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|Title:||Inverse Engineering: A Machine Learning Approach to Support Engineering Synthesis|
|Author(s):||Rao, R. Bharat|
|Doctoral Committee Chair(s):||Lu, Stephen C-Y|
|Department / Program:||Electrical Engineering|
|Degree Granting Institution:||University of Illinois at Urbana-Champaign|
|Subject(s):||Engineering, Electronics and Electrical
|Abstract:||This research presents a knowledge processing methodology called inverse engineering, that uses machine learning techniques for early stage design in parameterized domains. This methodology functions as a model translator, changing the representation of analysis knowledge embedded in a unidirectional simulator, into a multidirectional model that supports design synthesis. This methodology requires addressing two issues.
The first is the task of learning models from data in specified representations. This thesis describes an empirical learning algorithm called KEDS, the Knowledge-based Equation Discovery System. The user selects a restricted hypothesis space bias in the form of a class of parameterized (polynomial) model families, and KEDS learns accurate models that are restricted to those forms. In addition to being a model-driven empirical discovery system, KEDS is also a conceptual clustering system that partitions the problem domain based upon the relationships that it discovers among the problem variables. The use of the minimum description length (MDL) principle as a preference bias for KEDS provides a foundation for learning the "best" models (i.e., those that minimize predictive error on unseen data). KEDS has been applied to model three real-world domains: a diesel engine combustion chamber, a CMOS circuit for an operational amplifier, and a turning process on a lathe.
The second issue is that of supporting early stage design. Current computer-aided methods for product and process design require the iterative use of computer-based analysis models in a generate-and-test fashion. While this process is essential to optimize performance during the final stages of design, it has a number of disadvantages during early design. By restricting the models families used by KEDS to forms that can provide synthesis support (hyperplanes), the user can learn a multidirectional model. The user can use this model to propagate constraints in the analysis as well as the synthesis direction. This avoids the time-consuming traditional procedure of iteratively using analysis models to support synthesis. Further this multidirectional model provides the user with great flexibility during early stage design, and the valuable ability to perform "What-if?" analysis. The inverse engineering methodology has been successfully applied to learn models to support early product design of combustion chambers for diesel engines, and to support process design for a turning machine.
Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1993.
|Date Available in IDEALS:||2014-12-16|
This item appears in the following Collection(s)
Dissertations and Theses - Electrical and Computer Engineering
Dissertations and Theses in Electrical and Computer Engineering
Graduate Dissertations and Theses at Illinois
Graduate Theses and Dissertations at Illinois