Self-adaptive adjusting system of precision machining equipment

Abstract

The invention discloses a self-adaptive adjustment system for precision machining equipment. The self-adaptive adjustment system comprises a real-time monitoring module, a cutting optimization module, a self-adaptive adjustment module, an intelligent electrode wear compensation module and a prediction maintenance module. An electrode wire path is optimized through an intelligent algorithm, machining parameters are adjusted in real time, the machining efficiency and precision are improved, the intelligent electrode abrasion compensation module prolongs the service life of the electrode wire by monitoring and automatically compensating abrasion, the prediction maintenance module reduces the non-planned shutdown time by predicting equipment faults and automatically arranging a maintenance plan, and the production efficiency is improved. The machining efficiency is improved, the downtime is shortened, the service life of the electrode wire is prolonged, the overall production cost is reduced, the equipment failure rate is reduced through predictive maintenance, and the reliability and the utilization rate of equipment are improved.

Description

Technical Field

[0001] The invention relates to the technical field of processing equipment control, in particular to a self-adaptive adjustment system for precision processing equipment. Background Art

[0002] In traditional EDM processing, the processing process often relies on the operator's experience and skills, lacking real-time monitoring and adaptive adjustment capabilities. This leads to low processing efficiency, unstable processing accuracy, and untimely equipment maintenance, which increases production costs and the risk of equipment failure. With the development of intelligent manufacturing technology, the demand for automation and intelligence of EDM processing is growing.

[0003] Existing EDM equipment faces the following technical challenges during processing:

[0004] 1. Due to the lack of real-time monitoring and dynamic adjustment capabilities, errors in the processing process are difficult to compensate in time, resulting in insufficient processing accuracy and stability;

[0005] 2. Complex workpiece shapes require precise optimization of the electrode wire path, but traditional methods are difficult to meet the needs of efficient and accurate path planning;

[0006] 3. Electrode wire wear has a significant impact on machining accuracy, but traditional methods lack effective wear monitoring and compensation mechanisms;

[0007] 4. The lack of an effective predictive maintenance mechanism leads to frequent equipment failures, increasing downtime and maintenance costs. Summary of the invention

[0008] In order to solve the above-mentioned technical problems, the present invention provides a self-adaptive adjustment system for precision machining equipment.

[0009] The technical solution of the present invention is achieved in this way:

[0010] An adaptive adjustment system for precision machining equipment, comprising a real-time monitoring module, a cutting optimization module, an adaptive adjustment module, an intelligent electrode wear compensation module and a predictive maintenance module;

[0011] The cutting optimization module uses artificial intelligence algorithms to automatically generate and optimize the electrode wire path, and dynamically adjusts it according to historical data and real-time processing conditions;

[0012] The adaptive adjustment module uses intelligent algorithms to automatically adjust the electrode wire path and processing parameters to achieve efficient processing of complex shapes;

[0013] The intelligent electrode wear compensation module utilizes an intelligent system to monitor electrode wire wear and automatically adjusts processing parameters to maintain processing accuracy and extend the service life of the electrode wire.

[0014] Furthermore, the real-time monitoring module includes a data acquisition unit, a data processing unit and a communication interface unit;

[0015] The data acquisition unit is responsible for collecting real-time data during the processing from various sensors (such as displacement, force, temperature, and vibration sensors) and providing raw data to the data processing unit;

[0016] The data processing unit pre-processes, analyzes and converts the collected data to extract useful information;

[0017] The communication interface unit acts as a bridge for data flow, connecting various modules to ensure that the data of the data acquisition unit and the data processing unit can be safely and effectively transmitted to other parts of the system.

[0018] Further, the cutting optimization module includes an intelligent algorithm unit, a historical database and a data extraction unit;

[0019] The intelligent algorithm unit dynamically adjusts the electrode wire path according to the data provided by the historical database and the data of the real-time monitoring module, and automatically generates and optimizes the electrode wire path using an artificial intelligence algorithm;

[0020] The historical database stores historical processing data for reference by the intelligent algorithm unit to optimize new processing paths, provides historical data support for the intelligent algorithm unit, and helps improve the accuracy and efficiency of the optimization algorithm;

[0021] The data extraction unit extracts relevant data from the historical database for use by the intelligent algorithm unit, and serves as an interface between the historical database and the intelligent algorithm unit to ensure accurate extraction and use of required data.

[0022] Further, the adaptive adjustment module includes an adaptive parameter calculation unit, an actuator unit and a feedback control unit;

[0023] The adaptive parameter calculation unit calculates the required processing parameters according to the real-time monitoring data and the preset control strategy, and transmits the calculation results to the actuator unit to achieve real-time adjustment of the processing process;

[0024] The actuator unit actually adjusts the working state of the processing equipment according to the instruction of the adaptive parameter calculation unit;

[0025] The feedback control unit receives feedback information from the actuator unit to form a closed-loop control to ensure dynamic adjustment and optimization of the machining process.

