Machine Tool Thermal Error Compensation Research Articles

Implementation of Precision Machine Tool Thermal Error Compensation in Edge-Cloud-Fog Computing Architecture

Abstract The implementation of precision machine tool thermal error compensation in edge-cloud-fog computing architecture has the potential to control the thermal error. However, the challenges faced by the successful implementation are described as follows: The data collection and transfer efficiency are low, and the control accuracy is not deficient. To address these challenges, a hardware design scheme is proposed for the high-performance intelligent gateway node based on the low-power processor architecture of ARM Cortex-A7. Moreover, a new transformer-improved-gate long short-term memory model is proposed, and then it is embedded into edge-cloud-fog computing architecture. With the implementation of gear profile grinding machine thermal error compensation in edge-cloud-fog computing architecture, the maximum values of the tooth profile tilt deviation are reduced from 17.4 μm to 5.4 μm and from 17.9 μm to 5.8 μm for the left and right tooth flanks, respectively. Moreover, the maximum values of the tooth profile deviation are reduced from 18.9 μm to 6.1 μm and from 18.2 μm to 5.8 μm for the left and right tooth flanks, respectively. Compared with the traditional collection mode, the response delay of the designed intelligent gateway in the acquisition mode is reduced by 40%.

ENHANCEMENT OF SPECIALISED VERTICAL TURNING LATHE ACCURACY THROUGH MINIMISATION OF THERMAL ERRORS DEPENDING ON TURNING AND MILLING OPERATIONS

Achieving high workpiece accuracy is a long-term goal of machine tool designers. There are many causes of workpiece inaccuracy, with thermal errors being the most dominant. Indirect compensation (using predictive models) is a promising strategy for reducing thermal errors without increasing machine tool cost. A modelling approach using thermal transfer functions (a dynamic method with a physical basis) embodies the potential to deal with this issue. The method does not require interventions into the machine tool structure, uses a minimum of additional gauges and its modelling and calculation speed is suitable for real-time applications with fine results with up to 80% thermal error reduction. Advanced machine tool thermal error compensation models have been successfully applied on various kinds of single-purpose machines (milling, turning, floor-type, etc.) and implemented directly into their control systems. This research reflects modern trends in machine tool usage and as such is focused on the applicability of the modelling approach to describe specialised vertical turning lathe versatility. The specialised vertical turning lathe is adequately capable of carrying out turning and milling operations. Calibration of the reliable compensation model is a real challenge. The applicability of the approach during immediate switching between turning and milling operations is discussed in more detail.

Prediction of Machine Tool Thermal Error Compensation Based on SVMR and ARM11

In order to improve the process precision of the machine tool, further development of SVMR was achieved by QT Creator. Support vector machine was applied to the ARM11 development board, SVMR model was online trained and real-time predicted the values of machine tool thermal error. Compared with the widely used BP neural network, this method has the characteristics of high compensation precision and strong generalization ability. Experiment research has proved that the stronger effectiveness and higher accuracy using this method.

Application of Ubiquitous Sensor Network in Collection and Analysis of CNC Machine Tool Processing Status Information

Because of the practical requirement of real-time collection and analysis of CNC machine tool processing status information, we discuss the necessity and feasibility of applying ubiquitous sensor network(USN) in CNC machine tools by analyzing the characteristics of ubiquitous sensor network and the development trend of CNC machine tools, and application of machine tool thermal error compensation based on USN is presented.

Optimal linear combination of neural networks to model thermally induced error of machine tools

In recent years, neural network (NN) modelling methods with different architectures and training strategies have been widely used in machine tool thermal error compensation field. Most of them are typically trial-and-error procedures in choosing the 'best network', while discarding all the rest. This paper introduces a new modelling method combining all of the trained NNs. This method is called optimal linear combination of neural networks (OLCNN), which can integrate the useful knowledge of the component NNs and thus improve model accuracy and generalisation ability (i.e., model robustness). Algorithms for selecting the component networks are presented after some associated critical issues are discussed. Real cutting experiments are conducted on a CNC turning machine to validate the effectiveness of the method. Analysis of different modelling conditions indicates that the best situation for using OLCNN is when the component networks are merely moderately trained. The modelling results show that significant improvement in the model accuracy and robustness is achieved comparing with single best NN and simple average of trained NNs. It is also applicable to hybrid different modelling methods other than NN.

Application of synthetic grey correlation theory on thermal point optimization for machine tool thermal error compensation

This paper presents two new methods to optimize the selection of minimum number of thermal sensors for machine tool thermal error compensation. The two methods, namely, direct criterion method and indirect grouping method, are based on the synthetic grey correlation theory. They are applied to analyze the data of an air cutting experiment on a CNC turning center. After optimization, the number of thermal points reduced from 16 to four. Thus, for machine tool thermal error modeling, the number of temperature variables is greatly reduced while coupling problems among temperature variables can be avoided. A real cutting experiment is then conducted to verify the efficiency of the presented optimization methods under practical manufacturing conditions. The comparison of the results between the model with all 16 thermal sensors and the model with four optimized thermal sensors indicates that, after optimization, the model accuracy is greatly improved.

Fault diagnosis and recovery for a CNC machine tool thermal error compensation system

Fault diagnosis and recovery for a CNC machine tool thermal error compensation system

Fault diagnosis and recovery for a CNC machine tool thermal error compensation system

The major role of temperature sensors in a machine tool thermal error compensation system is the improvement of machining accuracy through the supply of reliable temperature data on the machine structure. This paper presents a new method for the fault diagnosis of temperature sensors along with the recovery of faulty data, thereby protecting the reliability of a thermal error compensation system. The proposed method of detecting a fault and its location is based on the correlation coefficients among the temperature data produced by the sensors. Thereafter, a multiple linear regression model, prepared using the complete normal data, is used for the recovery of faulty data. The effectiveness of this method was tested by comparing computer simulation results with measured data from a CNC machining center.