A generalized analysis of energy saving strategies through experiment for CNC milling machine tools

• Published in: International journal of advanced manufacturing technology Vol. 117; no. 3-4; pp. 751 - 763
• Main Authors: Feng, Chunhua, Chen, Xiang, Zhang, Jingyang, Huang, Yugui
• Format: Journal Article
• Language: English
• Published: London Springer London 01.11.2021; Springer Nature B.V
• Subjects: CAE) and Design; Computer-Aided Engineering (CAD; Correlation coefficients; Cutting parameters; Cutting tool paths; Cutting tools; Energy consumption; Engineering; Industrial and Production Engineering; Machine tools; Mechanical Engineering; Media Management; Milling machines; Numerical controls; Original Article; Parameter identification; Spindles; Tool path; CNC milling process; Energy-saving strategy; Energy predict model
• ISSN: 0268-3768; 1433-3015
• DOI: 10.1007/s00170-021-07787-9

This paper proposes the elaboration model of energy requirement prediction taking into account the power of standby, spindle rotation in non-load, feeding, and rapid movement in X, Y, Z+, and Z− axially, and specific energy consumption (SEC) in the X and Y cutting directions, respectively, which could not be considered complete in other models. Each part energy of specific machine tools could be obtained through little experiments for identifying the relationship between energy and tool path with cutting parameters. The method is validated by 27 trial cutting experiment in X and Y cutting directions in the VMC850E machine; the results show that the SEC in the X and Y cutting directions is different. Moreover, it is found that spindle power should be piecewise linear representation according to spindle speed characteristic, due to the correlation coefficient of power model only has 25.45% without segmented. Additionally, the correlation coefficient of the improved SEC model could reach more than 99.98% in each segment. The contribution of this paper is mainly the elaboration energy consumption model considering the cutting direction, which is an efficient approach for predicting energy consumption through tool path to achieve sustainable production in manufacturing sectors.