Table 5.

Developed machining process optimization for tool wear.

Approach	Objective	Methods	Feedback	Machining Process
Offline	Tool wear [149]	An experimental approach using RSM is developed to identify the most significant cutting parameters on surface roughness, flank wear, and acceleration of drill vibration velocity. The optimal parameters are determined using a multi-response optimization algorithm	Acousto-Optic Emission (AOE) signal (laser Doppler vibrometer)	Drilling
Online	Tool wear [144]	A multi-objective optimization of flank tool wear, cutting forces, and machining vibrations is developed using an experimental RSM-based approach	Cutting forces and vibrations	Turning
Offline	Tool wear [150]	An experimental procedure is conducted to minimize the flank wear and crater using regression modelling, desirability analysis, and GA algorithms in the machining of Al alloy and SiC composites	-	Turning
Offline/Online	Tool wear control [21]	Taguchi experimental design and optimization are used to minimize flank wear in the machining of AISI 1050 material, considering cutting speed, feed rate, and tool tip type as the inputs	Tangential cutting force and AE signals	Turning
Offline/Online	Tool wear control [151]	Model-based force-wear predictor along with delamination and/or thermal damage estimator [152]—stepwise decision making	Motor power signal	Drilling
Offline/Online	Tool wear control [129]	Multi-objective optimization to minimize tool wear and surface roughness and maximize MRR is developed based on an adaptive neuro-fuzzy inference system (ANFIS) for modelling and the vibration and communication particle swarm optimization (VCPSO) algorithm for the optimization	Cutting forces	Milling