[0026] Further, the intelligent electrode wear compensation module includes a wear monitoring unit, a compensation algorithm unit, an execution adjustment unit and a life prediction unit;

[0027] The wear monitoring unit monitors the actual wear of the electrode wire and provides real-time wear data to the compensation algorithm unit;

[0028] The compensation algorithm unit automatically adjusts the processing parameters to compensate for the electrode wire wear according to the data of the wear monitoring unit, and transmits the calculation results to the execution adjustment unit to achieve wear compensation;

[0029] The execution adjustment unit adjusts the processing parameters according to the instructions of the compensation algorithm unit and directly acts on the equipment to achieve wear compensation;

[0030] The life prediction unit predicts the remaining service life of the electrode wire, optimizes the replacement time, provides a basis for maintenance scheduling, and reduces unplanned downtime.

[0031] Further, the predictive maintenance module includes a data analysis unit, a predictive algorithm unit, a maintenance scheduling unit and a historical maintenance recording unit;

[0032] The data analysis unit analyzes the health monitoring data of the equipment, identifies abnormal patterns and potential failures, and provides analysis results to the prediction algorithm unit to help predict equipment failures;

[0033] The prediction algorithm unit uses a machine learning algorithm to predict equipment failures and maintenance requirements, performs failure prediction based on the results of the data analysis unit, and guides the work of the maintenance scheduling unit;

[0034] The maintenance scheduling unit automatically arranges a maintenance plan according to the prediction results;

[0035] The historical maintenance record unit stores the maintenance history of the device for reference by the prediction algorithm unit, thereby providing historical data support for the prediction algorithm unit.

[0036] Compared with the prior art, the present invention has the following beneficial effects:

[0037] 1. The real-time monitoring module of the present invention collects displacement, force, temperature, vibration and other sensor data to monitor the processing status in real time, promptly discover deviations, and dynamically adjust the processing parameters according to the real-time monitoring data and the preset control strategy to achieve real-time optimization of the processing process, thereby improving the processing accuracy and stability. In addition, the cutting optimization module uses artificial intelligence algorithms, combines historical databases and real-time monitoring data, and automatically generates and optimizes the electrode wire path to meet the processing requirements of complex shapes, thereby solving the problem of insufficient processing accuracy and stability in the prior art, and at the same time can achieve the purpose of optimizing the electrode wire path of complex shapes;

[0038] 2. By setting up an intelligent electrode wear compensation module, the actual wear of the electrode wire is monitored, and the processing parameters are automatically adjusted to compensate for the wear, maintain the processing accuracy, and extend the service life of the electrode wire, which supplements the missing electrode wire wear monitoring and compensation mechanism in the existing technology;

[0039] 3. By setting up a predictive maintenance module, the data analysis unit identifies abnormal patterns and potential failures, uses machine learning algorithms to predict equipment failures and maintenance needs, automatically arranges maintenance plans, and reduces unexpected downtime, solving the problem of the lack of an effective predictive maintenance mechanism in existing technologies;

[0040] 4. In summary, the present invention improves processing efficiency and precision by optimizing the electrode wire path and adjusting processing parameters in real time through intelligent algorithms. The intelligent electrode wear compensation module extends the service life of the electrode wire by monitoring and automatically compensating for wear. The predictive maintenance module reduces unplanned downtime by predicting equipment failures and automatically arranging maintenance plans. By improving processing efficiency, reducing downtime and extending the service life of the electrode wire, the overall production cost is reduced. Predictive maintenance reduces equipment failure rate and improves equipment reliability and utilization. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 This is a system framework diagram of a self-adaptive adjustment system for precision machining equipment of the present invention;

[0042] Figure 2 It is a structural schematic diagram of the real-time monitoring module in the present invention;

[0043] Figure 3 It is a structural schematic diagram of the cutting optimization module in the present invention;

[0044] Figure 4 It is a structural schematic diagram of the adaptive adjustment module in the present invention;

[0045] Figure 5 It is a structural schematic diagram of the intelligent electrode wear compensation module in the present invention;

[0046] Figure 6 It is a structural schematic diagram of the predictive maintenance module in the present invention. DETAILED DESCRIPTION

[0047] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0048] Example 1

[0049] like Figure 1-6 As shown, a precision machining equipment adaptive adjustment system includes a real-time monitoring module, a cutting optimization module, an adaptive adjustment module, an intelligent electrode wear compensation module and a predictive maintenance module;

[0050] The cutting optimization module uses artificial intelligence algorithms to automatically generate and optimize the electrode wire path, and dynamically adjusts it according to historical data and real-time processing conditions;

[0051] The adaptive adjustment module uses intelligent algorithms to automatically adjust the electrode wire path and processing parameters to achieve efficient processing of complex shapes;

[0052] The intelligent electrode wear compensation module uses an intelligent system to monitor electrode wire wear and automatically adjusts processing parameters to maintain processing accuracy and extend the service life of the electrode wire;

