Improving Manufacturing Precision Using the Karhunen

The status of fault patterns on part surfaces can provide valuable information about the condition of the manufacturing system. In this work, we aim to develop a reliable fault detection and diagnosis tool in order to assure the automated production of high-quality parts. Such a tool provides a means of integrating the manufacturing and design phases. Accurate detection of the part surface condition in manufacturing ensures the fault-free design of the manufacturing parameters and machine components. This paper introduces a mathematical transform that has the potential to detect faults in manufacturing machines. Specifically , the paper focuses on the decomposition of complex signals to allow the detection of faults. The Karhunen-Lo eve transform is investigated by means of numerically-generated signals. Numerical signals are studied to decompose a variety of signals, including deterministic, stochastic, stationary, and nonstationary signals. Finally, the potential utility of the proposed technique is discussed in the context of a newly-maturing manufacturing process. In the era of intelligent manufacturing, it is crucial that designers and manufacturers rely on the exchange of accurate information about part production. The surface precision of a part produced during manufacturing is a crucial source of information, often not accurately known to the In this work, we aim to improve and predict the part production process. Two tasks are crucial in accomplishing this goal: (1) quantifying the surface precision of parts produced from a manufacturing process; and, (2) designing/redesigning manufacturing machines and/or choosing machine parameters to produce parts with improved precision. The former is accomplished by introducing a mathematical means of detecting faults on surface prooles. The latter is accomplished by introducing a mathematical means of diagnosing the origin of these faults. In this work, we aim to close the design and manufacturing loop by providing accurate information about the fault status of parts and machine components. The detection and diagnosis of faults in manufacturing systems can provide valuable information for manufacturers and designers. Manufacturers beneet from a fault-free part production , while designers beneet from an accurate isolation of design problems. The elds of fault diagnosis and mechanical signature analysis have a signiicant history deviations in observed behavior from a set of \normal" behaviors. In this work, faults are monitored based on signals measured from a manufacturing system, such as part surface deviations and machine tool vibrations. Fault detection is the recognition of an unacceptable behavior; and fault diagnosis is the identiication of …