Numerical control precision machining method for hardware products

Abstract

The invention belongs to the technical field of numerical control machining, and particularly relates to a hardware product numerical control precision machining method which comprises the steps that an industrial-grade machining full-link state sensing system is constructed, and vibration, acoustic emission, cutting force and temperature data are collected in real time through multiple types of sensors; based on a deep convolution and long-short-term memory hybrid neural network model, a dynamic parameter self-adaptive regulation and control mechanism of an industrial control core is fused, and the spindle rotating speed, the feeding rate, the cutting depth and the cooling liquid flow are accurately adjusted; after each process, a non-contact optical probe is used for carrying out online quality evaluation, and a result is fed back to the model to form industrial-grade closed-loop control. According to the invention, through a sensing-decision-execution-feedback industrial control architecture, adaptive optimization of process parameters and multi-process collaborative error compensation are realized, the processing stability, the yield and the efficiency of multi-variety and small-batch hardware are remarkably improved, and industrial control requirements in a flexible manufacturing scene are met.

Description

Technical Field

[0001] This invention belongs to the field of CNC machining technology, specifically a CNC precision machining method for hardware products. Background Technology

[0002] As the manufacturing industry moves towards higher precision, higher efficiency, and greater intelligence, CNC precision machining technology has become a core support in the hardware product manufacturing field. The deep integration of industrial control technology is key to achieving intelligent upgrades in processing. Hardware products are widely used in industries such as construction, home furnishing, electronics, and machinery equipment. Their structural complexity and dimensional accuracy requirements are constantly increasing, placing higher standards on the stability of processing techniques, repeatability, and closed-loop control of surface quality in industrial control. Against this backdrop, CNC machining, integrating industrial control logic, is gradually replacing traditional manual or semi-automatic processing methods, becoming a key technological path for achieving complex geometric features and micron-level tolerance control, thanks to its programmed control, multi-axis linkage, and automated operation capabilities.

[0003] The CNC precision machining of hardware products needs to efficiently complete complex processes such as drilling, milling, tapping and contour forming. This process relies on the collaborative optimization of machining path planning, tool parameter matching and real-time cutting status feedback in the industrial control system. The core objective is to minimize machining deformation, tool wear and surface roughness deterioration while ensuring material removal efficiency.

[0004] However, existing industrial control solutions are increasingly showing significant bottlenecks when dealing with the flexible production needs of multi-variety, small-batch hardware parts: traditional industrial control systems generally adopt fixed process parameter libraries and offline programming modes, lacking adaptive industrial control capabilities based on real-time sensing data. They cannot dynamically adjust according to material batch differences, real-time tool wear status, and machine tool dynamic characteristics, resulting in difficulty in ensuring processing consistency. In composite machining, multiple process switching is frequent, and traditional CNC industrial control systems lack closed-loop sensing and dynamic compensation mechanisms for machining status, easily leading to cumulative errors and dimensional deviations. For thin-walled, irregularly shaped, or high-hardness hardware parts, existing industrial control methods have limited capabilities in cutting force control and thermal deformation suppression, often requiring manual intervention or secondary finishing, which seriously restricts the improvement of production efficiency and yield. Although recent studies have introduced sensor fusion and machine learning algorithms to improve machining adaptability, they are mostly limited to single-process optimization or offline model training, failing to build a closed-loop control system of "sensing-decision-execution-feedback" that meets industrial control standards, especially lacking a systematic industrial control solution for the coordinated optimization of multi-process coupled disturbances and dynamic process parameters.

[0005] Therefore, the present invention provides a CNC precision machining method for hardware products. Summary of the Invention

