Robust modeling method for thermal error of CNC machine tools based on random forest algorithm
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DOI: 10.1007/s10845-021-01894-w
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- A. Mosallam & K. Medjaher & N. Zerhouni, 2016. "Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction," Journal of Intelligent Manufacturing, Springer, vol. 27(5), pages 1037-1048, October.
- Antoni Świć & Arkadiusz Gola & Łukasz Sobaszek & Natalia Šmidová, 2021. "A thermo-mechanical machining method for improving the accuracy and stability of the geometric shape of long low-rigidity shafts," Journal of Intelligent Manufacturing, Springer, vol. 32(7), pages 1939-1951, October.
- S. Zhang & T. N. Wong, 2018. "Integrated process planning and scheduling: an enhanced ant colony optimization heuristic with parameter tuning," Journal of Intelligent Manufacturing, Springer, vol. 29(3), pages 585-601, March.
- Liang Tian & Yu Luo, 2020. "A study on the prediction of inherent deformation in fillet-welded joint using support vector machine and genetic optimization algorithm," Journal of Intelligent Manufacturing, Springer, vol. 31(3), pages 575-596, March.
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- You-Shyang Chen & Jieh-Ren Chang & Ying-Hsun Hung & Jia-Hsien Lai, 2023. "Oversampling Application of Identifying 3D Selective Laser Sintering Yield by Hybrid Mathematical Classification Models," Mathematics, MDPI, vol. 11(14), pages 1-30, July.
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Keywords
Thermal error; Machine tool; Artificial intelligence; Random forest; Machine learning;All these keywords.
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