Effect of Wiper Edge Geometry on Machining Performance While Turning AISI 1045 Steel in Dry Conditions Using the VIKOR-ML Approach

Author:

Abbas Adel T.1ORCID,Sharma Neeraj2,Soliman Mahmoud S.1ORCID,El Rayes Magdy M.1ORCID,Sharma Rakesh Chandmal3ORCID,Elkaseer Ahmed4ORCID

Affiliation:

1. Department of Mechanical Engineering, College of Engineering, King Saud University, P.O. Box 800, Riyadh 11421, Saudi Arabia

2. Department of Mechanical Engineering, Maharishi Markandeshwar Engineering College, Maharishi Markandeshwar (Deemed to be University), Mullana 133207, India

3. Department of Mechanical Engineering, Graphic Era (Deemed to be University), Dehradun 248002, India

4. Institute for Automation and Applied Informatics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany

Abstract

AISI 1045 can be machined well in all machining operations, namely drilling, milling, turning, broaching and grinding. It has many applications, such as crankshafts, rollers, spindles, shafts, and gears. Wiper geometry has a great influence on cutting forces (Fr, Ff, Fc and R), temperature, material removal rate (MRR) and surface roughness (Ra). Wiper inserts are used to achieve good surface quality and avoid the need to buy a grinding machine. In this paper, an optimization-based investigation into previously reported results for Taguchi’s based L27 orthogonal array experimentations was conducted to further examine effect of the edge geometry on the turning performance of AISI 1045 steel in dry conditions. Three input parameters used in current research include the cutting speed (Vc), feed (f) and depth of cut (ap), while performance measures in this research were Ra, Fr, Ff, Fc, R, temperature (temp) and MRR. The Vise Kriterijumska Optimizacija Kompromisno Resenje (VIKOR) method was used to normalize and convert all the performance measures to a single response known as the VIKOR-based performance index (Vi). The machine learning (ML) approach was used for the prediction and optimization of the input variables. A correlation plot is developed between the input variable and Vi using the ML approach. The optimized setting suggested by Vi-ML is Vc: 160 m/min; ap: 1 mm and f: 0.135 mm/rev, and the corresponding value of Vi was 0.2883, while the predicted values of Ra, Fr, Ff, Fc, R, temp and MRR were 2.111 µm, 43.85 N, 159.33 N, 288.13 N, 332,16 N, 554.4 °C and 21,600 mm3/min, respectively.

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Optimization,Mechanical Engineering,Computer Science (miscellaneous),Control and Systems Engineering

Reference52 articles.

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5. Walker, T. (2015). The MQL Handbook, A Guide to Machining with Minimum Quantity Lubrication, Unist, Inc.. V1.0.7; 2015.

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