التفاصيل البيبلوغرافية
| العنوان: |
Improved milling stability analysis for chatter-free machining parameters planning using a multi-fidelity surrogate model and transfer learning with limited experimental data |
| المؤلفون: |
Congying Deng, Jielin Tang, Sheng Lu, Ying Ma, Lijun Lin, Jianguo Miao |
| المصدر: |
Taylor & Francis Journals, International Journal of Production Research. 62(4):1126-1143 |
| سنة النشر: |
2024 |
| الوصف: |
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. |
| نوع الوثيقة: |
redif-article |
| اللغة: |
English |
| DOI: |
10.1080/00207543.2023.217 |
| الإتاحة: |
https://ideas.repec.org/a/taf/tprsxx/v62y2024i4p1126-1143.html |
| رقم الأكسشن: |
edsrep.a.taf.tprsxx.v62y2024i4p1126.1143 |
| قاعدة البيانات: |
RePEc |
