Sustainable production plays an important role in product lifecycle management by considering the social sustainability. Energy-efficient machining is an efficient approach for sustainable production in current manufacturing sectors. Although many related efforts have been achieved, a comprehensive energy optimization approach oriented to manufacturing parts is still a challenge. Therefore, this paper selects Standard for the Exchange of Product model data-Numerical Control (STEP-NC) as the enabling technology to achieve energy-efficient machining. An optimization model is proposed based on the energy calculation method using the workingstep in STEP-NC. An improved ant colony optimization (ACO) solution, consisting of encoding and decoding, initialization, machining scheme generation, idea of local multiple iteration, evaluation, pheromone evaporation and update, is presented. A part with typical manufacturing features is applied to verify the effectiveness of the proposed approach. The generated solution can provide a comprehensive machining scheme for low energy demandI by improving the efficiency with 25% for solving the optimization problem.
Due to soaring energy prices and environmental pollution, research on sustainable product lifecycle management (SPLM) has been focused recently. Sustainable production is one important phase of SPLM. Reducing energy consumption during machining operations plays a critical role in achieving sustainable production, i.e., energy-efficient machining. Energy-efficient machining has attracted increasingly more efforts in recent years (CamposecoNegrete, 2013; Gong et al., 2016; Velchev et al., 2014; Yan and Li, 2013; Zhou et al., 2016). Deciding machining schemes (MSs) for a part to be machined from the perspective of energy efficiency is an effective way to perform energy-efficient machining. A MS consists of many key elements, e.g., machining resources, machining parameters, tool path, process route, etc. In general, there exist more than one reasonable MS, which could constitute a group of candidate machining schemes (CMSs), and the best machining scheme (BMS), i.e., energy-efficient machining scheme (EEMS), is generated from this group. It is obvious that this process refers to an optimization problem. To solve the optimization problem, two key procedures are necessary, i.e., optimization model and the corresponding solution method. An optimization model includes optimization objective, optimization variables and constraints. The objective may be single (i.e. only energy consumption) or multiple (e.g. time, energy, tool life, etc.). No matter single-objective or multipleobjectives optimization, an energy consumption model for indicating the energy calculation approach during machining processes is essential, which is used to formulating the optimization objective. Besides, the optimization variables are the contributing factors to the objective, and the constraints are determined in terms of the specific conditions. Regarding the solution method, a metaheuristic algorithm is a type of effective method of solving the optimization model, such as genetic algorithm (GA), ant colony optimization (ACO), particle swarm optimization (PSO), tabu search (TS), simulated annealing (SA) and NSGA-II.