An investigation into methods for predicting material removal energy consumption in turning. (20th August 2018)

• Record Type: Journal Article
• Authors: Lv, Jingxiang; Tang, Renzhong; Tang, Wangchujun; Jia, Shun; Liu, Ying; Cao, Yanlong
• Is Part Of: Journal of cleaner production. Volume 193(2018)
• Page Start: 128
• Page End: 139
• Publication Date: 2018-08-20
• Subjects: Material removal -- Energy consumption -- Cutting force -- Uncertainty -- Cutting parameter selection; 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.2018.05.035
• Languages: English
• ISSNs: 0959-6526

Abstract

Abstract: The wide use of machining processes has imposed a large pressure on environment due to energy consumption and related carbon emissions. The total power required in machining include power consumed by the machine before it starts cutting and power consumed to remove material from workpiece. Accurate prediction of energy consumption in machining is the basis for energy reduction. This paper investigates the prediction accuracy of the material removal power in turning processes, which could vary a lot due to different methods used for prediction. Three methods, namely the specific energy based method, cutting force based method and exponential function based method are considered together with model coefficients obtained from literature and experiments. The methods have been applied to a cylindrical turning of three types of workpiece materials (carbon steel, aluminum and ductile iron). Methods with model coefficients obtained from experiments could achieve a higher prediction accuracy than those from literature, which can be explained by the inability of the coefficients from literature to match the specific machining conditions. When the coefficients are obtained from literature, the prediction accuracy is largely dependent on the sources of coefficients and there is no definitive dominance of one approach over another. With model coefficients from experiments, the cutting force based model achieves the best accuracy, followed by the exponential function based method and specific energy based method. Furthermore, the power prediction methods can be used in process design stage to support energy consumption reduction of a machining process. Highlights: Three methods can be used to predict material removal energy. The accuracy and implementation difficulty of three methods are investigated. The coefficients for the methods can be obtained experimentally. The cutting force based method can provide the most accurate power prediction. The methods should be selected according to the required accuracy in industry.