We describe DeepMachining, a deep learning-based AI system for online prediction of machining errors of lathe machine operations. We have built and evaluated DeepMachining based on manufacturing data from factories. Specifically, we first pretrain a deep learning model for a given lathe machine's operations to learn the salient features of machining states. Then, we fine-tune the pretrained model to adapt to specific machining tasks. We demonstrate that DeepMachining achieves high prediction accuracy for multiple tasks that involve different workpieces and cutting tools. To the best of our knowledge, this work is one of the first factory experiments using pre-trained deep-learning models to predict machining errors of lathe machines.
@article{arxiv.2403.16451,
title = {DeepMachining: Online Prediction of Machining Errors of Lathe Machines},
author = {Xiang-Li Lu and Hwai-Jung Hsu and Che-Wei Chou and H. T. Kung and Chen-Hsin Lee and Sheng-Mao Cheng},
journal= {arXiv preprint arXiv:2403.16451},
year = {2025}
}