基于轻量级机器学习的产品质量识别及其工业应用

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2024-02-14 DOI:10.1080/0305215x.2024.2305242

Jieyang Peng, Dongkun Wang, Simon Kreuzwieser, Andreas Kimmig, Xiaoming Tao, Liting Wang, Jivka Ovtcharova

引用次数: 0

摘要

质量管理在制造业中起着至关重要的作用，因为它直接影响着产品质量和客户满意度。传统制造业已开始将质量管理纳入其生产流程。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Product quality recognition and its industrial application based on lightweight machine learning

Quality management plays a crucial role in manufacturing industry as it directly impacts product quality and customer satisfaction. Traditional manufacturing industries have begun incorporating dee...

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来源期刊

Engineering Optimization 管理科学-工程：综合

CiteScore

5.90

自引率

7.40%

发文量

审稿时长

3.5 months

期刊介绍： Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process. Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.