CN-121042990-B - Self-adaptive grinding system and method for linear guide rail sliding block channel

CN121042990BCN 121042990 BCN121042990 BCN 121042990BCN-121042990-B

Abstract

The invention relates to the technical field of guide rail slide block machining, in particular to a self-adaptive grinding system and a self-adaptive grinding method for a linear guide rail slide block channel, comprising a monitoring unit, a decision unit and an execution unit, wherein the monitoring unit is used for collecting micro-displacement data and acoustic emission data of a slide block workpiece in the grinding process, the decision unit is connected with the monitoring unit, and is used for calculating through a digital twin simulation module and a wear prediction and optimization module according to the received micro-displacement data and acoustic emission data, outputting optimized grinding parameters and compensation instructions, and the execution unit is connected with the decision unit, and realizes dynamic perception, accurate prediction and self-adaptive control of the grinding process through integrating high-precision micro-displacement and acoustic emission real-time monitoring, intelligent decision based on a digital twin and wear prediction model and an active vibration and thermal compensation execution mechanism, thereby improving the final machining precision, surface quality and intelligent level and long-term stability of the whole machining system of the linear guide rail slide block channel.

Inventors

SHI YINGJIAN
LUO ZENGHUI
ZHANG GUOQUAN
WANG LONGYU
Zuo Anwen
YAO HONG
CUI GANG
ZHAO XIN
LI ZHANFENG

Assignees

陕西蓝海秦工科技有限公司

Dates

Publication Date: 20260508
Application Date: 20251014

Claims (10)

