Paper Title:
Predictive Modeling and Optimization of High Performance Machining
  Abstract

High performance machining refers to the material removal operation that delivers the maximum achievable part quality, process competitiveness, and ecological compatibility through strategic utilization of cutters, machine tools, operation configuration, and process parameters. It is rapidly emerging as a prerequisite to productivity and profitability of machining operations and associated manufacturing systems. To accomplish high performance machining, a thorough understanding of the underlying mechanics that affect the performance attributes such as tool life, part integrity, air quality, etc., and how it is attributed to tooling conditions, operation configuration, and process parameters, is required. This paper reviews and summarizes a series of analytical methodologies by coupling with studies performed at the Georgia Institute of Technology for the quantitative modeling of fundamental mechanics of machining in the context of thermal, mechanical, tribological, and metallurgical effects and their interactions. In this study, cutting stresses, residual stress and tool life are explicitly described as functions of tool geometries, cutting speed, chip load, cutting fluid properties, interface tribological conditions, and the cutter/workpiece material constants. These analytical models facilitate the prediction of machining performance thereby allowing the optimal planning of machining processes in pursuing maximum performance. An array of experimental cutting data is also presented in comparison to model-based predictions for the validation of all aspects of the machining mechanics analysis.

  Info
Periodical
Edited by
Kai Cheng, Yingxue Yao and Liang Zhou
Pages
842-849
DOI
10.4028/www.scientific.net/AMM.10-12.842
Citation
S. Y. Liang, B. M. Abraham, "Predictive Modeling and Optimization of High Performance Machining", Applied Mechanics and Materials, Vols. 10-12, pp. 842-849, 2008
Online since
December 2007
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Price
$32.00
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