Analysis of Materials Expansion Properties for Computation of Thermal Error Compensation Values for Machine Tool Applications

Abstract

One of the major causes of the total geometric inaccuracy of the machine is the thermo-mechanical error due to the deformation of machine tools, which is caused by both internal and external heat sources. Understanding the factors driving this is crucial to bring down errors to negligible values on machine tools. There are many different thermal factors, and it is a combination of all of these influences and their histories that determines the actual temperature on the distribution on the elements of machine tools. The expansion properties of the machine tool elements are analyzed in the computation of thermal expansion of these elements. Neural network as a part of artificial intelligence is widely used for this type of application as the data captured from the process is highly nonlinear. Giving the right data for the neural network training is at most important as this decides about the quality of neural network training. As the data is huge enough considering various conditions existing in the machining environment, the proper data pre-processing only will make the training much more effective. This paper’s main aim is to study the thermal expansion properties by properly analyzing the data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.