Modelling approach for energy efficiency of machining system based on torque model and angular velocity. (15th April 2021)

• Record Type: Journal Article
• Authors: Xie, Jun; Cai, Wei; Du, Yanbin; Tang, Ying; Tuo, Junbo
• Is Part Of: Journal of cleaner production. Volume 293(2021)
• Publication Date: 2021-04-15
• Subjects: Machining systems -- Energy efficiency -- Monitoring -- Sustainable machining; 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.2021.126249
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: As the most important part of machining system, machine tools are widely used in industry, and the resource consumption and environmental problems caused by their use are becoming more and more serious. For promoting energy efficiency of machining system, various methods have been developed based on statistical approaches with design of experiments. However, the methods cannot be easily utilized when the optimization target or machine tool design is modified because the optimal solution is determined based on the experimentally measured data. An effective and practical monitoring method is not only the precondition of energy efficiency promoting, but also the basis of the integration with industry 4.0 applications. This paper presents a modelling approach for energy efficiency of machining system which can be used to monitor energy efficiency on-line and without any extra cutting force dynamometer. The approach is proposed based on the energy consumption mechanism of machining system and the dynamic factors (temperature, lubrication, etc.) of machining processes are considered, so that it has a quite high accuracy under various machining conditions. The case study and validation have been carried out and the experimental results indicate that the accuracy of the approach can reach over 90% under various processing conditions. Finally, the static errors and dynamic errors which may affect monitoring accuracy are discussed.