Abstract
The advancement of materials science during the last few decades has led to the development of many hard-to-machine materials, such as titanium, stainless steel, high-strength temperature-resistant alloys, ceramics, refractories, fibre-reinforced composites, and superalloys. Titanium is a prominent material and widely used for several industrial applications. However, it has poor machinability and hence efficient machining is critical. Machining of titanium alloy (Grade II) in minimum quantity lubrication (MQL) environment is one of the recent approaches towards sustainable manufacturing. This problem has been solved using various approaches such as experimental investigation, desirability, and with optimization algorithms. In the group of socio-inspired optimization algorithm, an artificial intelligence (AI)-based methodology referred to as Cohort Intelligence (CI) has been developed. In this paper, CI algorithm and Multi-CI algorithm have been applied for optimizing process parameters associated with turning of titanium alloy (Grade II) in MQL environment. The performance of these algorithms is exceedingly better as compared with particle swarm optimization algorithm, experimental and desirability approaches. The analysis regarding the convergence and run time of all the algorithms is also discussed. It is important to mention that for turning of titanium alloy in MQL environment, Multi-CI achieved 8% minimization of cutting force, 42% minimization of tool wear, 38% minimization of tool-chip contact length, and 15% minimization of surface roughness when compared with PSO. For desirability and experimental approaches, 12% and 8% minimization of cutting force, 42% and 47% minimization of tool wear, 53% and 40% minimization of tool-chip contact length, and 15% and 20% minimization of surface roughness were attained, respectively.
Highlights
Machining is a major industrial activity consisting of various processes in which a raw material is cut to a desired final shape and size by a controlled material removal process
It is important to mention that for turning of titanium alloy in minimum quantity lubrication (MQL) environment, Multi-Cohort Intelligence (CI) achieved 8% minimization of cutting force Fc, 42% minimization of tool wear VBmax, 38% minimization of tool-chip contact length L, and 15% minimization of surface roughness Ra when compared with particle swarm optimization (PSO)
This paper presents the application of socio-inspired optimization techniques, viz. CI and its variations as well as Multi-CI for solving real-life machining optimization problem of titanium alloy (Grade II) in MQL environment
Summary
Machining is a major industrial activity consisting of various processes in which a raw material is cut to a desired final shape and size by a controlled material removal process. Cutting power consumption, tool wear, and surface roughness, have been investigated employing dry and MQL machining environment. Three cutting parameters such as cutting speed, cutting feed and depth of cut have been considered. Its several variations and significantly modified version referred to as Multi-CI have been successfully applied for solving real-world optimization problem of Titanium alloy under MQL Environment. The performance responses such as tangential force Fc , tool wear VBmax, surface roughness Ra and tool-chip contact length L have been successfully optimized.
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