Flexible optimization for drilling in composite and metal stacks /
| Main Author: | |
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| Other Authors: | , , |
| Format: | Thesis Book |
| Language: | English |
| Published: |
1993.
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| Subjects: | |
| Online Access: | ProQuest, Abstract Link to OAKTrust copy |
| Abstract: | Drilling tests in composite and metal stacks were conducted in close collaboration with several industrial sponsors. These tests indicate a need to enhance raw drilling data by applying optimization techniques. The diversity of materials and criteria for acceptable holes requires flexibility with regard to mathematical models, objective functions, constraints, and analysis methods. Flexible optimization needs to be a part of an overall process planning system that impacts the test plan, experimentation, and modeling, and takes advantage of other test data. This research to meet these needs is organized into two phases. The processes of creating and solving various optimization models are developed and applied to practical problems in the first phase. The constrained problems with traditional extended Taylor and Colding models are solved and compared to the unconstrained problem. The concept of cutting rate-tool life (R -T) characteristic curve is extended to the constrained machining economics problem and defined as the constrained R-T characteristic curve. The methods to determine the constrained R -T characteristic curve are presented for posynomial and lognomial formulations. New quality objective functions and constraints are introduced for quality optimization. The methodology for including quality in a wear test is made possible by including hole number as a variable in the mathematical modeling. A machining economics problem is formulated based on quality and the tool life is extrapolated in the quality test stage. In the second phase of this study, a drilling operations planning system (DOPS) for composite and metal stacks is developed and implemented based on the findings of the first phase and accumulated data. The computer-aided system, including all steps of process planning: testing, modeling, and optimization, is demonstrated on eight original data sets combined into one data base. With this system, it is possible to reproduce the statistical analysis of the originators on the individual data sets and, in addition, conduct an optimization in a matter of minutes. The implication is that analyses could be quickly tailor made for different companies with different criteria. |
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| Item Description: | Vita. "Major subject: Mechanical Engineering." |
| Physical Description: | xi, 131 leaves : illustrations ; 28 cm |
| Bibliography: | Includes bibliographical references. |