting temperature fluctuation Tfl in the second workpiece data set, the fault early warning coefficient Fws is calculated by combining the following formula:

10. The automated boring control system for an automotive part according to claim 9, characterized in that: The specific fault assessment unit is used to preset the cutting precision threshold E and the fault warning threshold R, and evaluate the cutting precision index Cyz and the fault warning coefficient Fws respectively. The specific content is as follows: If the cutting precision index Cyz > the cutting precision threshold E, it indicates that there is an abnormality in the machining process, and the machining precision is unqualified due to unstable cutting force. At this time, check the tool or adjust the machining parameters; If the cutting precision index Cyz ≤ the cutting precision threshold E, it indicates that the machining process is normal, and continue with normal machining; If the fault warning coefficient Fws > the fault warning threshold R, it indicates that there is an abnormality in the machining process, and there is a potential fault risk caused by machine tool vibration, tool wear or overheating. At this time, trigger a warning and adjust the machining parameters or check the equipment status; If the fault warning coefficient Fws ≤ the fault warning threshold R, it means that the current machining process is stable and there is no fault risk; Finally, when both the cutting precision index Cyz and the fault warning coefficient Fws are 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 an abnormal machining process.

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