[0006] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0007] The technical solution adopted by this invention to solve its technical problem is: a CNC precision machining method for hardware products, comprising: Step 1: Construct an industrial-grade end-to-end state perception system for machining processes (the core of the perception layer in industrial control). Deploy multi-type sensor arrays at the CNC machine tool spindle, turret, and workpiece clamping positions to collect vibration signals, acoustic emission signals, three-dimensional components of cutting force, and temperature field distribution data in real time during the machining process. Simultaneously collect and convert analog-to-digital data through a high-speed data acquisition card at a sampling frequency of no less than 100 kHz to provide high-fidelity input data for industrial control decisions. Step 2: Establish an industrial-grade dynamic process parameter adaptive adjustment mechanism based on multi-source sensing data (the core of the decision-making layer of industrial control). A hybrid model of an 8-layer convolutional neural network and a long short-term memory network is used to extract features and identify the state of the collected data. This model determines tool wear, cutting stability, and material removal status in real time, following the precise control logic of industrial control to generate process parameter adjustment instructions to improve machining accuracy. The hybrid model contains 4 convolutional layers, 2 pooling layers, and 2 long short-term memory layers. The convolutional kernel size is 3×3, and the activation function is a modified linear unit. The model is trained using a dataset containing 100,000 machining state samples over 200 training cycles. The final model achieves a classification accuracy of 99.2% on the test set, meeting the real-time and reliability requirements of industrial control. The degree of tool wear is determined by a quantization threshold based on the features fused from multi-source sensing data, specifically: Initial wear stage: The total harmonic distortion (THD) of the vibration signal is between 1.5% and 3.0%, the root mean square value (RMS) of the acoustic emission signal is between 0.2V and 0.5V, the frequency domain energy ratio (FE) of the cutting force signal is between 20% and 35%, and the temperature gradient change (TG) is between 0.5℃ / s and 1.5℃ / s. When at least three of the above four characteristics meet the range, and the confidence level of the support vector machine classifier output is ≥95%, it is determined to be initial wear, providing a clear trigger condition for adjusting industrial control parameters. Normal wear stage: Total harmonic distortion (THD) of vibration signal is between 3.0% and 6.0%, root mean square value (RMS) of acoustic emission signal is between 0.5V and 1.2V, frequency domain energy ratio (FE) of cutting force signal is between 35% and 55%, and temperature gradient change (TG) of temperature signal is between 1.5℃ / s and 3.0℃ / s. When at least 3 of the above 4 characteristics meet the range, and the classification confidence is ≥95%, it is judged as normal wear. Severe wear stage: Total harmonic distortion (THD) of vibration signal is greater than 6.0%, root mean square value (RMS) of acoustic emission signal is greater than 1.2V, frequency domain energy ratio (FE) of cutting force signal is greater than 55%, and temperature gradient change (TG) of temperature signal is greater than 3.0℃ / s; when at least 3 of the above 4 characteristics meet this condition and the classification confidence is ≥95%, it is judged as severe wear.

[0008] The process parameter adjustment instructions are generated according to the following explicit rules based on different combinations of tool wear, cutting stability, and material removal status, ensuring that those skilled in the art can implement them directly: Parameter adjustment based on tool wear condition: Initial wear: Reduce spindle speed by 5%-10% from the current operating value (e.g., adjust to 5400-5700 rpm if the current speed is 6000 rpm); reduce feed rate by 8%-12% (e.g., adjust to 0.088-0.092 mm / rpm if the current feed rate is 0.1 mm / rpm); maintain coolant flow rate at the current value or increase it by 5%; maintain depth of cut at the current setting. Normal wear: Spindle speed, feed rate, and depth of cut maintain the current optimized parameters; coolant flow rate is dynamically adjusted according to pulse width modulation rules based on feedback data from the infrared temperature sensor. Severe wear: Spindle speed decreases by 15%-20% (e.g., if the current speed is 5000 rpm, adjust to 4000-4250 rpm); feed rate decreases by 18%-25% (e.g., if the current feed is 0.2 mm / rpm, adjust to 0.15-0.164 mm / rpm); depth of cut decreases by 0.02-0.05 mm (e.g., if the current depth of cut is 0.3 mm, adjust to 0.25-0.28 mm); at the same time, the CNC system issues a tool change warning signal. If severe wear is still detected for 3 consecutive sampling cycles (100ms per cycle), machining will be automatically paused, waiting for manual tool change.

[0009] Parameter adjustment based on cutting stability: Steady state: Maintaining the current process parameters unchanged; Slight chatter: Adjust the spindle speed by ±10%-15% (prioritizing adaptation to the direction away from the chatter frequency; for example, if the detected chatter frequency corresponds to a speed of 3000 rpm, if the current speed is 2800 rpm, adjust it to 3120-3220 rpm; if the current speed is 3200 rpm, adjust it to 2720-2880 rpm); reduce the feed rate by 10%-15%; decrease the depth of cut by 0.01-0.03 mm. Severe chatter: Adjust spindle speed by ±15%-25%; reduce feed rate by 20%-30%; increase coolant flow rate by 15%-25% (e.g., if the current flow rate is 10 liters / minute, adjust to 11.5-12.5 liters / minute); if the spindle does not return to a stable state within one sampling cycle (100ms) after adjustment, suspend machining and provide chatter alarm information.

[0010] Parameter adjustment based on material removal status: Normal state: Maintain the current process parameters unchanged; Overcut: Reduce the depth of cut by 0.02-0.06mm (for example, if the current depth of cut is 0.2mm, adjust it to 0.14-0.18mm); reduce the feed rate by 12%-18%; if the spindle speed is higher than 3000 rpm, reduce it by 10%-15%, and if it is lower than 3000 rpm, keep it unchanged; Undercutting: Increase the depth of cut by 0.01-0.03mm (for example, if the current depth of cut is 0.15mm, adjust it to 0.16-0.18mm); increase the feed rate by 5%-10%; increase the spindle speed by 8%-12% (maximum not exceeding 8000 rpm).

