An expert system for the validation and interpretation of x-ray residual stress data
Although widely recognized in the research community as one of the most accurate non-destructive methods for the determination of residual stress in polycrystalline structural materials, X-ray diffraction has not been widely adopted in the field. This is partly due to the fact that such measurements...
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| Format: | Others |
| Language: | en |
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Virginia Tech
2014
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| Online Access: | http://hdl.handle.net/10919/45313 http://scholar.lib.vt.edu/theses/available/etd-10242009-020322/ |
| Summary: | Although widely recognized in the research community as one of the most accurate non-destructive methods for the determination of residual stress in polycrystalline structural materials, X-ray diffraction has not been widely adopted in the field. This is partly due to the fact that such measurements require, most often, a well-trained user with knowledge in both materials and mechanical sciences in addition to the specific know-how of the instrument. We believe that computer assistance could contribute to the promotion of this technique by increasing the productivity and accuracy of these measurements. We have developed a prototype of an expert system, using Nexpert Object's shell, to assist a non-trained operator in the validation and interpretation of X-ray diffraction residual stress data.
The present work describes this prototype which has been designed to confirm the feasibility of the concept. Its knowledge base contains relevant examples of the rules necessary for data validation. The prototype has also validated most of the concepts required for the implementation of a full-scaled version by evaluating all of the major technical features such as graphics representation, external routine calls, and databases access. We have implemented significant rules to validate an experiment, link our expert system with a database management system, develop a superset of data able to receive output from any existing X-ray machine, and are working with a statistical pattern recognition software to discriminate between various d-vs-sin²Psi curves, to classify our data. === Master of Science |
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