Date of Award
Doctor of Philosophy
Industrial and Manufacturing Engineering
Dr. Jorge Rodriguez
Dr. Steven E. Butt
Dr. Tarun Gupta
Dr. Azhim Houshyar
Engineering design decisions influence more than 70% of product costs. Various computational analysis tools such as Finite Element Analysis (FEA) are typically utilized, to achieve an effective design cycle. Literature review in 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.
This research bridges these gaps in design process through 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 which utilized the proposed iFEA framework had an average total development cost of only 20% of the cost of projects using traditional methods. 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 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 9 variables and yielded more than 20,000 data points. From this database, knowledge rules were derived for all the four categories of codification. 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 was illustrated, where the value of information provided by the iFEA and its desired level of accuracy is deduced through a decision tree approach.
Narasimhan, Sathyanarayanan, "Virtual Design Verification and Process Improvement of Composite Sheet Material Products Using Intelligent Finite Element Analysis (iFEA)" (2011). Dissertations. 442.