[0011] Step 3: Execute industrial-grade multi-process collaborative control and error compensation for precision assurance (core of industrial control execution layer). Based on the adjustment instructions output in Step 2, the spindle speed, feed rate, depth of cut, and coolant flow rate are dynamically corrected through the programmable logic controller (PLC) built into the CNC system. The spindle speed adjustment range is 500 to 8000 revolutions per minute, and the feed rate adjustment range is 0.05 mm to 0.3 mm per revolution, ensuring that the machining process is always in a high-precision and stable state according to industrial control standards. Step 4: Implement online processing quality assessment and industrial-grade feedback closed loop (core of industrial control closed loop). After each process is completed, use a non-contact optical measurement probe integrated on the machine tool to perform online dimensional detection and surface roughness assessment of the processing features, and feed the measurement data back to the neural network model in Step 2 for model parameter update, forming a "perception-decision-execution-feedback" closed-loop precision control system that conforms to industrial control standards.

[0012] Preferably, the multi-type sensor array in step 1 includes three triaxial accelerometers, two acoustic emission sensors, one six-dimensional force sensor, and four infrared temperature sensors. The accelerometers have a frequency response range of 0.5 Hz to 10 kHz, the force sensors have a range of ±5000 N, and the temperature sensors have a measurement accuracy of ±0.5 degrees Celsius. All sensors communicate with the CNC system via the industrial Ethernet protocol, with a network latency of less than 1 millisecond, ensuring the real-time performance and reliability of the sensed data and supporting high-precision machining control.

[0013] Preferably, in step 1, the vibration signal analysis adopts the empirical mode decomposition algorithm, which decomposes the original vibration signal into 8 intrinsic mode functions, and calculates the sample entropy of the 3rd to 5th intrinsic mode functions as a feature index to characterize the stability of the processing process and provide a quantitative basis for identifying abnormal states in precision machining.

[0014] Preferably, the tool wear degree judgment in step 2 is based on multi-source sensing data fusion, specifically including extracting the total harmonic distortion of the vibration signal, the root mean square value of the acoustic emission signal, the frequency domain energy ratio of the cutting force signal, and the gradient change characteristics of the temperature signal. The tool state is divided into three stages: initial wear, normal wear, and severe wear by a support vector machine classifier, with a classification confidence level of more than 95%, providing a reliable basis for tool state for high-precision machining.

[0015] Preferably, in step 3, when dynamically correcting the process parameters, for precision machining scenarios of easily deformable structures such as thin-walled hardware parts, an additional cutting force prediction model based on finite element analysis is introduced. This model predicts the workpiece deformation during the machining process by solving the elastic mechanical equilibrium equation in real time, and adaptively adjusts the cutting parameters accordingly, controlling the maximum deformation within 50% of the tolerance range, effectively ensuring geometric accuracy.

[0016] Preferably, in step 3, the coolant flow control adopts pulse width modulation technology. Based on the real-time temperature data fed back by the infrared temperature sensor, the duty cycle of the coolant solenoid valve is dynamically adjusted to keep the temperature of the processing area stable at least 30 degrees Celsius below the material recrystallization temperature, so as to avoid the impact of thermal deformation on the processing accuracy.

[0017] Preferably, in step 4, the non-contact optical measurement probe is a confocal chromatic displacement sensor with a measurement accuracy of ±1 micrometer and a repeatability error of less than 0.5 micrometers. The measurement data is read in real time through the macro program interface of the machine tool CNC system and compared with the theoretical values ​​in the standard process database to achieve online quality monitoring with micrometer-level accuracy.

[0018] Preferably, the online dimensional detection in step 4 includes geometric measurement of 12 common processing features such as hole diameter, groove width, and step height. No less than 50 measurement points are collected for each feature. The actual contour is fitted using the least squares method, and the deviation between it and the theoretical model is calculated. When the deviation exceeds half of the tolerance band width, the process parameter re-optimization process is triggered to ensure that the processing results always meet the precision tolerance requirements.

[0019] Preferably, it also includes establishing a processing knowledge base and an adaptive learning mechanism, storing the perception data, adjustment instructions and final quality assessment results of each processing process in a relational database, and using an incremental learning algorithm to periodically update the neural network model in step 2, so that the system can continuously optimize and adapt to the high-precision processing requirements of new materials, new tools and new workpiece structures.

[0020] Preferably, the method is integrated into a flexible manufacturing unit, which realizes automatic loading and unloading of workpieces through industrial robots. The robot's repeatability positioning accuracy is ±0.02 mm, and it interacts with CNC machine tools in real time via PROFIBUS bus to achieve fully automated precision machining from blank to finished product. The single-piece processing cycle is shortened by more than 30%, while ensuring the consistency accuracy of batch products.