1. A self-adaptive grinding system for a linear guide rail slide block channel is characterized by comprising, The monitoring unit (100) is used for collecting micro-displacement data and acoustic emission data of the sliding block workpiece in the grinding process; The decision unit (200) is connected with the monitoring unit (100), and the decision unit (200) is used for calculating through the digital twin simulation module (201) and the wear prediction and optimization module (202) according to the received micro-displacement data and acoustic emission data and outputting optimized grinding parameters and compensation instructions; the execution unit (300) is connected with the decision unit (200), and the execution unit (300) is used for driving the grinding mechanism, the compensation mechanism and the cooling mechanism according to the received grinding parameters and the received compensation instruction so as to finish the self-adaptive grinding of the slide block workpiece; the digital twin simulation module (201) is used for receiving the micro-displacement data and the acoustic emission data, performing grinding force-thermal coupling effect simulation, and outputting predicted dimensional deviation and thermal deformation; The abrasion prediction and optimization module (202) is connected with the digital twin simulation module (201), the abrasion prediction and optimization module (202) is integrated with an LSTM model (202 a) trained based on a federal learning framework, the LSTM model (202 a) analyzes the spectral entropy value characteristics of acoustic emission signals to predict the abrasion state of the grinding wheel, and the output of the digital twin simulation module (201) is combined to dynamically calculate the optimal grinding parameters; the execution unit (300) includes: The piezoelectric ceramic-disc spring composite supporting module (301) comprises an annular piezoelectric ceramic array (301 a) integrated in the center of the disc spring group, wherein the piezoelectric ceramic array (301 a) receives a compensation instruction and generates high-frequency micro-vibration with the frequency of 5-20kHz and the amplitude of 0.1-0.5 mu m; The micro-channel cooling module (302) is embedded in the high-molecular damping pad, the micro-channel cooling module (302) is of a tree fractal structure, the channel width is 200+/-10 mu m, and cooling liquid containing 5wt% of Al 2 O 3 nano particles is introduced.
2. The linear guide rail slider channel adaptive grinding system of claim 1 wherein said monitoring unit (100) comprises: a laser interferometry module (101) which adopts a helium-neon laser source with the wavelength of 632.8nm and emits four paths of differential beams to monitor the micro-displacement of the slide block workpiece in three directions of X, Y, Z; And the acoustic emission sensing module (102) synchronously collects acoustic emission data generated in the grinding process and emits acoustic emission signals.
3. The linear guide slider channel adaptive grinding system of claim 2 wherein said LSTM model (202 a) comprises: an input layer (202 a-1) that receives multi-dimensional monitoring data; a hidden layer (202 a-2) containing 128 LSTM units, capturing time series characteristics; And an output layer (202 a-3) for outputting the predicted value of the abrasion loss of the grinding wheel.
4. The linear guide slider channel adaptive grinding system of claim 3 wherein said annular piezoceramic array (301 a) has a diameter of 8.+ -. 0.1mm.
5. The linear guide rail slider channel adaptive grinding system of claim 4 wherein said decision unit (200) is further configured to: When the predicted dimensional deviation of the digital twin simulation module (201) is larger than 0.3 mu m, the rotating speed of the grinding wheel is dynamically adjusted, the adjusting range is +/-100 rpm, and/or the feeding amount is dynamically adjusted, and the adjusting range is 0.05-0.2 mm/min.
6. The self-adaptive grinding method for the linear guide rail slide block channel is applied to the self-adaptive grinding system for the linear guide rail slide block channel, and is characterized in that: placing a linear guide rail slide block workpiece on a self-adaptive grinding fixture; The monitoring unit (100) monitors and collects micro-displacement of the sliding block workpiece in the grinding process and synchronously collects acoustic emission signals generated in the grinding process; inputting the micro-displacement and acoustic emission signal data acquired in real time into a digital twin simulation module (201) to predict the current grinding state and the possible size deviation; analyzing the characteristics of acoustic emission signals such as frequency spectrum entropy values, predicting the abrasion state of the grinding wheel, and calculating optimal grinding parameters; Driving the piezoelectric ceramic array (301 a) to generate high-frequency micro-vibration to inhibit vibration patterns, and controlling the micro-channel cooling system to control temperature so as to compensate thermal deformation; According to the optimal grinding parameters, the rotating speed and the feeding amount of the grinding wheel are dynamically adjusted, and precise grinding of the workpiece channel of the sliding block is completed; Uploading the processing data to a federal learning server, aggregating with other equipment data in the cluster, updating and optimizing an LSTM wear prediction model; the step of inputting the data acquired in real time into the digital twin simulation module (201) for simulation is to perform grinding force-thermal coupling effect simulation so as to predict the current grinding state, size deviation and thermal deformation; The acoustic emission signal is analyzed to predict the abrasion state of the grinding wheel, specifically, the spectral entropy value characteristic of the acoustic emission signal is extracted and input into an LSTM model (202 a) trained based on a federal learning framework for analysis and prediction; The step of uploading the processing data to the federal learning server is that each device encrypts the parameters or gradients of the local LSTM model (202 a) subjected to desensitization treatment and then uploads the encrypted parameters or gradients; the method comprises the steps that an LSTM wear prediction model is updated and optimized, specifically, a cloud aggregation server aggregates parameters or gradients from a plurality of devices in a cluster by adopting a federal average algorithm to generate an optimized global model, and the global model parameters are issued to each device for subsequent prediction; the method also comprises the following steps: Triggering the step of calculating optimal grinding parameters and compensating instructions when the dimensional deviation predicted by the digital twin simulation module (201) is greater than 0.3 μm.
7. The method for adaptively grinding the linear guide rail slide block channel according to claim 6, wherein the method is characterized in that the micro-displacement of the slide block workpiece in the grinding process is monitored and collected, and particularly the micro-displacement of the slide block workpiece in three directions X, Y, Z is monitored in real time by emitting four differential beams through a laser interferometry module (101) with the wavelength of 632.8 nm.
8. The method for adaptively grinding the linear guide rail slide block channel according to claim 7, wherein the calculated optimal grinding parameters comprise a grinding wheel rotating speed and a feeding amount; The adjusting range of the rotating speed of the grinding wheel is +/-100 rpm, and the adjusting range of the feeding amount is 0.05-0.2 mm/min.
9. The method for adaptively grinding a linear guide rail sliding block channel according to claim 8, wherein the piezoelectric ceramic array (301 a) generates high-frequency micro-vibration to drive the annular piezoelectric ceramic array (301 a) integrated in the center of the disc spring group, the high-frequency micro-vibration with the frequency of 5-20kHz and the amplitude of 0.1-0.5 μm is generated, and the diameter of the annular piezoelectric ceramic array (301 a) is 8+/-0.1 mm.
10. The method for adaptively grinding a linear guide rail sliding block channel according to claim 9, wherein the control micro-channel cooling system controls the temperature, in particular the flow of cooling liquid in a tree-shaped fractal micro-channel embedded in a high-molecular damping pad; The cooling liquid contains 5wt% of Al 2 O 3 nano particles, and the channel width is 200+/-10 mu m.

