Modeling of CNC Machine Tool Energy Consumption and Optimization Study Based on Neural Network and Genetic Algorithm

2012 ◽  
Vol 195-196 ◽  
pp. 770-776 ◽  
Author(s):  
Dong Xie ◽  
Guo Rong Chen ◽  
Feng Wang ◽  
Jian Qu Zhu
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Energy Consumption ◽  
Machine Tool ◽  
Cutting Speed ◽  
Cutting Parameters ◽  
Feed Speed ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Optimization Study

The issue of CNC machine tool energy consumption and environmental protection plays an important role on manufacturing technology research since CNC machine tool energy is consumed during motor racing or cutting process. The paper analyzes CNC machine tool energy consumption influence by cutting parameters of cutting speed, feed speed, cutting depth. Based on nonlinear mapping ability of neural network, the model of CNC machine tool energy consumption related to cutting parameters is established by using experimental data, and then the optimal combination of cutting parameters are searched by using global optimization of genetic algorithm, and verified in CNC machine tool cutting experiment. The proposed method provides a good energy control proposal for CNC machine tool roughing process. The experimental results show that the energy consumption is optimal.

scholarly journals DAMPING OF SELF-OSCILLATIONS AT LATHE TURNING BY SOFTWARE MODULATION OF CNC MACHINE-TOOL CUTTING SPEED

2018 ◽  
Vol 22 (12) ◽  
pp. 115-124
Author(s):  
Valery Svinin ◽  
◽  
Andrey Savilov ◽  
Anton Shutenkov ◽  
Mikhail Panin ◽  
...  
Keyword(s):  
Machine Tool ◽  
Cutting Speed ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Tool Cutting

Application of Partial Least Squares Neural Network in Thermal Error Modeling for CNC Machine Tool

2008 ◽  
Vol 392-394 ◽  
pp. 30-34 ◽  
Author(s):  
J.H. Shen ◽  
Jian Guo Yang
Keyword(s):  
Neural Network ◽  
Machine Tool ◽  
Least Squares ◽  
Partial Least Squares ◽  
Thermal Error ◽  
Error Modeling ◽  
Neural Network Modeling ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Thermal Error Modeling

This paper presents a partial least squares neural network modeling method for CNC machine tool thermal errors. This method uses the neural network learning rule to obtain the PLS parameters instead of the traditional linear method in partial least squares regression so as to overcome the multicollinearity and nonlinearity problem in thermal error modeling. The basic principle and architecture of PLSNN is described and the new method is applied on the thermal error modeling for a CNC turning center. After model validation with two groups of new testing data and performance comparison with other five different modeling methods, PLSNN performs better than the others with better robustness.

Research and Development of a CNC Machine Tool Dynamic Characteristics Test and Analysis System

2012 ◽  
Vol 562-564 ◽  
pp. 1352-1357
Author(s):  
Kai Tian ◽  
Qiang Liu ◽  
Song Mei Yuan ◽  
Yi Qing Yang
Keyword(s):  
Machine Tool ◽  
Dynamic Characteristics ◽  
Function Analysis ◽  
Cutting Parameters ◽  
Parameters Optimization ◽  
Cnc Machine Tool ◽  
Cnc Machine ◽  
Function Modules ◽  
Analysis System ◽  
Cutting Parameters Optimization

A dynamic characteristics test and analysis system for CNC machine tool is developed based on C++/Qt platform and NI-DAQmx data acquisition driver. The function modules include hammer experiment, signal continuous acquisition, spectral analysis, frequency response function analysis and signal coherence analysis, etc. The analysis results provide the basis of dynamics characteristics simulation and cutting parameters optimization. The system can be also used to analysis the vibration of the cutting process. Spectrum analysis modules help to assess the cutting stability and diagnose problems. Experiments show that the system has good accuracy and efficiency while the size and price are decreased compare with traditional equipment.

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