P
Prashant Kulkarni
Researcher at University of Michigan
Publications - 13
Citations - 1041
Prashant Kulkarni is an academic researcher from University of Michigan. The author has contributed to research in topics: Slicing & Process (engineering). The author has an hindex of 9, co-authored 12 publications receiving 953 citations.
Papers
More filters
Journal ArticleDOI
A review of process planning techniques in layered manufacturing
TL;DR: In this article, the authors define, conceptualize and review the literature in this emerging area, and conclude with future projections on the possible directions of research in this area and present the essential tasks in LM.
Journal ArticleDOI
An accurate slicing procedure for layered manufacturing
Prashant Kulkarni,Debasish Dutta +1 more
TL;DR: Two factors associated with the slicing procedures used in layered manufacturing processes that introduce geometric inaccuracy are described, and solutions for their redressal are suggested.
Journal ArticleDOI
Deposition Strategies and Resulting Part Stiffnesses in Fused Deposition Modeling
Prashant Kulkarni,Debasish Dutta +1 more
TL;DR: In this article, the effects of different deposition paths on this deposition based LM process are investigated and some variations on the current deposition strategy are also proposed, and the stiffness of parts manufactured by the different strategies is experimentally determined.
Journal ArticleDOI
Region-based adaptive slicing
TL;DR: Whereas in traditional adaptive slicing the user can impose a single surface finish requirement for the whole object, in region-based adaptive slicing, user has the flexibility to impose different surface finish requirements on different surfaces of the model.
Journal ArticleDOI
On the Integration of Layered Manufacturing and Material Removal Processes
Prashant Kulkarni,Debasish Dutta +1 more
TL;DR: In this paper, the authors developed methodologies for the integration of layered manufacturing and traditional material removal processes into an integrated manufacturing setup, and the scenarios under which this integration would be beneficial are identified and algorithms for integration developed.