March 26, 2024, 4:42 a.m. | Xiang-Li Lu, Hwai-Jung Hsu, Che-Wei Chou, H. T. Kung, Chen-Hsin Lee

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.16451v1 Announce Type: new
Abstract: 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 …

abstract ai system arxiv cs.ai cs.lg data deep learning errors factories learn machine machines manufacturing operations prediction type

AI Research Scientist

@ Vara | Berlin, Germany and Remote

Data Architect

@ University of Texas at Austin | Austin, TX

Data ETL Engineer

@ University of Texas at Austin | Austin, TX

Lead GNSS Data Scientist

@ Lurra Systems | Melbourne

Senior Machine Learning Engineer (MLOps)

@ Promaton | Remote, Europe

Business Data Analyst

@ Alstom | Johannesburg, GT, ZA