Multi-response optimization of the turn-assisted deep cold rolling process parameters for enhanced surface characteristics and residual stress of AISI 4140 steel shafts

P. R. Prabhu, S. M. Kulkarni, Sathyashankara Sharma

Research output: Contribution to journalArticlepeer-review

Abstract

Surface and near-surface areas play an important role as far as safety and dependability ofengineering components particularly when it is subjected to fatigue loading. By applyingdiverse mechanical surface enhancement (MSE) strategies, close to surface layers can becustom-made bringing about enhanced fatigue strength. MSE methods are used to gener-ate surface hardened components without the time and energy-consuming heat treatment.Deep cold rolling (DCR) is one such method that can be employed where the mechanicalenergy induced enables surface-hardening of steels and thereby the combination of hard-ening and finishing in one single step. The objective of this work is to enhance residualstress and near-surface properties of AISI 4140 steel which is the most commonly usedmaterial in the automobile and aerospace industry. The samples were first turned and thendeep cold rolled with various process parameters. Microstructure, surface hardness, sur-face finish, fatigue life, and residual compressive stress after the treatment were examined.Response surface methodology (RSM) and desirability function approach (DFA) was used torelate the empirical relationship between the various process variables and responses andalso to determine the optimum parameter settings for better responses. Further, numericalsimulation of turn-assisted deep cold rolling (TADCR) process was done by utilizing ANSYS-LS-DYNA software to understand the state of residual stress under various treating settings.Confirmation experiments conducted with the optimum parameter setting to validate theimprovements in response and it is found that the deviation between optimum predictedand confirmatory experimental values is about 5%.

Original languageEnglish
Pages (from-to)11402-11423
Number of pages22
JournalJournal of Materials Research and Technology
Volume9
Issue number5
DOIs
Publication statusPublished - 2020

All Science Journal Classification (ASJC) codes

  • Ceramics and Composites
  • Biomaterials
  • Surfaces, Coatings and Films
  • Metals and Alloys

Fingerprint Dive into the research topics of 'Multi-response optimization of the turn-assisted deep cold rolling process parameters for enhanced surface characteristics and residual stress of AISI 4140 steel shafts'. Together they form a unique fingerprint.

Cite this