Models & Optimisation and Mathematical Analysis Journal
Volume 9, Numéro 1, Pages 15-20
2021-12-31
Genetic Algorithm Optimization Of Machining Aisi 52100 Hard Steel.
Authors : Abed Zaki Abdelfetah . Makhfi Souâd . Haddouche Kamel . Habak Malek .
Abstract
This paper presents an elegant optimization by the Genetic Algorithm (GA) approach in the hard turning of AISI 52100 bearing steel with a Cubic Boron Nitrides (CBN) cutting tool. AISI 52100 is bearing steel with high carbon, chromium and manganese content, which has applications in several parts such as drive shafts, cams, bearings, gears, etc. For accurate optimization of hard machining, it is very crucial to know the material characteristics of the workpiece under the extreme conditions imposed during cutting. The bearing steel is used in various metallurgical states characterized by the presence or absence of carbides in a bainitic or martensitic matrix by Scanning Electron Microscopy (SEM). Both states were been studied for identical hardnesses between 45 Hardness Rockwell C (HRC) and 60 HRC. GA optimization is used to identify the optimal combination of machining parameters that maximize the Material Removal Rate (MRR). The non-linear models used for the machining force and cutting power are obtained from Multiple Linear Regression (MLR). The independent variables are workpiece hardness, cutting speed, feed rate, and depth of cut. The results showed that the GA approach holds promise for future multiobjective optimization.
Keywords
AISI 52100 ; Genetic Algorithm ; Hard turning ; Machining ; MLR ; Optimization.
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