An investigation into methods for predicting material removal energy consumption in turning

Lv, J., Tang, R., Tang, W., Jia, S., Liu, Y. and Cao, Y. (2018) An investigation into methods for predicting material removal energy consumption in turning. Journal of Cleaner Production, 193, pp. 128-139. (doi: 10.1016/j.jclepro.2018.05.035)

163534.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives.



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.

Item Type:Articles
Additional Information:Authors would like to acknowledge financial support of National Natural Science Foundation of China (Grant No. U1501248), South Tai Lake Program of Huzhou Zhejiang and Project of Shandong Province Higher Educational Science and Technology Program (No. J17KA167).
Glasgow Author(s) Enlighten ID:Liu, Dr Ying
Authors: Lv, J., Tang, R., Tang, W., Jia, S., Liu, Y., and Cao, Y.
College/School:College of Science and Engineering > School of Engineering > Systems Power and Energy
Journal Name:Journal of Cleaner Production
ISSN (Online):1879-1786
Published Online:04 May 2018
Copyright Holders:Copyright © 2018 Elsevier
First Published:First published in Journal of Cleaner Production 193:128-139
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

University Staff: Request a correction | Enlighten Editors: Update this record