Predictive modeling and optimization of turning operations with complex grooved cutting tools for curled chip formation and chip breaking

I. S. Jawahir, A. K. Balaji

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

This paper presents a summary of the most recent developments in predictive modeling and optimization of turning operations. While almost all previous models for turning operations deal only with flat-faced tools, the present work presents the more practical turning operations involving the use of complex grooved tools for producing 3D chip flow, curl and breaking. The paper provides details of the analytical modeling efforts including the development and validation of the universal slip-line model for 2D curled chip formation. Side-curl and up-curl dominated 3D chip formation process is then discussed with case studies for predicting 3D chip curl using a newly developed Equivalent Toolface (ET) model followed by a description of analytical models and experimental work on 2D and 3D cyclic chip formation and chip breaking involving variable tool-chip contact. At the end, new tools and techniques used in the machining process optimization for optimum machining performance including cutting tool selection are presented in the paper. This covers nonlinear programming methods, genetic algorithms and the use of traditional and hybrid models for single-pass and multi-pass turning operations performed using complex grooved tools. The major machining performance measures considered in the optimization process include cutting forces, tool-wear/tool-life, surface roughness, chip-form/chip breakability and material removal rate. The paper places an emphasis for a machining systems approach to include the integrated effects of workpiece, cutting tool and machine tool.

Original languageEnglish
Pages (from-to)399-443
Number of pages45
JournalMachining Science and Technology
Volume4
Issue number3
DOIs
StatePublished - 2000

Bibliographical note

Funding Information:
The authors gratefully acknowledge the research support of National Science Foundation (Projects: DDM 9311819, DMII 9624640 and DMII 9713932) and the Center for Robotics and Manufacturing Systems at the University of Kentucky. The work presented in this paper is largely drawn from the completed graduate research by the first author's former Ph.D. students (X.D. Fang, R. Ghosh, Z. J. Da, L. Wang, Jae-Um, P. X. Li, A. K. Balaji, and M. Lin). Their hard work and contributions are recognized. Several colleagues helped to develop a modest machining research program at the University of Kentucky. This includes Professors K. Rouch, J. P. Sadler, O. W. Dillon, A. T. Male and K. Saito. Their contribu-

ASJC Scopus subject areas

  • General Materials Science
  • Mechanical Engineering
  • Industrial and Manufacturing Engineering

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