  In recent times Mechanical and Production industries are facing increasing
challenges related to the shift toward sustainable manufacturing. In this
article, machining was performed in dry cutting condition with a newly
developed coated insert called AlTiSiN coated carbides coated through scalable
pulsed power plasma technique in dry cutting condition and a dataset was
generated for different machining parameters and output responses. The
machining parameters are speed, feed, depth of cut and the output responses are
surface roughness, cutting force, crater wear length, crater wear width, and
flank wear. The data collected from the machining operation is used for the
development of machine learning (ML) based surrogate models to test, evaluate
and optimize various input machining parameters. Different ML approaches such
as polynomial regression (PR), random forest (RF) regression, gradient boosted
(GB) trees, and adaptive boosting (AB) based regression are used to model
different output responses in the hard machining of AISI D6 steel. The
surrogate models for different output responses are used to prepare a complex
objective function for the germinal center algorithm-based optimization of the
machining parameters of the hard turning operation.