[0021] Compared with existing technologies, the innovation of this invention lies in the deep integration of a multi-sensor perception system with a specific configuration, a high-precision deep learning state recognition model, and a multi-process collaborative control strategy to construct a closed-loop precision machining system with real-time perception, intelligent decision-making, and dynamic feedback capabilities. This technical solution not only overcomes the limitations of traditional fixed process parameter libraries and offline programming modes in precision machining, but also significantly improves the dimensional accuracy, geometric tolerance control capabilities, and process stability of multi-variety, small-batch hardware parts processing through a model-driven adaptive control mechanism. Simultaneously, through multi-process collaborative control and online error compensation mechanisms, it effectively suppresses thermal and elastic deformation in the processing of thin-walled irregular-shaped parts, reducing manual intervention and the need for secondary finishing. Overall processing efficiency, yield, and product consistency are substantially improved, fully demonstrating the technical relevance and engineering application value of this invention in the field of CNC precision machining. Attached Figure Description

[0022] The invention will now be further described with reference to the accompanying drawings.

[0023] Figure 1This is a flowchart of a CNC precision machining method for hardware products proposed in this invention; Figure 2 This is a schematic diagram of the core principle framework of the dynamic process parameter adaptive adjustment mechanism based on multi-source sensing data in this invention; Figure 3 This is a logical flowchart of the multi-process collaborative control and error compensation for accuracy assurance in this invention. Detailed Implementation

[0024] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0025] Reference Appendix Figure 1 The schematic diagram of the overall technical solution architecture of the CNC precision machining method for hardware products proposed in this invention clearly illustrates the complete closed-loop process from state perception and intelligent decision-making to execution control and feedback evaluation. The core of this architecture lies in constructing a highly integrated perception-decision-execution-feedback system to achieve dynamic control and precision assurance throughout the entire CNC precision machining process of hardware products. Specifically, the method includes the following four core steps, each containing multiple engineering implementation details and technical features, collectively forming a collaborative precision machining system.

[0026] In the aforementioned CNC precision machining method for hardware products, step 1 involves constructing a state-sensing system for precision machining. This involves deploying multi-type sensor arrays at the CNC machine tool spindle, turret, and workpiece clamping positions to collect vibration signals, acoustic emission signals, the three-dimensional components of cutting force, and temperature field distribution data in real time during the machining process. These data are then synchronously acquired and converted from analog to digital using a high-speed data acquisition card at a sampling frequency of at least 100 kHz, providing high-fidelity input for subsequent precision control. Specifically, the multi-type sensor array includes three triaxial accelerometers, two acoustic emission sensors, one six-dimensional force sensor, and four infrared temperature sensors. The triaxial accelerometers are deployed in the spindle housing, turret base, and workpiece fixture base to monitor structural vibrations generated by cutting force excitation during machining. Their frequency response range is 0.5 Hz to 10 kHz, ensuring coverage of vibration characteristics across the entire frequency range, from low-frequency chatter to high-frequency tool breakage. An acoustic emission sensor is installed at the flange connecting the turret and spindle to capture high-frequency elastic wave signals generated by microcrack propagation and plastic deformation during material removal. Its effective detection frequency band is 20 kHz to 1 MHz. A six-dimensional force sensor is integrated inside the turret to directly measure the force components (Fx, Fy, Fz) and their corresponding torque components (Mx, My, Mz) acting on the tool during cutting. Its range is ±5000 N, with a resolution of 0.1 N, enabling precise quantification of instantaneous changes in cutting load. Four infrared temperature sensors are positioned on the upper, lower, and side surfaces of the workpiece, as well as near the cutting edge of the tool, to construct a two-dimensional temperature field distribution map of the machining area. Their measurement accuracy is ±0.5 degrees Celsius, and the response time is less than 10 milliseconds. All sensors communicate with the CNC system via the industrial Ethernet protocol, with a network latency of less than 1 millisecond, ensuring the real-time performance and reliability of the sensed data and supporting high-precision machining control. The high-speed data acquisition card adopts a 16-bit resolution, 8-channel synchronous sampling architecture, with a sampling frequency configurable to 100 kHz, 200 kHz, or 500 kHz to meet the data fidelity requirements of different processing scenarios. The acquired raw analog signals are subjected to anti-aliasing filtering and then analog-to-digital conversion. A timestamp alignment mechanism is used to achieve strict synchronization of multi-source signals, with a time synchronization error of less than 1 microsecond. Furthermore, the vibration signal analysis in step 1 employs an empirical mode decomposition algorithm, decomposing the raw vibration signal into eight intrinsic mode functions (EMFs). The sample entropy of the 3rd to 5th EMFs is calculated as a feature index to characterize the stability of the processing process, providing a quantitative basis for identifying abnormal states in precision machining. The empirical mode decomposition algorithm adaptively decomposes non-stationary and nonlinear vibration signals into a series of physically meaningful EMFs through an iterative screening process. Each EMF represents the oscillation mode of the signal at a specific time scale. Sample entropy is used to measure the complexity and regularity of the time series; a smaller value indicates a more regular and stable signal.By calculating the sample entropy of the 3rd to 5th intrinsic mode functions, the changes in mid-frequency dynamic characteristics caused by tool wear, chatter, or material inhomogeneity during the machining process can be effectively captured, providing highly discriminative feature inputs for subsequent state recognition.

