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In recent times Mechanical and Production industries are facing increasingchallenges related to the shift toward sustainable manufacturing. In thisarticle, machining was performed in dry cutting condition with a newlydeveloped coated insert called AlTiSiN coated carbides coated through scalablepulsed power plasma technique in dry cutting condition and a dataset wasgenerated for different machining parameters and output responses. Themachining parameters are speed, feed, depth of cut and the output responses aresurface roughness, cutting force, crater wear length, crater wear width, andflank wear. The data collected from the machining operation is used for thedevelopment of machine learning (ML) based surrogate models to test, evaluateand optimize various input machining parameters. Different ML approaches suchas polynomial regression (PR), random forest (RF) regression, gradient boosted(GB) trees, and adaptive boosting (AB) based regression are used to modeldifferent output responses in the hard machining of AISI D6 steel. Thesurrogate models for different output responses are used to prepare a complexobjective function for the germinal center algorithm-based optimization of themachining parameters of the hard turning operation.

Machine learning based modelling and optimization in hard turning of AISI D6 steel with newly developed AlTiSiN coated carbide tool