DeepMachining: Online Prediction of Machining Errors of Lathe Machines
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Abstract (arXiv)
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.
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References · 18
- 2015Deep Residual Learning for Image Recognitioncited by 51,950
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,304
- 2020Language Models are Few-Shot Learnerscited by 32,320
- 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,231
- 2014ImageNet Large Scale Visual Recognition Challengecited by 8,476
- 2015Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shiftcited by 6,991
- 2016Layer Normalizationcited by 5,624
- 2014Going Deeper with Convolutionscited by 4,239
- 2022A ConvNet for the 2020scited by 3,563
- 2016Gaussian Error Linear Units (GELUs)cited by 3,472
- 2018CBAM: Convolutional Block Attention Modulecited by 2,469
- 2021BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-modelscited by 1,240
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- 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 868
- 2020AdapterFusion: Non-Destructive Task Composition for Transfer Learningcited by 785
- 2022Fine-Tuning can Distort Pretrained Features and Underperform Out-of-Distributioncited by 732
- 2020TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learningcited by 32
- 2022The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shiftcited by 25
- 2024Ask Your Distribution Shift if Pre-Training is Right for Youcited by 7
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