Turning part design for joint optimisation of machining and transportation energy consumption. (20th September 2019) - Hu, Luoke; Liu, Weipeng; Xu, Kangkang; Peng, Tao; Yang, Haidong; Tang, Renzhong
Abstract: Motivated by sustainable development in the manufacturing industry, many approaches have been proposed to reduce the machining energy consumption (MEC) of machine tools at the production stage. However, the design stage determines the majority of the MEC for a machined part, and has greater energy-saving potential than the other stages. The question is how to design an optimal diameter of a part to minimise its future MEC for external turning. If the MEC is reduced by increasing the final diameter based on the same blank, the weight of the part will increase. Consequently, the transportation energy consumption (TEC) of electric vehicles (e.g. automatic guided vehicles) for moving the parts will also increase. Based on this finding, herein we consider a trade-off between the reductions of MEC and TEC. A TEC model is developed and integrated with a modified MEC model to obtain a machine-vehicle system energy consumption (MVSEC) model. Thus, as a novel contribution of this study, an optimised trade-off is proposed between reductions of MEC and TEC by using an integrated optimal design of diameter and cutting parameters. In a case study, the optimal design parameters of a shaft sleeve to be processed by a machine tool (CK6153i) and an electric vehicle (ZX001) were found. The experiment results show that simulated annealing can provide high-quality solutions in a short computation time. The optimal solution achieved a 3.53% MVSEC (28.281 KJ) reduction for a case. Finally, the relationship between transportation distance and optimal diameter is analysed, and the effect of MVSEC optimisation on machining time is discussed. Graphical abstract: Image 108461 Highlights: Energy for machining and transportation is jointly optimised at the design stage. A machine-vehicle system energy consumption model is developed. The optimal diameter and cutting parameters are obtained by simulated annealing. The relationship between transportation distance and optimal diameter is analysed. The approach achieves a 3.53% energy consumption (28.281 KJ) reduction for a case.