Leveraging AI for energy-efficient manufacturing systems: Review and future prospectives

IF 12.2 1区 工程技术 Q1 ENGINEERING, INDUSTRIAL
Mohammad Mehdi Keramati Feyz Abadi, Chao Liu, Ming Zhang, Youxi Hu, Yuchun Xu
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引用次数: 0

Abstract

Energy poses a significant challenge in the industrial sector, and the abundance of data generated by Industry 4.0 technologies offers the opportunity to leverage Artificial Intelligence (AI) for enhancing energy efficiency (EE) in manufacturing processes, particularly within manufacturing systems. However, fully realizing AI's potential in addressing energy challenges requires a comprehensive review of AI methodologies aimed at overcoming obstacles in energy-efficient manufacturing systems. This article provides a systematic review that combines both quantitative and qualitative analyses of literature from the past ten years, focusing on mitigating prevalent energy efficiency challenges in manufacturing systems through AI-related methodologies. These challenges include Monitoring and Prediction, Real-Time Control, Scheduling, and Parameters Optimization. The AI-related solutions proposed in the reviewed research articles utilize Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) techniques, either individually or in combination with other methods. A total of 67 journal papers on manufacturing systems, addressing the mentioned energy challenges through AI-related approaches, have been identified and thoroughly reviewed. As a result of this review, an Energy Efficient-Digital Twin (EE-DT) framework is proposed, demonstrating how a DT, equipped with AI techniques, can be applied to solve energy issues in manufacturing systems. This study provides scholars with a comprehensive guideline for selecting various types of AI methods to address common challenges in energy-efficient manufacturing systems, while also highlighting some promising future research directions.
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来源期刊
Journal of Manufacturing Systems
Journal of Manufacturing Systems 工程技术-工程:工业
CiteScore
23.30
自引率
13.20%
发文量
216
审稿时长
25 days
期刊介绍: The Journal of Manufacturing Systems is dedicated to showcasing cutting-edge fundamental and applied research in manufacturing at the systems level. Encompassing products, equipment, people, information, control, and support functions, manufacturing systems play a pivotal role in the economical and competitive development, production, delivery, and total lifecycle of products, meeting market and societal needs. With a commitment to publishing archival scholarly literature, the journal strives to advance the state of the art in manufacturing systems and foster innovation in crafting efficient, robust, and sustainable manufacturing systems. The focus extends from equipment-level considerations to the broader scope of the extended enterprise. The Journal welcomes research addressing challenges across various scales, including nano, micro, and macro-scale manufacturing, and spanning diverse sectors such as aerospace, automotive, energy, and medical device manufacturing.
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