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Doctoral Dissertation Announcement
Candidate: Sathyanarayanan Narasimhan
Doctor of Philosophy
Department: Industrial and Manufacturing Engineering
Title: Virtual Design Verification and Process Improvement of Composite Sheet Material Products Using Intelligent Finite Element Analysis (iFEA)
Dr. Jorge Rodriguez, Chair
Dr. Steven Butt
Dr. Azim Houshyar
Dr. Tarun Gupta
Dr. Joshua Naranjo
Date: Friday, April 23, 2010 3:00 p.m. - 5:00 p.m.
College of Engineering and Applied Sciences, Room C258
Engineering design decisions influence more than 70% of the product cost. Various tools are utilized within the entire engineering design process, including computational analysis tools such as Finite Element Analysis (FEA), to achieve an effective and efficient design cycle. Literature reviewed in the design process indicates a striking reality that about 75% of design errors can be eliminated through analysis and about 20% of analyses are misrepresented, leading to inadequate or faulty design. Also, analyses in general generate more information about the problem than is often looked into, leading to a vast potential to study the knowledge creation from the analysis and reuse perspective. This research is aimed at bridging these gaps in design process through the efficient use of computer-based analysis tools. An iFEA framework was successfully developed to proactively utilize predictive FEA analysis. This framework was successfully validated in the product development process of thermoforming headliners. Projects that utilized the proposed iFEA framework had an average total development cost of only 20% of the cost of projects using traditional methods used in industry. In order to achieve this result, the following aspects were successfully developed.
A hot stretch test and an inverse engineering method were developed to characterize a wide array of composite sheet material. This method yielded a very high quality stress-strain relationship for the material for use in forming analysis. Correlation of more than 99% of with actual test data was achieved in all 46 cases used to verify the robustness of this method.
A predictive FEA was developed to successfully simulate thermoforming headliners. The strain-based correlation between predicted values from FEA and actual measurement showed a correlation of more than 90% in 19 out of 21 cases.
A virtual DOE method was successfully devised to aid explicit knowledge codification for headliner thermoforming. The virtual DOE method analyzed more than 350 combinations of nine variables and yielded more than 20,000 data points. From this database, knowledge rules were derived for all the four categories of codification such as abstraction, structuring, prescribing and embedding. Guidelines on best practice use of iFEA framework for knowledge detection, assessment and transfer were developed.
A pragmatic approach to the development of an iFEA framework is illustrated, where the value of information provided by the iFEA and its desired level of accuracy is deduced through a decision tree approach. Since a development of iFEA framework for a particular application is very intensive in both time and cost, the proposed approach will aid to justify a development of this magnitude.