[0027] In the aforementioned CNC precision machining methods for hardware products, such as Figure 2 As shown, step 2 establishes a dynamic adaptive adjustment mechanism for process parameters based on multi-source sensing data. A hybrid model combining an 8-layer convolutional neural network and a long short-term memory network is used to extract features and identify the state of the collected data. This model determines tool wear, cutting stability, and material removal status in real time, and generates process parameter adjustment instructions to improve machining accuracy. Specifically, the hybrid model contains four convolutional layers, two pooling layers, and two long short-term memory layers. The convolutional kernel size is 3×3, and the activation function is a modified linear unit. The model is trained using a dataset containing 100,000 machining state samples, with a training cycle of 200 rounds. The final model achieves a classification accuracy of 99.2% on the test set. The input to this hybrid model is a preprocessed multi-source sensing data sequence, including the time-domain spectrogram of vibration signals, the envelope spectrum of acoustic emission signals, the time-domain waveform and frequency-domain energy distribution of cutting force signals, and the time-series data of the temperature field. The four convolutional layers are stacked sequentially to automatically extract local spatial features from the input data. Each convolutional layer is followed by a modified linear unit activation function to introduce nonlinear expressive power. Two pooling layers employ max pooling to reduce the spatial dimensionality of the feature maps, enhancing the model's translation invariance and reducing computational cost. Subsequently, two long short-term memory (LSM) layers process the feature sequences extracted through convolution, capturing the long-term dependencies and dynamic evolution of processing states over time. The LSM layers have 128 hidden units, effectively memorizing historical processing state information for more accurate judgment of the current state. The model's output layer is a fully connected layer, with the number of neurons corresponding to the number of predefined state categories, including tool wear degree (initial wear, normal wear, severe wear), cutting stability (stable, slight chatter, severe chatter), and material removal state (normal, overcut, undercut). During inference, the model processes the real-time perceived data stream in a sliding window of 10 milliseconds, outputting the state recognition result and corresponding confidence score every 100 milliseconds.

[0028] The degree of tool wear is determined through a dual verification process using multi-source feature quantization thresholds and classifier confidence. Feature data preprocessing: The total harmonic distortion (THD) of the vibration signal, the root mean square value (RMS) of the acoustic emission signal, the frequency domain energy ratio (FE) of the cutting force signal, and the gradient change (TG) of the temperature signal are normalized to eliminate the influence of differences in the measurement range of different sensors. Threshold comparison: The preprocessed feature values ​​are compared with the preset threshold range, and at least 3 features are matched to the corresponding stage range; Confidence verification: The confidence level of the state output by the support vector machine classifier must be ≥95% before the wear stage can be finally confirmed; Dynamic calibration: After processing 100 workpieces, the system automatically calls up historical data in the processing knowledge base to perform dynamic calibration of ±5% on the threshold range to ensure adaptation to the wear characteristics differences of different batches of tools.

[0029] The process parameter adjustment instructions are parsed and executed by the programmable logic controller built into the CNC system according to the following priority and execution sequence: Priority order: Cutting stability adjustment > Tool wear adjustment > Material removal status adjustment. When multiple statuses are triggered simultaneously, this priority order shall be followed. Subsequent adjustments shall be superimposed on the previous adjustments. Execution timing: After receiving the adjustment command, the response time for spindle speed adjustment is ≤50ms, the response time for feed rate adjustment is ≤30ms, the response time for depth of cut adjustment is ≤20ms, and the response time for coolant flow rate adjustment is ≤10ms. Boundary limits: All parameter adjustments must not exceed the preset range (spindle speed 500-8000 rpm, feed rate 0.05-0.3 mm / rpm, etc.). If the adjustment command exceeds the range, it will be executed according to the boundary value and an exception log will be recorded. Verification feedback: After the adjustment is executed, the model will re-detect the state within one sampling period. If the state does not improve, it will be adjusted again by 50% of the original adjustment range, up to a maximum of 3 consecutive adjustments. If the state still does not improve, the processing will be suspended.

[0030] Furthermore, the tool wear determination in step 2 is based on multi-source sensing data fusion, specifically including extracting the total harmonic distortion (THD) of the vibration signal, the root mean square (RMS) value of the acoustic emission signal, the frequency domain energy ratio of the cutting force signal, and the gradient change characteristics of the temperature signal. A support vector machine classifier is used to classify the tool state into three stages: initial wear, normal wear, and severe wear, with a classification confidence level greater than 95%, providing a reliable basis for high-precision machining. The THD quantifies the degree of distortion of harmonic components in the vibration signal relative to the fundamental wave; its value increases with increasing tool wear. The RMS value of the acoustic emission signal reflects the intensity of energy release during material removal and has a non-linear relationship with the tool wear state. The frequency domain energy ratio of the cutting force signal refers to the proportion of energy in a specific frequency band (e.g., 0 to 500 Hz) to the total energy; this proportion changes significantly during severe tool wear. The gradient change characteristics of the temperature signal refer to the rate of temperature change in the machining area over time; an abnormal increase in this rate often indicates tool failure or deterioration of cutting conditions. These manually extracted features are fused with features automatically learned by deep learning models, and the final decision is made through a support vector machine classifier, forming a dual guarantee mechanism to further improve the robustness and accuracy of tool state recognition.

