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A number of analytical tools have been used, without any significant success, in the machine manufacturing industry to predict performance of machining processes. The overall equipment efficiency numbers realized on the plant floors provide the supporting evidence of a need of considerable improvements and offer an opportunity for the development of methodologies to characterize the machining processes before installation on the plant floor. Mechanical vibrations signature analysis approach has been used to characterize machine components on the plant floor, but in a limited capacity and under idle conditions. However, attempts to establish vibration standards in machining process characterization without exercising machining loads can cause a false sense of security. This paper describes a methodology of characterizing a given machining process by considering the machine structure, tooling and spindle, workpiece fixture, and the part as the major elements of the machining process.

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.

There is an ever increasing demand on part quality and tighter tolerances for machining of components in high volume manufacturing. A major source of problem in the machine tools is the thermally induced error due to thermal gradients and uneven heating and expansion of various machine components. Current practice of manufacturing precision parts involves periodic gaging of parts, whereby, production is interrupted and manual compensating offsets are input to the controller. Also, additional production costs are introduced due to requirement of initial warm up cycles without cutting parts and utilization of chillers for temperature controlled coolants. In this paper, a methodology is described for automatic compensation for thermal error by means of components/locations temperature profile and calculated error between the tool tip and the workpiece.