[0053] In the adaptive adjustment module, the core task of the adaptive parameter calculation unit is to calculate the required processing parameters based on the real-time monitoring data and the preset control strategy, which involves the analysis and processing of the monitoring data, as well as the decision-making of adjusting the processing parameters based on these data;

[0054] ① First, the adaptive parameter calculation unit needs to analyze the real-time monitoring data to determine the current processing status. This data may include:

[0055] Displacement, velocity and acceleration (obtained from displacement sensors);

[0056] Force and pressure (obtained from force sensors);

[0057] Temperature (obtained from a temperature sensor);

[0058] Vibration (obtained from a vibration sensor);

[0059] ② Based on the analysis results, the adaptive parameter calculation unit needs to decide how to adjust the machining parameters to optimize the machining process, which may include:

[0060] Discharge current (I): affects material removal rate and surface roughness;

[0061] Pulse width (T_on) and pulse interval (T_off): affect electrode wire wear and processing efficiency;

[0062] Voltage (V): affects the discharge gap and material removal rate;

[0063] ③ Adjust the discharge current according to the requirements of material removal rate (MR) and surface roughness (Ra):

[0064]

[0065] Among them, I new is the new discharge current, I current is the current discharge current, MR disired is the desired material removal rate, MR current is the current material removal rate;

[0066] According to the requirements of electrode wire wear rate (W) and processing efficiency (E), adjust the pulse width and pulse interval:

[0067]

[0068]

[0069] T on，new and T off，new is the new pulse width and pulse interval, T on，current and T off，current is the current pulse width and pulse interval, E desired and E currient are the expected and current processing efficiencies,

[0070] W desired and W currient is the expected and current electrode wire wear rate;

[0071] ④ The feedback control unit receives feedback information from the actuator unit, including the actual discharge current, pulse width and voltage, etc., compares it with the preset target value, and makes adjustments through the control algorithm (such as PID control) to ensure dynamic adjustment and optimization of the machining process.

[0072] Furthermore, the real-time monitoring module includes a data acquisition unit, a data processing unit and a communication interface unit;

[0073] The data acquisition unit is responsible for collecting real-time data during the processing from various sensors (such as displacement, force, temperature, and vibration sensors) and providing raw data to the data processing unit;

[0074] The data processing unit pre-processes, analyzes and converts the collected data to extract useful information;

[0075] The communication interface unit acts as a bridge for data flow, connecting various modules to ensure that the data of the data acquisition unit and the data processing unit can be safely and effectively transmitted to other parts of the system.

[0076] Further, the cutting optimization module includes an intelligent algorithm unit, a historical database and a data extraction unit;

[0077] The intelligent algorithm unit dynamically adjusts the electrode wire path according to the data provided by the historical database and the data of the real-time monitoring module, and automatically generates and optimizes the electrode wire path using an artificial intelligence algorithm;

[0078] This embodiment aims to automatically generate and optimize the path of the electrode wire in electric discharge machining (EDM) through an intelligent algorithm, and adopts a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to dynamically adjust the electrode wire path according to historical data and real-time machining conditions to improve machining efficiency and accuracy;

[0079] In the optimization of the electrode wire path, the PSO algorithm can quickly explore the solution space and find a better electrode wire path, while the GA can perform fine-grained searches in these solutions and find a better path through crossover and mutation operations, as follows:

[0080] ①Data input:

[0081] Data provided by the historical database: historical optimal path length L hist ; Historical best path coordinates P hist =(x hist ,y hist );

[0082] Historical processing data D hist , including successful processing paths and processing efficiency;

[0083] Data provided by the real-time monitoring module: real-time machine tool status M hist , including the current machine temperature and vibration status; real-time electrode wire status W real , including the current electrode wire wear degree; real-time processing efficiency E real ; Real-time processing quality Q real ;

[0084] ②Data calculation:

[0085] Randomly initialize the particle position x i and speed v i , please refer to P hist ;

[0086] Speed ​​update, using the formula:

[0087] v i (t+1)=ω·v i (t)+c1·r1·(p i -x i t)+c2·r2·(p g -x i t))

[0088] Among them, p i and p gCan be based on L hist and real-time data hist , W real Make adjustments;

[0089] Position update, using the formula:

[0090] x i (t+1)=x i (t)+v i (t+1)

[0091] Calculation result, updated particle position x i (t+1) and speed v i (t+1);

[0092] According to the updated particle position, calculate the new path length. If the new path length is better than the historical best, update p i and p g , adjust particle parameters according to real-time data to adapt to the real-time status of machine tools and electrode wires;

[0093] ④Fitness evaluation, using the formula:

[0094] f (x) = Defined by path length and smoothness

[0095] The fitness function can be combined with D hist 、E real and Q real To evaluate; fitness function f (x) It is used to evaluate the performance of each individual, that is, the performance of each electrode wire path, where x is a vector encoding individual characteristics, that is, a vector encoding electrode wire path characteristics, representing a potential electrode wire path solution. The goal of this step is to maximize the fitness function; in this embodiment, f (x) It can be defined based on parameters such as path length, smoothness, machining efficiency and quality;