[0031] In the aforementioned CNC precision machining methods for hardware products, such as Figure 3As shown, step 3 involves multi-process collaborative control and error compensation for precision assurance. Based on the adjustment commands output in step 2, the spindle speed, feed rate, depth of cut, and coolant flow rate are dynamically corrected by the programmable logic controller (PLC) built into the CNC system. The spindle speed adjustment range is 500 to 8000 revolutions per minute, and the feed rate adjustment range is 0.05 mm to 0.3 mm per revolution, ensuring that the machining process remains in a high-precision and stable state. Specifically, the PLC receives the status recognition results and adjustment commands from step 2, parses them into specific process parameter corrections, and sends them to the corresponding actuators through the real-time control interface of the CNC system. The spindle speed adjustment is achieved through a frequency converter with a response time of less than 50 milliseconds, enabling rapid tracking of command changes. The feed rate adjustment is achieved by controlling the speed of the feed axis motor through a servo driver, achieving a position control accuracy of 0.1 micrometers. The depth of cut adjustment is achieved through micro-motion control of the Z-axis servo motor, enabling fine adjustments at the 0.001 mm level during the finishing stage. The coolant flow rate is controlled using pulse width modulation (PWM) technology. Based on real-time temperature data from an infrared temperature sensor, the duty cycle of the coolant solenoid valve is dynamically adjusted to keep the temperature in the processing area at least 30 degrees Celsius below the material recrystallization temperature, thus preventing thermal deformation from affecting processing accuracy. The PWM signal frequency is 1 kHz, and the duty cycle adjustment range is 0% to 100%, corresponding to a continuously adjustable coolant flow rate from 0 liters per minute to 20 liters per minute. Furthermore, in step 3, when dynamically correcting process parameters, a cutting force prediction model based on finite element analysis is introduced for precision machining of easily deformable structures such as thin-walled hardware. This model predicts the workpiece deformation during machining by solving the elastic equilibrium equation in real time and adaptively adjusts the cutting parameters accordingly, controlling the maximum deformation within 50% of the tolerance range, effectively ensuring geometric accuracy. This finite element analysis model is generated offline before machining based on the workpiece CAD model and material properties (such as elastic modulus and Poisson's ratio) and stored in the CNC system's memory. During machining, the model receives the current three-dimensional components of the cutting force in real time as boundary loads. An explicit integration algorithm is used to solve the deformation field within 10 milliseconds, predicting the displacement of key feature points on the workpiece. If the predicted deformation exceeds a preset threshold (i.e., 50% of the tolerance range), the system automatically reduces the feed rate or depth of cut until the deformation returns to a safe range. This mechanism effectively solves the elastic deformation problem of thin-walled parts under cutting forces, significantly improving their dimensional accuracy and geometric tolerance control capabilities.

[0032] In the aforementioned CNC precision machining method for hardware products, step 4 implements online quality assessment and feedback closed-loop. After each process is completed, a non-contact optical measurement probe integrated on the machine tool is used to perform online dimensional detection and surface roughness assessment of the machining features. The measurement data is then fed back to the neural network model in step 2 for model parameter updates, forming a closed-loop precision control system of "perception-decision-execution-feedback". Specifically, the non-contact optical measurement probe is a confocal chromatic displacement sensor with a measurement accuracy of ±1 micrometer and a repeatability error of less than 0.5 micrometers. The measurement data is read in real time through the macro program interface of the machine tool CNC system and compared with the theoretical values ​​in the standard process database to achieve online quality monitoring with micrometer-level precision. The confocal chromatic displacement sensor is installed next to the machine tool spindle and can directly scan and measure the workpiece after machining without changing the tool. Its working principle is based on the characteristic that light of different wavelengths focuses at different positions in a lens, and the distance of the measured point is determined by analyzing the spectral composition of the reflected light. The sensor's measurement range is 0.5 mm, and the scanning speed can reach 1000 points per second. Step 4, online dimensional detection, includes geometric measurements of 12 common machining features such as hole diameter, groove width, and step height. At least 50 measurement points are collected for each feature. The actual contour is fitted using the least squares method, and the deviation from the theoretical model is calculated. When the deviation exceeds half the tolerance band width, a process parameter re-optimization process is triggered to ensure that the machining results always meet precision tolerance requirements. For example, for a hole diameter feature with a tolerance of ±10 micrometers, if the online measurement results show an actual dimensional deviation of 6 micrometers, the system will determine it as a potential out-of-tolerance risk and immediately initiate the process parameter re-optimization process. This process will call the neural network model from step 2, combine the current measurement data with historical machining data, recalculate the optimal combination of process parameters, and apply it to the machining of the next workpiece. Furthermore, it includes establishing a machining knowledge base and an adaptive learning mechanism. The perceived data, adjustment instructions, and final quality assessment results of each machining process are stored in a relational database, and the neural network model from step 2 is periodically updated using an incremental learning algorithm, enabling the system to continuously optimize and adapt to the high-precision machining needs of new materials, new cutting tools, and new workpiece structures. The relational database uses a MySQL architecture and has dedicated tables for storing raw sensing data, feature vectors, status labels, adjustment instructions, measurement results, and workpiece information. The incremental learning algorithm is based on an online gradient descent strategy. Every 1000 new high-quality labeled samples are accumulated, the weights of the neural network model are fine-tuned, with a learning rate set to 0.001 to avoid catastrophic forgetting. This mechanism ensures that the system can continuously accumulate experience over time, improving its adaptability to new processing scenarios.

