Machining Stability Categorization and Prediction Using Process Model Guided Machine Learning

Jeong Hoon Ko

doi:10.3390/met12020298

Abstract

The time-domain dynamic process model is used to generate data and guides the stability criteria for machine learning, saving the experimental costs for a number of required data for the metal process. Fourier transformation of vibration data simulated using a dynamic process model generates the feature lists including multiple frequencies and amplitudes at each process condition. The feature lists for milling stability are analyzed for training the machine learning algorithm. The amplitude and frequency distributions may change according to the dynamic pattern of the machining stability. The vibration patterns are grouped into stable, chatter, and boundary conditions by performing data training using support vector machines and gradient tree boosting. In the high-speed milling of Al6061-T6 with 6000 to 18,000 RPM and variations of axial and radial depths of cuts, 2400 data sets of the time domain data were trained and tested. Actual experimental tests are carried out for new process conditions with the range of 9890 to 28,470 RPM and 989 to 2847 mm/min. The experimental stability outcomes are compared with predictions from the algorithms. Stability is accurately predicted over new conditions with around 0.9 prediction accuracy, which means the methodology can be used to predict, categorize, and monitor stability in end milling processes.

Highlights

Chatter is one of the significant issues in metal processing and is still a bottleneck to maximize the process under a tightened quality requirement in modern manufacturing industries
Dynamic model-guided data analytics with 2400 sets of data generated in less than 10 min reduces the cost of experimental data for milling chatter prediction for wide ranges of process conditions
The machine learning algorithm with the proposed methodology demonstrates that the stability prediction accuracy can reach around 0.9

Summary

Introduction

Chatter is one of the significant issues in metal processing and is still a bottleneck to maximize the process under a tightened quality requirement in modern manufacturing industries. [22] used multi-sensor data for chatter tests and applied SVM for classification with high accuracy Their cutting parameters ranged from 180 to 220 RPM and 0.4 to 1.0 mm cutting depths. Machine learning has been performed for limited ranges of process conditions, such as fixed variation of radial depth of cut or spindle rpm due to experimental costs and computation time. This article proposes simulation structure and algorithm to use data sets from the time domain dynamics model to significantly reduce experimental costs over the wide range of process conditions such as axial depth of cut 1.3 mm to 3.4 mm, the radial depths of cut 2.5 mm to 7.5 mm, spindle RPM 6000 to 28,470 RPM, and feed rates 600 to 2847 mm/min. Dynamic model-guided data analytics with 2400 sets of data generated in less than 10 min reduces the cost of experimental data for milling chatter prediction for wide ranges of process conditions

Generation of Data Sets for Feature Elements Using Time-Domain Process Model

Stable

Findings

Discussion

Conclusions