A Decision-Making Approach for Sustainable Machining Processes Using Data Clustering and Multi-Objective Optimization

Abstract

Achieving sustainable machining processes has become crucial in many industries in order to support sustainable development goals (e.g., good health and well-being, decent work and economic growth, affordable and clean energy). Many attempts have been made to optimize the sustainability aspect during machining processes and to offer optimized cutting conditions. However, there is a vital need to develop a decision-making approach that can be flexible and offer optimal sustainable solutions for different machining scenarios. The current study offers a new decision-making approach for sustainable machining processes using data clustering (i.e., K-means clustering) and multi-objective optimization methods (i.e., grey relational analysis). Utilizing the multi-objective optimization after the clustering phase provides the decision maker with optimal and sustainable cutting conditions for different clusters. The developed approach is validated through a case study that includes five design variables (i.e., feed, speed, nose radius, cooling strategy, and rake angle), three machining outputs (i.e., surface roughness, specific energy, and unit volume machining time), and four different scenarios (i.e., finishing, roughing, balanced, and entropy). Three clusters were generated, and the obtained results were compatible with the physical meaning of each studied scenario. Such an approach can provide the decision maker with sufficient flexibility to select the optimal cutting settings for various scenarios, as well as the freedom to switch between clusters and/or scenarios with minimal effort.