[0033] To further illustrate the technical effects and engineering implementation details of this invention, a specific application example is provided below. This example focuses on the precision machining of a thin-walled aluminum alloy hardware part used in the housing of high-end electronic devices. Its typical features include multiple through holes with a depth of 5 mm and a diameter of 2 mm, and an annular groove with a wall thickness of only 0.8 mm. The dimensional tolerance of this workpiece is ±5 micrometers, and the surface roughness requirement is Ra less than 0.8 micrometers.

[0034] Before machining begins, the system first loads the initial machining program for the workpiece from the standard process database, including parameters such as toolpath, initial spindle speed (6000 rpm), and initial feed rate (0.1 mm / rpm). The workpiece is clamped onto the worktable using a pneumatic fixture, with the clamping force monitored by a pressure sensor to ensure it remains within the range of 500 N to 800 N, thus preventing clamping deformation. After machining starts, the multi-type sensor array from step 1 begins synchronously acquiring data at a sampling frequency of 200 kHz. During the drilling process, the six-dimensional force sensor detects a sudden increase in the Z-axis cutting force from 800 N to 1200 N, while the total harmonic distortion of the vibration signal increases from 3% to 8%. The hybrid neural network model in step 2 identifies this state as "initial tool wear" within 100 milliseconds and outputs adjustment commands: reduce the spindle speed to 5500 rpm and the feed rate to 0.08 mm / rpm. Upon receiving the commands, the programmable logic controller immediately executes the parameter correction. In the subsequent annular groove milling process, due to the thinness of the workpiece wall, the maximum deformation predicted by the finite element analysis model reached 6 micrometers, exceeding the 50% tolerance threshold (i.e., 2.5 micrometers). The system immediately activated the deformation compensation mechanism, further reducing the feed rate to 0.06 mm per revolution and increasing the coolant flow rate to 15 liters per minute, stabilizing the temperature of the machining area at 120 degrees Celsius (the recrystallization temperature of aluminum alloy is 150 degrees Celsius). After machining, a confocal chromatic displacement sensor performed a full-size scan of the workpiece, collecting 800 measurement points. The least squares fitting results showed that the actual dimensional deviation of all apertures was within ±3 micrometers, the contour error of the annular groove was 4 micrometers, and the surface roughness Ra was 0.6 micrometers, fully meeting the design requirements. The measurement data was stored in the machining knowledge base and used for the next round of model incremental learning. The entire machining process achieved fully automated operation without human intervention, with a single-piece machining cycle of 8 minutes, which is 35% shorter than the traditional method, and the standard deviation of batch machining consistency accuracy is less than 1 micrometer.

[0035] Furthermore, the method can be integrated into a flexible manufacturing unit, enabling automated workpiece loading and unloading via industrial robots. The robots achieve a repeatability accuracy of ±0.02 mm and interact with CNC machine tools in real-time via a PROFIBUS bus, achieving fully automated precision machining from raw material to finished product. This reduces the single-piece machining cycle by more than 30% while ensuring consistent precision across batches. In this flexible manufacturing unit, the industrial robot picks up raw materials from the hopper, precisely places them on the CNC machine tool's worktable, and then places the finished product into the finished product hopper after machining. Communication between the robot and the CNC machine tool is achieved via a PROFIBUS bus with a communication cycle of 10 milliseconds, ensuring seamless integration of loading / unloading actions with the machining program. Upon receiving a signal from the robot indicating workpiece placement is complete, the CNC machine tool automatically starts the machining program; after machining, it sends a pick-up signal to the robot. This integrated solution not only improves production efficiency but also further ensures product consistency by reducing the uncertainties introduced by manual operation.

