低碳智能制造的智能算法和方法:回顾过去的研究、最近的发展和未来的研究方向

IF 2.5 Q2 ENGINEERING, INDUSTRIAL
Sudhanshu Joshi, Manu Sharma
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引用次数: 0

摘要

低碳智能制造(SM)作为促进制造业可持续发展和无碳排放的一项战略,受到了广泛关注。智能算法和程序的实施有助于实现和改进低碳智能制造流程。这些算法有助于实时监控和预测性维护,确保高效、可持续的运营和数据驱动决策,提高资源利用率、减少浪费和能源效率。本研究探讨了低碳智能制造中算法的应用,包括机器学习、优化算法和预测分析。研究对 2011 年至 2023 年的文献进行了全面评估,以评估低碳方法在智能制造中的重要性。采用了内容分析、网络数据分析、文献计量分析和聚类分析等综合方法。根据已发表的文献,印度、中国、美国、英国、新加坡和意大利是低碳制造研究的主要贡献者。研究结果显示了五大主题--低碳智能制造与算法应用;面向低碳制造的工业 4.0 技术;低碳与绿色制造;低碳制造与产品设计和控制;精益系统与智能制造。本研究的目的是通过根据已确定的研究缺陷对研究工作进行评估,为政策制定者和研究人员提供低碳制造学术发展指南。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Intelligent algorithms and methodologies for low-carbon smart manufacturing: Review on past research, recent developments and future research directions

Intelligent algorithms and methodologies for low-carbon smart manufacturing: Review on past research, recent developments and future research directions

Significant attention has been given to low-carbon smart manufacturing (SM) as a strategy for promoting sustainability and carbon-free emissions in the manufacturing industry. The implementation of intelligent algorithms and procedures enables the attainment and enhancement of low-carbon clever manufacturing processes. These algorithms facilitate real-time monitoring and predictive maintenance, ensuring efficient and sustainable operations and data-driven decision-making, increasing resource utilisation, waste reduction, and energy efficiency. The research examines the utilisation of algorithms in the context of low-carbon smart manufacturing, encompassing machine learning, optimisation algorithms, and predictive analytics. A comprehensive literature evaluation spanning from 2011 to 2023 is conducted to assess the significance of low-carbon approaches in the context of smart manufacturing. An integrated approach is used using content analysis, network data analysis, bibliometric analysis, and cluster analysis. Based on the published literature the leading contributors to low-carbon manufacturing research are India, China, United States, United Kingdom, Singapore, and Italy. The results have shown five main themes—Low-carbon smart manufacturing and applications of Algorithms; Industry 4.0 technologies for low-carbon manufacturing; low carbon and green manufacturing; Low-carbon Manufacturing, and Product design and control; Lean Systems and Smart Manufacturing. The purpose of this study is to provide policymakers and researchers with a guide for the academic development of low-carbon manufacturing by evaluating research efforts in light of identified research deficits.

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来源期刊
IET Collaborative Intelligent Manufacturing
IET Collaborative Intelligent Manufacturing Engineering-Industrial and Manufacturing Engineering
CiteScore
9.10
自引率
2.40%
发文量
25
审稿时长
20 weeks
期刊介绍: IET Collaborative Intelligent Manufacturing is a Gold Open Access journal that focuses on the development of efficient and adaptive production and distribution systems. It aims to meet the ever-changing market demands by publishing original research on methodologies and techniques for the application of intelligence, data science, and emerging information and communication technologies in various aspects of manufacturing, such as design, modeling, simulation, planning, and optimization of products, processes, production, and assembly. The journal is indexed in COMPENDEX (Elsevier), Directory of Open Access Journals (DOAJ), Emerging Sources Citation Index (Clarivate Analytics), INSPEC (IET), SCOPUS (Elsevier) and Web of Science (Clarivate Analytics).
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