Energy-Efficient machining process analysis and optimisation based on BS EN24T alloy steel as case studies. (August 2019) - Moreira, L.C.; Li, W.D.; Lu, X.; Fitzpatrick, M.E.
Highlights: Novel qualitative analysis and an optimisation algorithm have been developed to improve energy consumption of machining processes on BS EN24T alloy (AISI 4340). The impact that key machining parameters have on energy consumption has been investigated in detail via experiments and qualitative analysis, supporting process planners in implementing energy saving measures. A multi-objective optimisation model has been formulated, and a novel improved multi-swarm fruit fly optimisation algorithm (iMFOA) has been developed to identify optimal solutions. Abstract: Computer Numerical Controlled (CNC) machining, which is one of the most widely-deployed manufacturing techniques, is an energy-intensive process. It is important to develop energy-efficient CNC machining strategies to achieve the overall goal of sustainable manufacturing. Due to the complexity of machining parameters, it is challenging to develop effective modelling and optimisation approaches to implement energy-efficient CNC machining. To address the challenge, in this paper, BS EN24T alloy (AISI 4340) has been used as a case study to conduct energy-efficient analysis and optimisation. Using a combination of experimentation and Taguchi analysis, the impact of the key machining parameters of CNC machining processes on energy consumption has been investigated in detail. A multi-objective optimisation model has been formulated, and a novel improved multi-swarm Fruit Fly optimisation algorithm (iMFOA) has been developed to identify optimal solutions. Case studies and algorithm benchmarking have been conducted to validate the effectiveness of the optimisation approach. The relationships between energy consumption and key machining parameters ( e.g ., cutting speed, feed per tooth, engagement depth) have been analysed to support process planners in implementing energy-saving measures efficiently. The optimisation approach developed is effective in fine-tuning key parameters for enhancing energy efficiency while meeting other technical requirements of production.