Description

Self-adaptive grinding system and method for linear guide rail sliding block channel Technical Field The invention relates to the technical field of guide rail slide block machining, in particular to a self-adaptive grinding system and a self-adaptive grinding method for a linear guide rail slide block channel. Background The linear guide rail is used as a core component of a precise transmission system, the machining precision of a slide block channel of the linear guide rail directly determines the running stability, rigidity and service life of the guide rail, and at present, the conventional grinding machining method of the slide block channel of the linear guide rail generally depends on preset fixed parameters, and lacks the capability of monitoring and compensating machining deviation caused by dynamic factors such as abrasion, thermal deformation, vibration and the like of a grinding wheel in real time in the grinding process, so that the machining precision, consistency and surface quality are difficult to further improve. Therefore, a self-adaptive grinding system and a self-adaptive grinding method for a linear guide rail sliding block channel are provided. Disclosure of Invention Some simplifications or omissions may be made in this section as well as in the description of the application and in the title of the application, which may not be used to limit the scope of the application. To solve the defects in the prior art, one object of the invention is to provide a linear guide rail slide block channel self-adaptive grinding system In order to achieve the above-mentioned aim, the invention adopts the following technical scheme that the self-adaptive grinding system of the linear guide rail slide block channel comprises a monitoring unit, a decision unit and an execution unit, wherein the monitoring unit is used for collecting micro-displacement data and acoustic emission data of a slide block workpiece in the grinding process, the decision unit is connected with the monitoring unit, the decision unit is used for calculating through a digital twin simulation module and a wear prediction and optimization module according to the received micro-displacement data and acoustic emission data, outputting optimized grinding parameters and compensation instructions, and the execution unit is connected with the decision unit and is used for driving a grinding mechanism, a compensation mechanism and a cooling mechanism according to the received grinding parameters and compensation instructions so as to finish self-adaptive grinding of the slide block workpiece. The invention relates to a linear guide rail slide block channel self-adaptive grinding system, which is characterized in that the monitoring unit comprises a laser interferometry module, an acoustic emission sensing module and a grinding module, wherein the laser interferometry module adopts a helium-neon laser source with the wavelength of 632.8nm and emits four paths of differential beams, the micro-displacement of a slide block workpiece in X, Y, Z directions is monitored, and acoustic emission data generated in the grinding process are synchronously acquired and acoustic emission signals are emitted. The digital twin simulation module is used for receiving micro-displacement data and acoustic emission data, performing grinding force-thermal coupling effect simulation, and outputting predicted dimensional deviation and thermal deformation. The abrasion prediction and optimization module is connected with the digital twin simulation module, the abrasion prediction and optimization module is integrated with an LSTM model trained based on a federal learning framework, the LSTM model analyzes the spectral entropy characteristics of acoustic emission signals to predict the abrasion state of the grinding wheel, and the digital twin simulation module is combined with the output of the digital twin simulation module to dynamically calculate the optimal grinding parameters. The self-adaptive grinding system for the linear guide rail sliding block channel comprises an input layer, a hidden layer, an output layer and an abrasion loss prediction value of an output grinding wheel, wherein the input layer is used for receiving multi-dimensional monitoring data, the hidden layer comprises 128 LSTM units, the time sequence characteristics are captured, and the output layer is used for outputting the abrasion loss prediction value of the grinding wheel. The execution unit comprises a piezoelectric ceramic-disc spring composite supporting module, wherein the piezoelectric ceramic composite supporting module comprises an annular piezoelectric ceramic array integrated in the center of a disc spring group, the piezoelectric ceramic array receives compensation instructions and generates high-frequency micro-vibration with the frequency of 5-20kHz and the amplitude of 0.1-0.5 mu m, and a micro-channel cooling module is embedded in a high-molecular damping pad, is of a