A systematic literature review on machine tool energy consumption. (1st December 2020)

• Record Type: Journal Article
• Authors: Sihag, Nitesh; Sangwan, Kuldip Singh
• Is Part Of: Journal of cleaner production. Volume 275(2020)
• Publication Date: 2020-12-01
• Subjects: Machining energy -- Systematic literature review -- Classification -- Modelling -- Efficiency assessment; Factory and trade waste -- Management -- Periodicals; Manufactures -- Environmental aspects -- Periodicals; Déchets industriels -- Gestion -- Périodiques; Usines -- Aspect de l'environnement -- Périodiques; 628.5
• DOI: 10.1016/j.jclepro.2020.123125
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: Energy efficiency has become an integral part of the metal manufacturing industries as a means to improve economic and environmental performance, and increase competitiveness. Machine tools are not only the major energy consumer in the manufacturing industry but also have very low efficiency. Therefore, the analysis of energy consumption by the machine tools is primarily important to understand their complex and dynamic energy consumption behavior. This will lead to the development of better corrective measures. Literature review helps in identifying and assessing the existing knowledge to recognize the future research areas for fostering the research interest on the specific topic. In this review article, the reference literature is identified using a systematic methodology followed by descriptive and content analysis to understand the evolution of research in machining energy. The review focuses on four machining energy aspects – classification, modelling, improvement strategies, and efficiency evaluation. A six level hierarchical model is proposed for better understanding of machining energy classification. The literature review shows that the research in this field intensified after 2009. It is observed that the research focus has shifted towards micro level classification of machining energy including transient states. More detailed and accurate energy consumption models are developed in recent years with increased use of soft computational methods. Real time energy data monitoring and its use for online optimization of machining processes is witnessed. The use of micro analysis, energy benchmarking and standardization of energy assessment indices require more research. Deployment of machining energy models for improving the sustainability of machine tools; data analytics and AI applications; and integration with industry 4.0 are new research opportunities in the field. Graphical abstract: Image 1 Highlights: A detailed analysis of literature in the machining energy area encompassing 226 articles published during 1994–2018. Analysis of four machining energy aspects– classification; modelling; saving strategies; efficiency assessment. Proposal of a six-level hierarchical model for machining energy classification. Identification and classification of the various machining energy aspects for selection of appropriate models/techniques. The future research directions in view of sustainability, industry 4.0, data analytics, and AI applications are highlighted.