[0096] ⑤ Select individuals for reproduction according to fitness, randomly select two individuals for crossover to generate new individuals, randomly change certain genes of individuals with a certain probability, and generate a new generation of populations, including the optimized electrode wire path;

[0097] The probability of selecting individuals for reproduction is proportional to their fitness. i is the probability of the i-th individual being selected, that is, the probability that the i-th electrode wire path is selected for reproduction, which is proportional to its fitness, then:

[0098]

[0099] Among them, x iis the encoding vector of the ith individual, representing the ith electrode wire path in the population, f(x i ) is the fitness of the ith individual, N is the total number of individuals in the population, and the total number of electrode wire paths in the population, that is, the number of paths considered for optimization;

[0100] The selected individuals generate new offspring through crossover operation. If the crossover point is k (indicating at which gene position the crossover operation is performed), and considering two individuals x i and x j , descendants x new It can be expressed as:

[0101] x new =(x i1 ,x i2 ,...,x ik ,x jk+1) ,...,x jn )

[0102] Among them, x i1 ,x i2 ,...,x ik is from individual x i The gene represents a coordinate sequence in the i-th path, x j(k+1) ,...,x jn is from individual x j The gene represents a coordinate sequence in the jth path; x new The new offspring individuals generated by the crossover operation represent a new electrode wire path;

[0103] Modify some genes of newborn individuals with a small probability μ to introduce mutations and increase the diversity of the population. nk , the mutation operation can be expressed as:

[0104] x' nk =x nk +δ, with probabilityμ

[0105] x nk is the gene after mutation operation, indicating that on path x n A slight adjustment was made on the k-th coordinate point;

[0106] Among them, δ is a small random change used to fine-tune the coordinates of the electrode wire path in the mutation operation, and μ is the mutation rate, which indicates the probability of the electrode wire path coordinates mutating in each generation;

[0107] By integrating PSO and GA algorithms and combining historical and real-time data, an efficient electrode wire path optimization method is provided for electrical discharge machining (EDM), which can adapt to the ever-changing machining environment and realize automatic optimization of the electrode wire path, thereby improving machining efficiency and quality and reducing production costs.

[0108] The historical database stores historical processing data for reference by the intelligent algorithm unit to optimize new processing paths, provides historical data support for the intelligent algorithm unit, and helps improve the accuracy and efficiency of the optimization algorithm;

[0109] The data extraction unit extracts relevant data from the historical database for use by the intelligent algorithm unit, and serves as an interface between the historical database and the intelligent algorithm unit to ensure accurate extraction and use of required data.

[0110] Further, the adaptive adjustment module includes an adaptive parameter calculation unit, an actuator unit and a feedback control unit;

[0111] The adaptive parameter calculation unit calculates the required processing parameters according to the real-time monitoring data and the preset control strategy, and transmits the calculation results to the actuator unit to achieve real-time adjustment of the processing process;

[0112] The actuator unit actually adjusts the working state of the processing equipment according to the instruction of the adaptive parameter calculation unit;

[0113] The feedback control unit receives feedback information from the actuator unit to form a closed-loop control to ensure dynamic adjustment and optimization of the machining process.

[0114] Further, the intelligent electrode wear compensation module includes a wear monitoring unit, a compensation algorithm unit, an execution adjustment unit and a life prediction unit;

[0115] The wear monitoring unit monitors the actual wear of the electrode wire and provides real-time wear data to the compensation algorithm unit;

[0116] The compensation algorithm unit automatically adjusts the processing parameters to compensate for the electrode wire wear according to the data of the wear monitoring unit, and transmits the calculation results to the execution adjustment unit to achieve wear compensation;

[0117] The execution adjustment unit adjusts the processing parameters according to the instructions of the compensation algorithm unit and directly acts on the equipment to achieve wear compensation;

[0118] The execution adjustment unit receives compensation instructions from the compensation algorithm unit, which contain specific adjustment measures required to compensate for electrode wire wear. According to the instructions provided by the compensation algorithm unit, the processing parameters in the numerical control system are adjusted, including modifying discharge parameters, adjusting the position of the electrode wire or changing the processing path, etc., directly acting on the EDM equipment to implement compensation measures.

[0119] The life prediction unit predicts the remaining service life of the electrode wire, optimizes the replacement time, provides a basis for maintenance scheduling, and reduces unplanned downtime;

[0120] In this embodiment, the compensation algorithm unit first receives real-time wear data from the wear monitoring unit, including changes in the diameter and length of the electrode wire. Based on the acquired wear data, the compensation algorithm unit calculates the required compensation amount to predict the remaining life and wear trend of the electrode wire, and adjusts the processing parameters accordingly, including the discharge frequency, voltage and current. The compensation algorithm unit formulates a set of compensation strategies based on the wear data and processing requirements, including adjusting the position of the electrode wire, changing the processing path or modifying the discharge parameters.

