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. / Graduation date: 1999

Thesis (M.S.)--Brigham Young University. Dept. of Mechanical Engineering, 2004. / Includes bibliographical references (p. 87-89).

Monitoring, diagnosis and prediction of failures play key roles in automatic supervision of machine tools. They have received much attention because of the potential for reduced maintenance expenses, down time, and an increase in the equipment utilization level. At present, signal analysis techniques are predominantly used. But methods involving system analysis are capable of providing more reliable information, especially for predictive applications of supervision. System analysis involves comprehensive analytical models combined with techniques developed in control theory, and experimental modal analysis. The primary objective of this research is to develop a methodology to monitor critical physical parameters of mechanical systems, which are difficult to measure directly. These parameters are inherent features of constitutive rigid body models. A method for computer aided model generation developed in this thesis leads to a gray box model structure by which physical parameters can be estimated from experimental data. Lagrange's energy formalism, linear algebra and homogenous transformations are used to promote parsimonious three-dimensional model building. A software environment allowing symbolic and arbitrary precision computations facilitates efficient mapping of physical properties of the actual system into specific quantities of the analytical model. Six different methods are postulated and analyzed in this thesis to estimate physical parameters such as masses, stiffnesses and damping coefficients. Implementation of this methodology is a prerequisite for the design of an on-line monitoring and diagnosis system, which can detect and predict process faults. Two mechanical systems are used to validate the proposed methods: (1) A simple multi degree-of-freedom (MDOF) system and (2) a machine tool spindle assembly. A practical application of physical parameter estimation is proposed for preload monitoring in high-speed spindles. Preload variations in the bearing can lead to thermal instability and bearing seizure. The feasibility of using accelerometers located on the spindle housing to estimate bearing preload is evaluated. The optimal environment for continuation of this research is collaboration with machine tool companies to incorporate the proposed methodology (or parts of it) into current design practices. / Graduation date: 1999