Experimental investigation and optimization using regression genetic algorithm of hard turning operation with wiper geometry inserts. (2020)

• Record Type: Journal Article
• Authors: Kumar, Amardeep; Pradhan, Sharad K.; Jain, Varun
• Is Part Of: Materials today. Volume 27:Part 3(2020)
• Page Start: 2724
• Page End: 2730
• Publication Date: 2020
• Subjects: CNC turning -- MRR-material removal rate -- Multi-objective optimization -- Residual stresses -- Taguchi's experimental design approach -- Surface roughness; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2019.12.191
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: The major objective of today's industries is to increase productivity and quality of product at low cost with minimum energy consumption which in turn lead to economic and eco-friendly manufacturing. Hard turning with the tool having wiper geometry possess these advantages over conventional turning process followed by a grinding process. Hard turning with wiper geometry tool is a primary metal cutting process which is extensively used for the reduction of the diameter of work piece to specified dimension and generation of smooth surface finish on the work piece material having a hardness greater than 45 HRC without supplementing it with grinding. In the present research work, for experimentation, hardened EN 24 and CBN are selected as work piece and cutting tool material respectively. Major influential process parameters viz. feed, cutting velocity and depth of cut are taken for experimentation while Material Removal Rate (MRR), Surface roughness (SR), Temperature (T) at tip of cutting tool, Strain equivalent to cutting force and feed force developed in cutting tool holder are taken as response parameter. During turning operation, surface roughness (SR), temperature, cutting strain and feed strain are desired to be minimum while material removal rate is desired to be maximum. To get the desired quality of product with minimum uses of resources, the investigation into the effect of process parameter on performance parameter and its optimization is necessary. In this research attempt, Taguchi, ANOVA, Regression techniques and Genetic Algorithm are employed to optimize the identified process parameters.