基于生成模型增强深度学习和激光再制造技术的刀具寿命预测和延长

IF 8 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuchen Liang , Yuqi Wang , Raymond Chiong , Anping Li , Jinzhong Lu
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引用次数: 0

摘要

在加工过程中预测和延长刀具的剩余寿命对可持续制造至关重要。传统的预测方法往往难以适应加工过程生命周期中不同的工况。本文提出了一个新的框架,通过集成多源数据和使用深度学习技术有效地解决了这些挑战。该系统集成了从计算机数控机床采集的增强功率和振动数据,并进行了以下创新:(1)建立了一种混合时间卷积网络(TCN)-注意力模型用于刀具剩余寿命预测,该模型的最佳预测准确率为98.51%,平均预测准确率为97.62%。此外,采用深度神经网络和增强的三元蜂算法选择了激光冲击强化的最佳参数。(2)采用时间序列生成对抗网络进行数据增强,增加了TCN模型训练的数据量。(3)利用t分布随机邻居嵌入、fr起始距离和均方根误差对数据质量进行评估,以确保真实数据与生成数据的相似性。(4)通过有限元分析和试验测试,验证了再制造方法的有效性,刀具寿命分别提高了28.95%和30.77%。这种综合方法有助于提高刀具寿命预测精度,优化可持续再制造工艺,从而提高生产效率,减少加工作业中的浪费。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Cutting tool life prediction and extension through generative model-augmented deep learning and laser remanufacturing techniques
Predicting and extending the remaining life of cutting tools during machining processes is essential for sustainable manufacturing. Traditional prognosis methods often struggle to adapt to different working conditions over the machining process lifecycle. This paper proposes a novel framework that effectively addresses the challenges by integrating multi-source data and using deep learning techniques. The system integrates augmented-power and vibration data collected from computer numerical control machines with the following innovations: (1) A hybrid temporal convolutional network (TCN)-attention model is developed for cutting tool remaining life prognosis, which achieves the best accuracy of 98.51 % and average of 97.62 %. In addition, optimal laser shock peening parameters are selected using a deep neural network and enhanced ternary bees algorithm. (2) A time-series generative adversarial network is used for data augmentation, which increases data quantity for TCN model training. (3) Data quality is evaluated using the t-distributed stochastic neighbor embedding, Fréchet inception distance, and root mean squared error to ensure similarity between real and generated data. (4) The effectiveness of the remanufacturing approach is validated with a 28.95 % and 30.77 % increase in tool life based on finite element analysis and experimental testing, respectively. This comprehensive approach contributes to enhancing tool life prediction accuracy and optimizing sustainable remanufacturing processes, thereby enhancing production efficiency and reducing waste in machining operations.
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来源期刊
Engineering Applications of Artificial Intelligence
Engineering Applications of Artificial Intelligence 工程技术-工程:电子与电气
CiteScore
9.60
自引率
10.00%
发文量
505
审稿时长
68 days
期刊介绍: Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.
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