Computer-aided model generation and validation for dynamic systems Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/ft848s96f

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  • The primary goal of any model is to emulate, as closely as possible, the desired behavioral phenomena of the real system but still maintain some tangible qualities between the parameters of the model and the system response. In keeping with this directive, models by their very nature migrate towards increasing complexity and hence quickly become tedious to construct and evaluate. In addition, it is sometimes necessary to employ several different analysis techniques on a particular system, which often requires modification of the model. As a result, the concept of versatile, step-wise automated model generation was realized as a means of transferring some of the laborious tasks of model derivation from the analyst to a suitable program algorithm. The focus of this research is on the construction and verification of an efficient modeling environment that captures the dynamic properties of the system and allows many different analysis techniques to be conveniently implemented. This is accomplished through the implementation of Mathematica by Wolfram Research, Inc.. The presented methodology utilizes rigid body, lumped parameter systems and Lagrange's energy formalism. The modeling environment facilitates versatility by allowing straightforward transformations of the model being developed to different forms and domains. The final results are symbolic expressions derived from the equations of motion. However, this approach is predicated upon the absence of significant low frequency flexible vibration modes in the system. This requirement can be well satisfied in the parallel structure machine tools, the main subject of this research. The modeling environment allows a number of techniques for validation to be readily implemented. This includes intuitive checks at key points during model derivation as well as applications of more traditional experimental validation. In all presented cases the analysis can be performed in the same software package that was used for model development. Integration of the generation, validation, and troubleshooting methodology delineated in this research facilitates development of accurate models that can be applied in structure design and exploitation. Possible applications of these models include parameter identification, visualization of vibration, automated supervision and monitoring, and design of advanced control strategies for minimization of dynamic tool path errors. The benefits are especially prevalent in parallel structure machine tools, where there is still a lack of experience. Latest developments in measurement techniques and the emergence of new sensors facilitate reliable validation and optimization of the models.
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