[0036] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for CNC precision machining of hardware products, characterized in that: Includes the following steps: Step 1: Construct a full-link status perception system for the machining process. Deploy multi-type sensor arrays at the CNC machine tool spindle, turret, and workpiece clamping position to collect vibration signals, acoustic emission signals, three-dimensional components of cutting force, and temperature field distribution data in real time during the machining process. The data is then collected synchronously and converted from analog to digital by a high-speed data acquisition card. Step 2: Establish a dynamic process parameter adaptive adjustment mechanism. Based on the multi-source sensing data collected in Step 1, a hybrid model of convolutional neural network and long short-term memory network is used for feature extraction and state recognition. The tool wear degree, cutting stability and material removal status are judged in real time, and process parameter adjustment instructions are generated. Step 3: Perform multi-process collaborative control and error compensation. Based on the adjustment instructions output in Step 2, dynamically correct the spindle speed, feed rate, depth of cut and coolant flow rate through the programmable logic controller built into the CNC system. Step 4: Implement online assessment and feedback loop for processing quality. After each process is completed, use a non-contact optical measurement probe integrated on the machine tool to perform online dimensional detection and surface roughness assessment of the processing features. Feed the measurement data back to the neural network model in Step 2 to update the parameters, forming a closed-loop control of perception-decision-execution-feedback.

2. The CNC precision machining method for hardware products according to claim 1, characterized in that: The multi-type sensor array in step 1 includes three triaxial accelerometers, two acoustic emission sensors, one six-dimensional force sensor, and four infrared temperature sensors; the accelerometer frequency response range is 0.5 Hz to 10 kHz, the force sensor range is ±5000 N, and the temperature sensor measurement accuracy is ±0.5 degrees Celsius; all sensors communicate with the CNC system via the industrial Ethernet protocol, with a network latency of less than 1 millisecond, and the high-speed data acquisition card sampling frequency is not less than 100 kHz.

3. The CNC precision machining method for hardware products according to claim 2, characterized in that: In step 1, the vibration signal analysis adopts the empirical mode decomposition algorithm, which decomposes the original vibration signal into 8 intrinsic mode functions. The sample entropy of the 3rd to 5th intrinsic mode functions is calculated as a feature index to characterize the stability of the processing.

4. The CNC precision machining method for hardware products according to claim 1, characterized in that: The hybrid model of convolutional neural network and long short-term memory network in step 2 includes 4 convolutional layers, 2 pooling layers and 2 long short-term memory layers; the convolutional kernel size is 3×3, and the activation function is a modified linear unit; the model is trained using a dataset of 100,000 processing state samples, with a training cycle of 200 rounds.

5. The CNC precision machining method for hardware products according to claim 4, characterized in that: In step 2, the tool wear degree is determined based on multi-source sensing data fusion: the total harmonic distortion of the vibration signal, the root mean square value of the acoustic emission signal, the frequency domain energy ratio of the cutting force signal, and the gradient change characteristics of the temperature signal are extracted. The tool state is divided into three stages: initial wear, normal wear, and severe wear by a support vector machine classifier.

6. The CNC precision machining method for hardware products according to claim 1, characterized in that: In step 3, when dynamically correcting the process parameters, a cutting force prediction model based on finite element analysis is introduced for the machining of thin-walled hardware parts. This model predicts the workpiece deformation by solving the elastic mechanical equilibrium equation in real time, and adaptively adjusts the cutting parameters accordingly to control the maximum deformation within 50% of the tolerance range. The spindle speed adjustment range is 500-8000 rpm, and the feed rate adjustment range is 0.05-0.3 mm / rpm.

7. A CNC precision machining method for hardware products according to claim 6, characterized in that: In step 3, the coolant flow control adopts pulse width modulation technology. Based on the real-time temperature data fed back by the infrared temperature sensor, the duty cycle of the coolant solenoid valve is dynamically adjusted to keep the temperature in the processing area stable at least 30 degrees Celsius below the material recrystallization temperature.

8. The CNC precision machining method for hardware products according to claim 1, characterized in that: In step 4, the non-contact optical measurement probe is a confocal chromatic displacement sensor with a measurement accuracy of ±1 micrometer and a repeatability error of less than 0.5 micrometers. The measurement data is read in real time through the macro program interface of the machine tool CNC system and compared with the theoretical values ​​in the standard process database.

9. A method for CNC precision machining of hardware products according to claim 8, characterized in that: In step 4, online dimensional detection includes geometric measurements of 12 common machining features such as hole diameter, groove width, and step height. At least 50 measurement points are collected for each feature, and the actual contour is fitted using the least squares method to calculate the deviation from the theoretical model. When the deviation exceeds half of the tolerance band width, the process parameter re-optimization process is triggered.

10. A CNC precision machining method for hardware products according to claim 1, characterized in that: It also includes establishing a processing knowledge base and an adaptive learning mechanism: storing the perception data, adjustment instructions and final quality assessment results of each processing process into a relational database, and using an incremental learning algorithm to periodically update the neural network model in step 2; the method is integrated into a flexible manufacturing unit, and the automatic loading and unloading of workpieces is achieved through industrial robots, with a robot repeatability accuracy of ±0.02 mm, and real-time data interaction with CNC machine tools via PROFIBUS bus.

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