[0121] Further, the predictive maintenance module includes a data analysis unit, a predictive algorithm unit, a maintenance scheduling unit and a historical maintenance recording unit;

[0122] The data analysis unit analyzes the health monitoring data of the equipment, identifies abnormal patterns and potential failures, and provides analysis results to the prediction algorithm unit to help predict equipment failures;

[0123] The prediction algorithm unit uses a machine learning algorithm to predict equipment failures and maintenance requirements, performs failure prediction based on the results of the data analysis unit, and guides the work of the maintenance scheduling unit;

[0124] Before fault prediction, the collected equipment health monitoring data must first be preprocessed to ensure data quality and availability, including data cleaning, normalization, and feature extraction;

[0125] Specifically, features with high correlation with equipment failure are extracted, including statistical features of temperature, pressure and vibration, such as mean, standard deviation, maximum value and minimum value. Through correlation analysis, features with high correlation with equipment failure are selected to reduce the complexity of the model and improve the prediction accuracy.

[0126] The maintenance scheduling unit automatically arranges a maintenance plan according to the prediction results;

[0127] The historical maintenance record unit stores the maintenance history of the device for reference by the prediction algorithm unit, thereby providing historical data support for the prediction algorithm unit.

[0128] This embodiment collects processing data from various sensors through a real-time monitoring module, and these data include parameters such as displacement, force, temperature and vibration. The cutting optimization module uses an artificial intelligence algorithm, combined with data in a historical database and real-time monitoring data, to automatically generate and optimize the electrode wire path. The adaptive adjustment module automatically adjusts the electrode wire path and processing parameters according to real-time data and preset control strategies to achieve efficient processing of complex shapes. The intelligent electrode wear compensation module monitors the electrode wire wear and automatically adjusts the processing parameters to maintain processing accuracy and extend the service life of the electrode wire. The predictive maintenance module identifies potential faults through a data analysis unit, and the predictive algorithm unit uses a machine learning algorithm to predict equipment failures and maintenance needs, thereby guiding the maintenance scheduling unit to automatically arrange maintenance plans.

[0129] It improves the automation and intelligence level of electrospark machining (EDM). It optimizes the electrode wire path through real-time monitoring and intelligent algorithms, significantly improving the processing efficiency and accuracy. The intelligent electrode wear compensation module effectively extends the service life of the electrode wire and reduces processing errors. The predictive maintenance module reduces unplanned downtime by predicting equipment failures and automatically scheduling maintenance plans, thereby improving equipment reliability and utilization, and reducing production and maintenance costs.

[0130] Example 2

[0131] This embodiment provides an adaptive adjustment system for precision machining equipment, including a real-time monitoring module, a cutting optimization module, an adaptive adjustment module, an intelligent electrode wear compensation module, and a predictive maintenance module;

[0132] The cutting optimization module uses artificial intelligence algorithms to automatically generate and optimize the electrode wire path, and dynamically adjusts it according to historical data and real-time processing conditions;

[0133] The adaptive adjustment module uses intelligent algorithms to automatically adjust the electrode wire path and processing parameters to achieve efficient processing of complex shapes;

[0134] The intelligent electrode wear compensation module utilizes an intelligent system to monitor electrode wire wear and automatically adjusts processing parameters to maintain processing accuracy and extend the service life of the electrode wire.

[0135] Furthermore, the real-time monitoring module includes a data acquisition unit, a data processing unit and a communication interface unit;

[0136] The data acquisition unit is responsible for collecting real-time data during the processing from various sensors (such as displacement, force, temperature, and vibration sensors) and providing raw data to the data processing unit;

[0137] The data processing unit pre-processes, analyzes and converts the collected data to extract useful information;

[0138] The communication interface unit acts as a bridge for data flow, connecting various modules to ensure that the data of the data acquisition unit and the data processing unit can be safely and effectively transmitted to other parts of the system.

[0139] Further, the cutting optimization module includes an intelligent algorithm unit, a historical database and a data extraction unit;

[0140] The intelligent algorithm unit dynamically adjusts the electrode wire path according to the data provided by the historical database and the data of the real-time monitoring module, and automatically generates and optimizes the electrode wire path using an artificial intelligence algorithm;

[0141] This embodiment aims to automatically generate and optimize the path of the electrode wire in electric discharge machining (EDM) through an intelligent algorithm, and adopts a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to dynamically adjust the electrode wire path according to historical data and real-time machining conditions to improve machining efficiency and accuracy;

[0142] In the optimization of electrode wire path, the PSO algorithm can quickly explore the solution space and find a better electrode wire path, while the GA can perform fine-grained search in these solutions and find a better path through crossover and mutation operations;

[0143] The historical database stores historical processing data for reference by the intelligent algorithm unit to optimize new processing paths, provides historical data support for the intelligent algorithm unit, and helps improve the accuracy and efficiency of the optimization algorithm;

[0144] The data extraction unit extracts relevant data from the historical database for use by the intelligent algorithm unit, and serves as an interface between the historical database and the intelligent algorithm unit to ensure accurate extraction and use of required data.

