Performance-Based Predictive Models and Optimization Methods for Turning Operations and Applications: Part 3—Optimum Cutting Conditions and Selection of Cutting Tools |
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| Institution: | 1. Human Medical Genetics and Genomics Program, University of Colorado School of Medicine, Aurora, Colorado, USA;2. Department of Dermatology, University of Colorado School of Medicine, Aurora, Colorado, USA;3. Department of Pathology, King Edward Medical University, Nelagumbad, Anarkali, Lahore, Pakistan;4. Department of Dermatology Unit-II, King Edward Medical University, Nelagumbad, Anarkali, Lahore, Pakistan;5. Department of Biochemistry, Faculty of Science, Sayajigunj, The M.S. University of Baroda, Vadodara, Gujarat, India;6. Department of Pediatrics, University of Colorado School of Medicine, Aurora, Colorado, USA;7. Barbara Davis Center for Childhood Diabetes, University of Colorado School of Medicine, Aurora, Colorado, USA |
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| Abstract: | This paper presents a summary of recent developments in developing performance-based machining optimization methodologies for turning operations. Four major machining performance measures (cutting force, tool wear/tool life, chip form/chip breakability, and surface roughness) are considered in the present work, which involves the development and integration of hybrid models for single and multi-pass turning operations with and without the effects of progressive tool wear. Nonlinear programming techniques were used for single-pass operations, while a genetic algorithms approach was adopted for multi-pass operations. This methodology offers the selection of optimum cutting conditions and cutting tools for turning with complex grooved tools. |
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