Sample Augmentation for Intelligent Milling Tool Wear Condition Monitoring Using Numerical Simulation and Generative Adversarial Network
| Autor: | Jiawei Xiang, Qinsong Zhu, Weifang Sun, Bintao Sun, Yuqing Zhou |
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| Rok vydání: | 2021 |
| Předmět: |
Computer simulation
Computer science business.industry 020208 electrical & electronic engineering SIGNAL (programming language) Condition monitoring Sample (statistics) Pattern recognition 02 engineering and technology Finite element method Sampling (signal processing) Classifier (linguistics) 0202 electrical engineering electronic engineering information engineering Artificial intelligence Electrical and Electronic Engineering Tool wear business Instrumentation |
| Zdroj: | IEEE Transactions on Instrumentation and Measurement. 70:1-10 |
| ISSN: | 1557-9662 0018-9456 |
| Popis: | Recent advances in artificial intelligence (AI) technology have led to increasing interest in the development of AI-based tool condition monitoring (TCM) methods. However, achieving good performance using these methods relies heavily on large training samples, which are both expensive and difficult to obtain in practical TCM applications. This article addresses this issue by employing a much smaller training sample composed of a non-exhaustive sampling of experimentally measured cutting force signals in conjunction with a novel data augmentation method that combines numerical simulation with a generative adversarial network (GAN). First, cutting force signal samples not present in the experimental dataset are obtained by numerical simulation using a finite element method simulated based on the Johnson–Cook model. Second, the GAN is employed to synthesize additional samples that are similar to both the simulated samples and the experimentally measured samples. The synthesized samples are combined with the measured and simulated samples to produce an appropriately large dataset necessary for the effective training of an AI classifier. The proposed sample augmentation method is applied in milling TCM experiments, and the classification accuracies obtained with several AI classifiers trained with the augmented dataset were all close to or equal to 100%. |
| Databáze: | OpenAIRE |
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