Surface Roughness Prediction in Ultra-Precision Milling: An Extreme Learning Machine Method with Data Fusion
Suiyan Shang,
Chunjin Wang,
Xiaoliang Liang,
Chi Fai Cheung,
Pai Zheng
Affiliations
Suiyan Shang
State Key Laboratory of Ultra-Precision Machining Technology, Department Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Chunjin Wang
State Key Laboratory of Ultra-Precision Machining Technology, Department Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Xiaoliang Liang
State Key Laboratory of Ultra-Precision Machining Technology, Department Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Chi Fai Cheung
State Key Laboratory of Ultra-Precision Machining Technology, Department Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
Pai Zheng
State Key Laboratory of Ultra-Precision Machining Technology, Department Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hong Kong, China
This paper pioneers the use of the extreme learning machine (ELM) approach for surface roughness prediction in ultra-precision milling, leveraging the excellent fitting ability with small datasets and the fast learning speed of the extreme learning machine method. By providing abundant machining information, the machining parameters and force signal data are fused on the feature level to further improve ELM prediction accuracy. An ultra-precision milling experiment was designed and conducted to verify our proposed data-fusion-based ELM method. The results show that the ELM with data fusion outperforms other state-of-art methods in surface roughness prediction. It achieves an impressively low mean absolute percentage error of 1.6% while requiring a mere 18 s for model training.
