Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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Author: search.filters.author.Balasubramanian, SundarSubject: search.filters.subject.Systems Integration Methodology

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    Fixture-Based Design Similarity Measures for Variant Fixture Planning
    (1999) Balasubramanian, Sundar; Herrmann, Jeffrey W.; ISR
    One of the important activities in process planning is the design of fixtures to position, locate and secure the workpiece during operations such as machining, assembly and inspection. The proposed approach for variant fixture planning is an essential part of a hybrid process planning methodology.

    The aim is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. Thus, the variant approach exploits this existing knowledge.

    However, since calculating each fixture's feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures based on a surrogate design similarity measure. Then, it evaluates the definitive usefulness metric for those promising fixtures and identifies the best one for the new design.

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    Using Neural Networks to Generate Design Similarity Measures
    (1999) Balasubramanian, Sundar; Herrmann, Jeffrey W.; Herrmann, Jeffrey W.; ISR
    This paper describes a neural network-based design similarity measure for a variant fixture planning approach. The goal is to retrieve, for a new product design, a useful fixture from a given set of existing designs and their fixtures. However, since calculating each fixture feasibility and then determining the necessary modifications for infeasible fixtures would require too much effort, the approach searches quickly for the most promising fixtures. The proposed approach uses a design similarity measure to find existing designs that are likely to have useful fixtures. The use of neural networks to generate design similarity measures is explored.This paper describes the back-propagation algorithm for network learning and highlights some of the implementation details involved. The neural network-based design similarity measure is compared against other measures that are based on a single design attribute.