HG-XAI:通过机器视觉增强可解释人工智能,实现人类指导的工具磨损识别方法

IF 5.9 2区 工程技术 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Aitha Sudheer Kumar, Ankit Agarwal, Vinita Gangaram Jansari, K. A. Desai, Chiranjoy Chattopadhyay, Laine Mears
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引用次数: 0

摘要

对于机床操作员来说,识别刀具磨损状态至关重要,因为这有助于做出及时更换刀具和后续加工操作的明智决策。由于每种磨损状态都对应一种独特的缓解策略,因此在实施解决方案以尽量减少刀具磨损时,及时识别至关重要。本文介绍了一种新颖的人工智能(HG-XAI)方法,通过将人类智能和人工智能与预先训练的卷积神经网络(CNN)、Efficient-Net-b0 模型相结合来识别刀具磨损状态。刀具磨损状态是根据 IN718 加工过程中的不同磨损机制确定的。该研究考虑了四种不同的刀具磨损状态,即侧面、侧面+BUE、侧面+表面和崩刃,分别代表磨损、粘着、扩散和断裂磨损机制。通过以不同的表面速度加工 IN718,创建了基于图像的数据集来描述各种刀具磨损状态。通过与缺乏人工智能和 XAI 的独立 Efficient-Net-b0 模型的预测精度进行比较,评估了所提出的 HG-XAI 方法的有效性。此外,还通过预测在不同切削参数下获取的图像中的磨损状态,检验了 HG-XAI 方法的可扩展性。本研究的结果表明,HG-XAI 方法能以 93.08% 的准确率预测刀具磨损状态,并能根据切削条件的变化进行扩展。此外,在开发基于视觉的机上刀具磨损监测系统时,还可以对所提出的方法进行扩展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

HG-XAI: human-guided tool wear identification approach through augmentation of explainable artificial intelligence with machine vision

HG-XAI: human-guided tool wear identification approach through augmentation of explainable artificial intelligence with machine vision

Identifying tool wear state is essential for machine operators as it assists in informed decisions for timely tool replacement and subsequent machining operations. As each wear state corresponds to a unique mitigation strategy, timely identification is vital while implementing solutions to minimize tool wear. The paper presents a novel Human Guided-eXplainable Artificial Intelligence (HG-XAI) approach for identifying the tool wear state by integrating human intelligence and eXplainable AI with a pre-trained Convolutional Neural Network (CNN), Efficient-Net-b0 model. The tool wear states were identified based on different wear mechanisms during the machining of IN718. The study considers four distinct tool wear states, i.e., Flank, Flank+BUE, Flank+Face, and Chipping, representing abrasion, adhesion, diffusion, and fracture wear mechanisms. The image-based datasets were created to depict various tool wear states by machining IN718 at varying surface speeds. The effectiveness of the proposed HG-XAI approach was evaluated by comparing its prediction accuracy with a standalone Efficient-Net-b0 model lacking human intelligence and XAI. Further, the scalability of the HG-XAI approach was examined by predicting wear states from images acquired at different cutting parameters. The results from the present study showed that the HG-XAI approach can predict the tool wear state with an accuracy of 93.08% and is scalable to variations in cutting conditions. Also, the proposed approach can be extended while developing vision-based on-machine tool wear monitoring systems.

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来源期刊
Journal of Intelligent Manufacturing
Journal of Intelligent Manufacturing 工程技术-工程:制造
CiteScore
19.30
自引率
9.60%
发文量
171
审稿时长
5.2 months
期刊介绍: The Journal of Nonlinear Engineering aims to be a platform for sharing original research results in theoretical, experimental, practical, and applied nonlinear phenomena within engineering. It serves as a forum to exchange ideas and applications of nonlinear problems across various engineering disciplines. Articles are considered for publication if they explore nonlinearities in engineering systems, offering realistic mathematical modeling, utilizing nonlinearity for new designs, stabilizing systems, understanding system behavior through nonlinearity, optimizing systems based on nonlinear interactions, and developing algorithms to harness and leverage nonlinear elements.
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