[0145] Further, the adaptive adjustment module includes an adaptive parameter calculation unit, an actuator unit and a feedback control unit;

[0146] The adaptive parameter calculation unit calculates the required processing parameters according to the real-time monitoring data and the preset control strategy, and transmits the calculation results to the actuator unit to achieve real-time adjustment of the processing process;

[0147] The actuator unit actually adjusts the working state of the processing equipment according to the instruction of the adaptive parameter calculation unit;

[0148] The feedback control unit receives feedback information from the actuator unit to form a closed-loop control to ensure dynamic adjustment and optimization of the machining process.

[0149] Further, the intelligent electrode wear compensation module includes a wear monitoring unit, a compensation algorithm unit, an execution adjustment unit and a life prediction unit;

[0150] The wear monitoring unit monitors the actual wear of the electrode wire and provides real-time wear data to the compensation algorithm unit;

[0151] The compensation algorithm unit automatically adjusts the processing parameters to compensate for the electrode wire wear according to the data of the wear monitoring unit, and transmits the calculation results to the execution adjustment unit to achieve wear compensation;

[0152] Electrode loss can be estimated by the following formula:

[0153]

[0154] L is the electrode loss, η is the electrode efficiency, V is the processing speed, and A is the effective area of ​​the electrode;

[0155] The compensation model is used to predict the process by simulation, so as to predict the electrode loss status and provide a basis for compensation. Based on the existing experimental data, combined with the electrode loss principle and processing conditions, the electrode loss status is predicted and the compensation model is established;

[0156] In EDM machining, due to the complexity of the electrode surface, each discharge forming will produce a loss area with equal volume but non-uniform shape on the electrode surface. This process can be regarded as peeling off shells of the same volume but different shapes layer by layer in the electrode feeding direction. Therefore, in the same time unit, each peeled layer is a loss layer with the same volume.

[0157] The mathematical model of the layered compensation principle involves calculating the compensation amount of each layer. Taking the i-th layer as an example, the compensation amount Q of the i-th layer is i It can be calculated by the following formula:

[0158] Q i =∫∫D i (f i (x,y,z)-f i-1 (x,y,z))ds

[0159] Among them, D i is the projection of the i-th compensation surface, f i (x, y, z) is the surface equation after compensation of the i-th layer, f i-1 (x,y,z)) is the equation of the surface to be compensated at the i-1th layer, and s is the projected area of ​​the compensation surface on the xy plane;

[0160] Combined with the prediction model of electrode loss, the layered compensation principle can be further refined. For example, if the electrode loss q in a single discharge process is known to be:

[0161]

[0162] Among them, t d is the discharge time, t p is the idle time, v w is the ratio of workpiece erosion volume to time, this model can be used to predict the electrode loss of each layer and calculate the compensation amount accordingly;

[0163] The application result of the layered compensation principle is that it can significantly improve the precision of EDM machining. Through layer-by-layer vectorized offset compensation, the pre-compensated initial shape of the electrode is finally obtained, thereby effectively compensating for electrode wear during EDM machining and reducing machining errors caused by electrode loss. The layered compensation principle regards electrode loss as a layered process and uses a mathematical model to predict the loss of each layer, providing an effective compensation method for electrode wear in EDM.

[0164] The execution adjustment unit adjusts the processing parameters according to the instructions of the compensation algorithm unit and directly acts on the equipment to achieve wear compensation;

[0165] The execution adjustment unit receives compensation instructions from the compensation algorithm unit, which contain specific adjustment measures required to compensate for electrode wire wear. According to the instructions provided by the compensation algorithm unit, the processing parameters in the numerical control system are adjusted, including modifying discharge parameters, adjusting the position of the electrode wire or changing the processing path, etc., directly acting on the EDM equipment to implement compensation measures.

[0166] The life prediction unit predicts the remaining service life of the electrode wire, optimizes the replacement time, provides a basis for maintenance scheduling, and reduces unplanned downtime;

[0167] In this embodiment, the compensation algorithm unit first receives real-time wear data from the wear monitoring unit, including changes in the diameter and length of the electrode wire. Based on the acquired wear data, the compensation algorithm unit calculates the required compensation amount to predict the remaining life and wear trend of the electrode wire, and adjusts the processing parameters accordingly, including the discharge frequency, voltage and current. The compensation algorithm unit formulates a set of compensation strategies based on the wear data and processing requirements, including adjusting the position of the electrode wire, changing the processing path or modifying the discharge parameters.

