Analysis and optimization in hard turning of titanium grade-I using grey relational analysis

Pandey, Banarsi; Mahto, Sachindra; Jha, Bharat

doi:10.1007/s12008-022-00924-y

Cited by 3 publications

(1 citation statement)

References 29 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Optimal parameters are spindle speed 910 rev/min, feed rate 0.194 mm/rev, depth of cut 0.2 mm and tool nose radius 0.2 mm. Parametric optimisation of titanium hard turning parameters was done by Pandey et al [11]. They found that the most important element is depth of cut.…”

Section: Introductionmentioning

confidence: 99%

Multi objective optimization of process parameters in hard turning of AISI 52100 steel with surface irregularities using GRA-PCA

Ponugoti,

Koka,

Dantuluri

2023

Eng. Res. Express

View full text Add to dashboard Cite

Dry hard-turning is a cost-effective, efficient manufacturing method for AISI 52100 hardened bearing steel. Surface Defect Machining (SDM) is a novel approach to address surface roughness, deteriorations, residual stresses, and metallurgical changes on machined steel. SDM involves exposing workpieces to surface irregularities, reducing cutting resistance, and enhancing surface integrity and finish. In the present work, surface irregularities are formed on the surface of the workpiece in the form of indentations. Using the response surface method's central composite design (CCD), 32 experimental runs were conducted to determine the optimal process parameters by varying the cutting and tool geometry parameters while AISI52100 steel hard turning (HT). Due to its complexity, multi-objective optimization is more challenging to study.The present work aims to evaluate the effects of input parameters on maching force, surface roughness, and workpiece surface temperature. Further, machining parameters optimization is performed employing the Grey relational analysis integrated with principal component analysis (GRA-PCA). Analysis of variance (ANOVA) was used to examine the impact of cutting and tool geometry parameters on grey relational grade (GRG). ANOVA revealed that feed has the highest influence on GRG, followed by depth of cut, nose radius, cutting speed, and negative rake angle. Cutting speed of 800 rpm, feed rate of 0.04 mm/rev, depth of cut of 0.5 mm, nose radius of 1 mm, and negative rake angle of 15º are the optimum combination of process parameters.

show abstract

Section: Introductionmentioning

confidence: 99%