Academic excellence in manufacturing engineering - Wu, S.M.; Stephenson, D.A. (Robotics and computer-integrated manufacturing, 1984)
Manufacturing Systems are inherently complex and interdisciplinary, and are normally analyzed in a piecewise fashion using experimental techniques which provide relatively little physical insight or theoritical methods brrowed from other disciplines (e.g., structural mechanics, control theory, etc.). For these reasons Manufacturing Engineering is often considered an unscientific and intuitive subject. With the increasing demand for manufacturing systems to operate at higher production rates without human intervention to reduce manufacturing costs, it is becoming increasingly important to develop scientifically based, general, and efficient analysis tools specifically tailored to the complex interdisciplinary problems encountered in Manufacturing Engineering. In addition to having direct practical benefits, such tools would stimulate academic interest in this field and help alleviate current academic and industrial personnel shortages in the manufacturing area. This paper describes one such analysis tool, the Dynamic Data System (D.D.S.) methodology, which has been developed at the University of Wisconsin. The D.D.S. methodology combines time series and systems analysis concepts in a computer-based modeling strategy for obtaining a physically meaningfully model of a system directly from input and output data in the form of stochastic difference/differential equations. The methodology can be applied to forecasting, control, system identification, characterization, signature analysis, and design. The basic features of the methodology, representative applications to the on-line detection and suppression of chatter in turning and the active compensation for roundness errors in boring, and areas for future development are discussed.