Automated Generation of Automated Visual Inspection (AVI) Routines for Surface Mounted Assembly Systems

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This research proposes the development of a methodology that will result in the automated generation of inspection algorithms of new surface mounted Technology (SMT) components. The envisioned methodology will make possible the development of automated visual inspection (AVI) systems that develop and optimize their own inspection algorithms for new components. Under this notion the resulting development framework will minimize the intervention of human developers to obtain inspection algorithms that meet minimal performance measures in terms of component discrimination and sensitivity to changes in environmental variables. This methodology will be based on a vector discrimination approach for the identification of defective components whose basic characteristics have already been developed. This vector approach, in conjunction with a hierarchical classification, will be used to obtain the envisioned methodology. This methodology does not seek the replacement of the human developer; the proposed framework will free him/her from tedious tasks, so that s/he can focus on more complex tasks such as the development of morphology-based features that may be needed for more sophisticated inspection tasks. If successful, this research will significantly shorten the time required to develop inspection algorithms for SMT components. This reduction in development time will result in added manufacturing flexibility that will translate into more rapid introduction of new products into existing manufacturing lines and shorter design lead-times. This flexibility will contribute to give a competitive advantage to the electronics industry, which has a clear strategic importance for the USA. It is also expected that as a direct result of the funded activities the PI expand the concept of multi-disciplinary pyramidal research teams. This concept has given excellent results in terms of recruiting undergraduate students to continue on to graduate school and also in terms of the participation of minority students in research activities.