The larger objective of this Sensors and Sensors Networks (Sensors) research project is to develop sturdy, consistent and adaptive methods for sensing in manufacturing. Within this framework, the focus of primary research is in prudent and yet accurate feature verification using coordinate measuring machines. The processing of parts results in surface errors that will be quantified using process physics prior to feature verification. Sensing will be guided by the geometry of the part and these prior process models. Support Vector Machine (SVM) represents a new type of learning machine based on statistical learning theory. Methodologies based on the SVMs and learning theory will be applied to integrate sampling and zone determination in part verification. Research will focus efforts on developing and evaluating search methods for sample reduction using SVM, and on quantifying errors generated during processing. Methodologies for non-linear forms and profile metrology will follow. Extensions will be investigated for full-part metrology and Reverse Engineering (RE). Suitability of proposed sensing concepts to surface metrology and tribology will also be investigated. Research will be deployed to classrooms and computer modules prepared for wider dissemination.
The success of this research for an adaptive procedure will lend itself for use to a wider domain of sensing, but the principal focus for this research is in coordinate metrology for part verification. Successful application will improve product and process designs and interchangeability. New generation software for coordinate metrology incorporating learning will also result. This research will lead to metrology standards as well as present improved solutions to the inspection enterprise. The two principal concepts embedded in this project are knowledge-based sensing and mathematical search. These concepts can be extended to other manufacturing sensing applications. Introduction of SVMs to manufacturing is also expected to lead to other applications. Significant potential exists in education and training in each area of manufacturing, metrology, and operations research/data mining.
