Recent advances in modelling of metal machining processes

P. J. Arrazola, T. Özel, D. Umbrello, M. Davies, I. S. Jawahir

Research output: Contribution to journalArticlepeer-review

649 Scopus citations

Abstract

During the last few decades, there has been significant progress in developing industry-driven predictive models for machining operations. This paper presents the state-of-the-art in predictive performance models for machining, and identifies the strengths and weaknesses of current models. This includes a critical assessment of the relevant modelling techniques and their applicability and/or limitations for the prediction of the complex machining operations performed in industry. This paper includes contributions from academia and industry, and is expected to serve as a comprehensive report of recent progress, as well as a roadmap for future directions. Process models often target the prediction of fundamental variables such as stresses, strains, strain-rates, temperatures etc. However, to be useful to industry, these variables must be correlated to performance measures: product quality (accuracy, dimensional tolerances, finish, etc.), surface and subsurface integrity, tool-wear, chip-form/breakability, burr formation, machine stability, etc. The adoption of machining models by industry critically depends on the capability of a model to make this link and predict machining performance. Therefore, this paper would identify and discuss several key research topics closely associated with predictive model development for machining operations, primarily targeting industry applications.

Original languageEnglish
Pages (from-to)695-718
Number of pages24
JournalCIRP Annals - Manufacturing Technology
Volume62
Issue number2
DOIs
StatePublished - 2013

Keywords

  • Chip formation
  • Machining
  • Modelling

ASJC Scopus subject areas

  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

Fingerprint

Dive into the research topics of 'Recent advances in modelling of metal machining processes'. Together they form a unique fingerprint.

Cite this