Data-Driven Multi-Criteria Decision-Making for Smart and Sustainable Machining

Abstract This paper proposes a multi-criteria decision-making analysis of the alternatives for smart and sustainable machining processes to provide visibility and clarity on the factors that can affect production performance. Identification of such parameters can aid in the adoption of smart manufacturing technologies. The framework developed for decision making utilizes fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to compare alternative machining scenarios. Machining with Tool Condition Monitoring (TCM) and machining with Computational Fluid Dynamics (CFD) for modeling ambient conditions are analyzed for their application and form use cases in the framework. Feasibility of TCM via vibration analysis when milling 17-4 Stainless Steel is investigated and a positive trend is observed between the surface roughness of the work piece and the cutting tool vibration at time steps where tool wear is predicted. Thus, a viable low-cost solution for TCM is available. The ambient conditions of the machining environment have been modelled with CFD to study temperature and airflow gradients. The CFD model can be used to reduce thermal errors for precision machining and enhance operator efficiency. The result from the decision-making framework shows a clear preference for smart machining alternatives as compared to the conventional machining. In all, machining with TCM and CFD is found to be the most preferred.