S.K. Gupta

Biography:
Dr. Satyandra K. Gupta is an Associate Professor in Mechanical Engineering Department and the Institute for Systems Research at the University of Maryland. Prior to joining the University of Maryland, he was a Research Scientist in the Robotics Institute and an Adjunct Assistant Professor of Manufacturing in the Graduate School of Industrial Administration at Carnegie Mellon University. Dr. Gupta received a Bachelor of Engineering (B.E.) degree in Mechanical Engineering from the University of Roorkee (presently known as Indian Institute of Technology, Roorkee) in 1988. He received a Master of Technology (M. Tech.) degree in Production Engineering from Indian Institute of Technology, Delhi in 1989. Dr. Gupta received a Ph.D. in Mechanical Engineering from the University of Maryland at College Park in 1994. Dr. Gupta is a member of American Society of Mechanical Engineers (ASME) and Society of Manufacturing Engineers (SME). He has authored or co-authored more than one hundred forty articles in journal, conference proceedings, and book chapters. Dr. Gupta has won many honors and awards for his academic excellence and his research contribution to design and manufacturing automation area. Awards received by Dr. Gupta include a Best Paper Award in ASME's International Conference on Computers in Engineering in 1994, a Best Paper Award in ASME's Design for Manufacturing Conference in 1999, ONR's Young Investigator Award in 2000, SME's Robert W. Galvin Outstanding Young Manufacturing Engineer Award in 2001, NSF's CAREER Award in 2001, Presidential Early Career Award for Scientist and Engineers (PECASE) in 2001, and a Best Paper Award in ASME's International Conference on Computers and Information in Engineering in 2006. .

Talk Title: Feature Based Part Similarity Assessment

Abstract: Popularity of 3D CAD systems is resulting in a large number of CAD models being generated. Availability of these CAD models is opening up new ways in which information can be archived, analyzed, and reused. 3D geometric information is one of the main components of CAD models. Therefore similarity assessment is a fundamental geometric reasoning problem that finds several different applications. In many design and manufacturing applications, the gross shape of the 3D parts does not play an important role in similarity assessment. Instead certain attributes of part features play a dominant role in determining the similarity between two parts. Different feature-based models are usually created using their own coordinate systems. Therefore, feature-based shape similarity assessment involves finding the optimal alignment transformations for two sets of feature vectors. The optimal alignment corresponds to the minimum value of a distance function that is computed between the two sets of feature vectors being aligned. In order to compute the distance value the closest neighbor to each feature vector needs to be identified. This presentation will cover the following three topics. First, we will describe optimal feature alignment algorithms. These algorithms utilize the partitioning of the transformation space into regions such that the closest neighbor information is invariant within each region. These algorithms can work with customizable distance functions and have polynomial time complexity. The second topic covered in this presentation will be a feature-based shape similarity analysis framework. This framework has been built using the feature alignment algorithms. Finally, we will describe an application of feature-based shape similarity assessment. We believe that the feature-based shape similarity assessment framework described in this presentation will provide the foundations for building a feature-based shape similarity analysis tools that will enable designers to efficiently retrieve archived geometric information. We expect that these tools will facilitate information reuse and therefore decrease product development time and cost.