Evaluation of chip morphology in hard turning using constitutive models and material property data

Gérard Poulachon, Alphonse L. Moisan, I. S. Jawahir

Research output: Contribution to conferencePaperpeer-review

13 Scopus citations

Abstract

Modelling of machining operations requires the use of constitutive relations which could represent as close as possible the material behavior in the primary and secondary zones. The knowledge of these behavior laws involves the use of different types of sophisticated mechanical tests which should provide with sufficient accuracy the material behavior for the relevant conditions of machining. In this paper, firstly, the flow stress of 100Cr6 (AISI 52100) bearing steel in its HV730 hardness state has been identified in order to assess the machinability in case of hard turning. With this, the dependence of the flow stress on strain, strain-rate and temperature, which poses significant difficulty, is presented. Secondly, the material machinability is evaluated with a shear instability criterion, enabling the prediction of chip formation with or without the shear localization. Quick-stop tests have been carried out on the bearing steel treated at different hardness values showing the chip formation variation. Micro-hardness tests performed on these quick-stop test samples show the effects of cutting temperature. A greater understanding of applied machinability is gained through this precise study of work material physical properties and behavior.

Original languageEnglish
Pages179-185
Number of pages7
StatePublished - 2001
Event2001 ASME International Mechanical Engineering Congress and Exposition - New York, NY, United States
Duration: Nov 11 2001Nov 16 2001

Conference

Conference2001 ASME International Mechanical Engineering Congress and Exposition
Country/TerritoryUnited States
CityNew York, NY
Period11/11/0111/16/01

ASJC Scopus subject areas

  • Engineering (all)

Fingerprint

Dive into the research topics of 'Evaluation of chip morphology in hard turning using constitutive models and material property data'. Together they form a unique fingerprint.

Cite this