Toward Energy Footprint Reduction of a Machining Process

Abstract

In a machining process, proper selection of process plans and cutting parameters can effectively reduce energy consumption and shorten production time. Traditionally, studies on process planning and cutting parameter optimization for energy saving are mostly concentrated on electrical energy consumption. Since the preparation process of cutting tools and cutting fluid consumes a considerable amount of energy, conservation of this part of energy consumption, namely, the embodied energy consumption, will achieve a more energy-efficient machining process. In this article, an integrated model for process planning and cutting parameter optimization is proposed to shorten production time and reduce the energy footprint (namely, electrical energy consumption and embodied energy consumption of cutting tools and cutting fluid) of a machining process. Considering that the optimization of process plan and cutting parameters in an integrated manner is a hybrid programming process, simulated annealing and quantum-behaved particle swarm optimization (SA-QPSO) hybrid algorithm is employed to solve the proposed model. Results of the case study show that: 1) embodied energy consumption of cutting tools and cutting fluid accounts for a nonnegligible proportion of energy footprint of the machining process and 2) there is a tradeoff between energy footprint and production time, and the balance of them is achieved through the proposed optimization approach. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This article, for the first time, to the best of our knowledge, proposes an integrated approach to reduce both electrical and embodied energy consumption of a machining process through optimizing process plan and cutting parameters. Such broader consideration makes this integrated optimization approach more applicable to real industry settings and contributes to the comprehensive improvement of energy efficiency in the machining process. To better use this approach, the following three steps should be highlighted: 1) the energy footprint characteristics of the machining process should be comprehensively analyzed and modeled; 2) the integrated optimization model for minimizing energy footprint and production time needs to cooperate with machining constraints, such as process centralization, machining sequence, and process requirements; and 3) solving the proposed model is a hybrid programming process since there are discrete decision variables and continuous variables. A proper algorithm should be used to solve the proposed model.