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Quantification of geometric and thermal characteristics of machinery is critical to the improvements in part dimensional accuracy and reduction of part to part dimensional variations in a high volume manufacturing operations. Assembly and alignment of different components in a machine result in geometric error over the machining volume of a machine. These errors, once quantified, can be corrected through offsets in positioning controls. The objectives of a good machine design should be to minimize the geometric errors during fabrication and assembly of the components, and replacement of the wear prone components during maintenance of the machine in operations. Thermal errors in machines are even more critical and have not been addressed sufficiently in improving part to part dimensional variations.

Low reliability and cumbersome calibration procedures for commercially available drill breakage detection system were the drivers for the development of a robust system which utilizes time and frequency domain analysis of vibration signatures from the spindle housing. Self learning capabilities in calibration and generic, multidiscriminant based decision making are the novel features of a system proven successful in single spindle applications1. However, use of a single sensor to monitor drill breakage in multi spindle station in a high volume manufacturing operation requires signal enhancement strategies to decipher similar signatures sensed from different spindles. Complexity of the problem increases if the station is one of the several stations in a dial machine, because one needs to consider the transmissivity characteristics between stations installed on a common rotary table.