Abstract
The objective of this paper is to introduce an applied systems approach for prediction and optimization of machining performance. Current techniques for assessing machining performance are highly localized and traditionally biased towards the work material. An integrated systems approach i.s proposed for a reliable and consistent assessment of applied machining performance (AMP). Three areas, namely: fundamental modeling, applied modeling and optimization. are fix;used on. Fundamental mixieling entails generic predictive inotlcls for machining performance measures (cutting forces/ torque/power, tool-wear/tiKil-life, chip form/chip breakability, surface roughness/surface integrity, part accuracy, etc.). Applied modeling involves application of the generic mcxlels to a specific machining system, including the interactions of the three major elements: workpiece, machine ttK,l and cutting tool. The subsequent step of optimization predicts the optimized machining performance (OMP).
| Original language | English |
|---|---|
| Title of host publication | Manufacturing Science and Engineering |
| Subtitle of host publication | Volume 2 |
| Pages | 3-12 |
| Number of pages | 10 |
| ISBN (Electronic) | 9780791826799 |
| DOIs | |
| State | Published - 1997 |
| Event | ASME 1997 International Mechanical Engineering Congress and Exposition, IMECE 1997 - Manufacturing Science and Engineering - Dallas, United States Duration: Nov 16 1997 → Nov 21 1997 |
Publication series
| Name | ASME International Mechanical Engineering Congress and Exposition, Proceedings (IMECE) |
|---|---|
| Volume | 1997-W |
Conference
| Conference | ASME 1997 International Mechanical Engineering Congress and Exposition, IMECE 1997 - Manufacturing Science and Engineering |
|---|---|
| Country/Territory | United States |
| City | Dallas |
| Period | 11/16/97 → 11/21/97 |
Bibliographical note
Publisher Copyright:© 1997 American Society of Mechanical Engineers (ASME). All rights reserved.
Funding
New research findings presented in this paper are largely from the various sponsored projects at the University of Kentucky. The authors gratefully acknowledge the generou.s funding of these projects by the National Science Foundation, General Motors Corporation, Ford Motor Company, Kennametal Inc. and the Center for Robotics and Manufacturing Systems at the University of Kentucky. The authors extend their sincere appreciation to Prof. E. J. A. Annarego (University of Melbourne, Australia) and Prof. V. A. Astakhov (Concordia University, Canada) for their constructive comments and encouragement during the preparation of this paper.
| Funders |
|---|
| Center for Robotics and Manufacturing Systems |
| Kennametal, Inc. |
| National Science Foundation (NSF) |
| Ford Motor Company |
| General Motors Corporation |
ASJC Scopus subject areas
- Mechanical Engineering
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