[0168] Further, the predictive maintenance module includes a data analysis unit, a predictive algorithm unit, a maintenance scheduling unit and a historical maintenance recording unit;

[0169] The data analysis unit analyzes the health monitoring data of the equipment, identifies abnormal patterns and potential failures, and provides analysis results to the prediction algorithm unit to help predict equipment failures;

[0170] The prediction algorithm unit uses a machine learning algorithm to predict equipment failures and maintenance requirements, performs failure prediction based on the results of the data analysis unit, and guides the work of the maintenance scheduling unit;

[0171] Before fault prediction, the collected equipment health monitoring data must first be preprocessed to ensure data quality and availability, including data cleaning, normalization, and feature extraction;

[0172] Specifically, features with high correlation with equipment failure are extracted, including statistical features of temperature, pressure and vibration, such as mean, standard deviation, maximum value and minimum value. Through correlation analysis, features with high correlation with equipment failure are selected to reduce the complexity of the model and improve the prediction accuracy.

[0173] ① Data preprocessing and normalization. For numerical features x, use the minimum-maximum normalization formula:

[0174]

[0175] ②Use unique hot encoding to convert categorical feature C into numerical feature:

[0176]

[0177] Assume that there are two types of electrode wire materials, C1 and C2. After one-hot encoding, if the electrode wire material is C1, then C ， is [1,0]; if it is C2, then C ， is [0,1];

[0178] ③ Use correlation analysis to select features, and select features whose correlation with equipment failure is higher than a certain threshold θ: Select features where |r|>θ, where r is the correlation coefficient between the feature and the failure;

[0179] ④Data segmentation: divide the data set D into the training set D train and the test set D test :D=D train ∪D test ;

[0180] ⑤Use random forest for model training, which can be expressed as: M = Train (D train , P), where M is the trained model and P is the model parameter;

[0181] ⑥ Use the test set to evaluate the model performance and calculate the accuracy A:

[0182] ⑦Model optimization, use grid search to optimize model parameters:

[0183] P* =GridSearch(D train , P candidates ), where P * is the optimal parameter, P candidates is a set of candidate parameters.

[0184] The maintenance scheduling unit automatically arranges a maintenance plan according to the prediction results;

[0185] The historical maintenance record unit stores the maintenance history of the device for reference by the prediction algorithm unit, thereby providing historical data support for the prediction algorithm unit.

[0186] In this embodiment, the adaptive adjustment module and the intelligent electrode wear compensation module ensure real-time adjustment and wear compensation of the machining process, while the predictive maintenance module predicts potential failures by analyzing equipment health monitoring data to achieve predictive maintenance; the electrode wire path is automatically generated and optimized through intelligent algorithms, which improves the efficiency and accuracy of EDM machining. The adaptive adjustment capability of the system enables the machining process to respond to environmental changes and workpiece characteristics in real time, achieving efficient machining of complex shapes. The intelligent electrode wear compensation module maintains machining accuracy and reduces machining errors caused by wear through real-time monitoring and automatic compensation. The application of the predictive maintenance module reduces equipment failure rates, optimizes maintenance plans, reduces maintenance costs, and improves the overall performance and production efficiency of the equipment.

Claims

1. An adaptive adjustment system for precision machining equipment, characterized in that: It includes real-time monitoring module, cutting optimization module, adaptive adjustment module, intelligent electrode wear compensation module and predictive maintenance module; The cutting optimization module uses artificial intelligence algorithms to automatically generate and optimize the electrode wire path, and dynamically adjusts it according to historical data and real-time processing conditions; The adaptive adjustment module uses intelligent algorithms to automatically adjust the electrode wire path and processing parameters to achieve efficient processing of complex shapes; The intelligent electrode wear compensation module utilizes an intelligent system to monitor electrode wire wear and automatically adjusts processing parameters to maintain processing accuracy and extend the service life of the electrode wire.

2. The self-adaptive adjustment system for precision machining equipment according to claim 1, characterized in that: The cutting optimization module includes an intelligent algorithm unit, a historical database and a data extraction unit; The intelligent algorithm unit dynamically adjusts the electrode wire path according to the data provided by the historical database and the data of the real-time monitoring module, and automatically generates and optimizes the electrode wire path using an artificial intelligence algorithm; The historical database stores historical processing data for reference by the intelligent algorithm unit to optimize new processing paths; The data extraction unit extracts relevant data from the historical database for use by the intelligent algorithm unit.

3. The self-adaptive adjustment system for precision machining equipment according to claim 1, characterized in that: The adaptive adjustment module includes an adaptive parameter calculation unit, an actuator unit and a feedback control unit; The adaptive parameter calculation unit calculates the required processing parameters according to the real-time monitoring data and the preset control strategy, and transmits the calculation results to the actuator unit to achieve real-time adjustment of the processing process; The actuator unit actually adjusts the working state of the processing equipment according to the instruction of the adaptive parameter calculation unit; The feedback control unit receives feedback information from the actuator unit to form a closed-loop control to ensure dynamic adjustment and optimization of the machining process.

