Improved milling stability analysis for chatter-free machining parameters planning using a multi-fidelity surrogate model and transfer learning with limited experimental data

Congying Deng, Jielin Tang, Sheng Lu, Ying Ma, Lijun Lin and Jianguo Miao

International Journal of Production Research, 2024, vol. 62, issue 4, 1126-1143

Abstract: Decision-making on chatter-free machining parameters is essential for process planning since chatter significantly affects production quality and efficiency. Stability lobe diagram (SLD) is commonly used for selecting chatter-free machining parameters, but its analytical prediction often has poor accuracy and experiment-based prediction is time-consuming. This paper proposes a multi-fidelity (MF) surrogate model and transfer learning-based method to improve the milling stability analysis. Firstly, an analytical stability model is constructed to predict low-fidelity (LF) SLDs for key combinations of radial cutting width (ae) and feed rate per tooth (ft). A few spindle speeds (ns) are selected from each key LF SLD to detect high-fidelity (HF) stability limits (aplim) through milling experiments. Subsequently, sufficient LF and limited HF combinations of ns, ae, ft, and aplim are taken to construct additive scaling function-based MF stability models. Predicted MF combinations of ns, ae, ft, and aplim are combined with limited HF combinations to construct more accurate stability models through transfer learning. Then, a neural network is ultimately trained to predict aplim values for arbitrary combinations of ns, ae, and ft. A detailed experimental validation indicates that the proposed method can provide more accurate lobe boundaries for machining parameters selection by introducing fewer experimental samples.

Date: 2024
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DOI: 10.1080/00207543.2023.2176698

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