Modified Mechanistic Model Based on Gaussian Process Adjusting Technique for Cutting Force Prediction in Micro-End Milling

Xiaoping Liao,Zhenkun Zhang,Juan Lu,Kai Chen,Junyan Ma,Kang Li

doi:10.1155/2019/7468698

Xiaoping Liao, Zhenkun Zhang + Show 4 more

Open Access

https://doi.org/10.1155/2019/7468698

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Abstract

Micro-end milling is in common use of machining micro- and mesoscale products and is superior to other micro-machining processes in the manufacture of complex structures. Cutting force is the most direct factor reflecting the processing state, the change of which is related to the workpiece surface quality, tool wear and machine vibration, and so on, which indicates that it is important to analyze and predict cutting forces during machining process. In such problems, mechanistic models are frequently used for predicting machining forces and studying the effects of various process variables. However, these mechanistic models are derived based on various engineering assumptions and approximations (such as the slip-line field theory). As a result, the mechanistic models are generally less accurate. To accurately predict cutting forces, the paper proposes two modified mechanistic models, modified mechanistic models I and II. The modified mechanistic models are the integration of mathematical model based on Gaussian process (GP) adjustment model and mechanical model. Two different models have been validated on micro-end-milling experimental measurement. The mean absolute percentage errors of models I and II are 7.76% and 6.73%, respectively, while the original mechanistic model’s is 15.14%. It is obvious that the modified models are in better agreement with experiment. And model II performs better between the two modified mechanistic models.

Highlights

Important applications of miniature parts and devices are growing widely in the fields of watches, medical devices, electronics optics, and biotechnology, to mention a few
Based on the above research, to improve the prediction accuracy of the mechanistic model of cutting force during micro-end-milling, this paper proposes a modified mechanistic model based on Gaussian process
Due to the complexity of the micro-end-milling process, the mechanical model cannot describe the detailed characteristics between parameters and cutting forces and the paper proposes two modified mechanistic models integrating mechanistic model with mathematical model based on Gaussian process instead of using the mechanistic model singly to predict micro-end milling forces

Summary

Introduction

Important applications of miniature parts and devices are growing widely in the fields of watches, medical devices, electronics optics, and biotechnology, to mention a few. In micro-end-milling process, the machine and tool deflect based on the behavior of cutting forces, which impacts the geometrical accuracy. The overall magnitudes of predictive values obtained by these mechanistic models for cutting force match well the experiment values, the maximum errors and the mean absolute error are still large between predictive values and experiment values, because the cutting forces are closely related to various phenomena found during machining process (e.g., cutting temperature, tool deflection, run-out, wear, and chattering), and these factors will lead to the change of the cutting forces These factors are hardly considered in the mechanical models. Based on the above research, to improve the prediction accuracy of the mechanistic model of cutting force during micro-end-milling, this paper proposes a modified mechanistic model based on Gaussian process. In order to provide more choices for the selection of predictive models, this paper compares the common integrated forms (logarithmic residuals and residuals) of data with different levels of accuracy

Validation of the Mechanistic Model

Analyses

Using Experimental Data to Adjust the Mechanistic Model

Experimental Results

Conclusions