4. The self-adaptive adjustment system for precision machining equipment according to claim 1, characterized in that: The intelligent electrode wear compensation module includes a wear monitoring unit, a compensation algorithm unit, an execution adjustment unit and a life prediction unit; The wear monitoring unit monitors the actual wear of the electrode wire and provides real-time wear data to the compensation algorithm unit; The compensation algorithm unit automatically adjusts the processing parameters to compensate for the electrode wire wear according to the data of the wear monitoring unit, and transmits the calculation results to the execution adjustment unit to achieve wear compensation; The execution adjustment unit adjusts the processing parameters according to the instructions of the compensation algorithm unit and directly acts on the equipment to achieve wear compensation; The life prediction unit predicts the remaining service life of the electrode wire, optimizes the replacement time, provides a basis for maintenance scheduling, and reduces unplanned downtime.

5. The self-adaptive adjustment system for precision machining equipment according to claim 1, characterized in that: The predictive maintenance module includes a data analysis unit, a predictive algorithm unit, a maintenance scheduling unit and a historical maintenance recording unit; The data analysis unit analyzes the health monitoring data of the equipment, identifies abnormal patterns and potential failures, and provides analysis results to the prediction algorithm unit to help predict equipment failures; The prediction algorithm unit uses a machine learning algorithm to predict equipment failures and maintenance requirements, performs failure prediction based on the results of the data analysis unit, and guides the work of the maintenance scheduling unit; The maintenance scheduling unit automatically arranges a maintenance plan according to the prediction results; The historical maintenance record unit stores the maintenance history of the device for reference by the prediction algorithm unit, thereby providing historical data support for the prediction algorithm unit.

6. The self-adaptive adjustment system for precision machining equipment according to claim 2, characterized in that: The intelligent algorithm unit dynamically adjusts the electrode wire path according to the data provided by the historical database and the data of the real-time monitoring module, and automatically generates and optimizes the electrode wire path using an artificial intelligence algorithm; This embodiment aims to automatically generate and optimize the path of the electrode wire in electric discharge machining (EDM) through an intelligent algorithm, and adopts a particle swarm optimization (PSO) algorithm and a genetic algorithm (GA) to dynamically adjust the electrode wire path according to historical data and real-time machining conditions to improve machining efficiency and accuracy; In the electrode wire path optimization, the PSO algorithm can quickly explore the solution space and find a better electrode wire path, while the GA can perform fine-grained searches among these solutions and find a better path through crossover and mutation operations.

7. The self-adaptive adjustment system for precision machining equipment according to claim 1, characterized in that: The real-time monitoring module includes a data acquisition unit, a data processing unit and a communication interface unit; The data acquisition unit is responsible for collecting real-time data during the processing from various sensors and providing raw data to the data processing unit; The data processing unit pre-processes, analyzes and converts the collected data to extract useful information; The communication interface unit acts as a bridge for data flow, connecting various modules to ensure that the data of the data acquisition unit and the data processing unit can be safely and effectively transmitted to other parts of the system.

8. The self-adaptive adjustment system for precision machining equipment according to claim 4, characterized in that: The process of implementing wear compensation by the compensation algorithm unit is as follows: Electrode loss can be estimated by the following formula: L is the electrode loss, η is the electrode efficiency, V is the processing speed, and A is the effective area of ​​the electrode; The compensation model is used to predict the process by simulation, so as to predict the electrode loss status and provide a basis for compensation. Based on the existing experimental data, combined with the electrode loss principle and processing conditions, the electrode loss status is predicted and the compensation model is established; In EDM machining, due to the complexity of the electrode surface, each discharge forming will produce a loss area with equal volume but non-uniform shape on the electrode surface. This process can be regarded as peeling off shells of the same volume but different shapes layer by layer in the electrode feeding direction. Therefore, in the same time unit, each peeled layer is a loss layer with the same volume. The mathematical model of the layered compensation principle involves calculating the compensation amount of each layer. Taking the i-th layer as an example, the compensation amount Q of the i-th layer is i Calculated by the following formula: Q i =∫∫D i (f i (x,y,z)-f i-1 (x,y,z))ds Among them, D i is the projection of the i-th compensation surface, f i (x, y, z) is the surface equation after compensation of the i-th layer, f i-1 (x,y,z)) is the equation of the surface to be compensated at the i-1th layer, and s is the projected area of ​​the compensation surface on the xy plane; Combined with the prediction model of electrode loss, the layered compensation principle can be further refined. For example, if the electrode loss q in a single discharge process is known to be: Among them, t d is the discharge time, t p is the idle time, v w is the ratio of workpiece erosion volume to time. This model can be used to predict the electrode loss of each layer and calculate the compensation amount accordingly.

9. The self-adaptive adjustment system for precision machining equipment according to claim 1, characterized in that: The real-time monitoring module includes a data acquisition unit, a data processing unit and a communication interface unit; The data acquisition unit is responsible for collecting real-time data during the processing from various sensors and providing raw data to the data processing unit; The data processing unit pre-processes, analyzes and converts the collected data to extract useful information; The communication interface unit acts as a bridge for data flow, connecting various modules to ensure that the data of the data acquisition unit and the data processing unit can be safely and effectively transmitted to other parts of the system.

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