Study of Thermal Sensitive Point Simulation and Cutting Trial of Five Axis Machine Tool Based on Genetic Algorithm

Abstract

The best heat source point selection method for multi axis machine tool is optimized. The selection of the heat source is accomplished using the wavelet compression technique and genetic algorithm. To test and simulate a large number of temperature data for the binary digital encoding, and the temperature data of gray image of the adjacent brightness difference between the compression target, the image data of the temperature of the multi wavelet compression. Reduce the temperature data redundancy, the final selection of 1024 node data for computing nodes. Using genetic optimization algorithm, the objective function is to measure the residuals between the value and the model value. The temperature image data is optimized for 650 generations, and the locations of the 4 best sensitive heat source is obtained, and the results are in line with the engineering requirements.