Improving Sustainability Index of Grey Cast Iron Finish Cutting Through High-Speed Dry Turning and Cutting Parameters Optimization Using Taguchi-Based Bayesian Method

Grey cast iron (GCI) is the most common metal casting product in the world. Therefore, achieving sustainable production for this particular material will provide a significant impact on the sustainability issue of metal industries in general. This paper attempts to enhance the sustainability of grey cast iron turning through a high speed, dry turning process. The effect of cutting parameters on three assessments of product sustainability index (PSI) called energy consumption (EC), tool cost (TC), and surface roughness (SR) was investigated using the Taguchi method. The analysis of the signal to noise (S/N) ratio shows that the minimum depth of cut (0.1 mm) gives the optimal performance for all three assessments. Meanwhile, the cutting speed and feed rate reveal a conflict in obtaining the best performance. To help with making a decision, a Taguchi-based Bayesian optimization method was proposed and the capability of the method on suppressing the number of samples to obtain the optimum cutting parameters was demonstrated. Furthermore, the reasons for the optimum cutting condition were also presented.