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This paper summarizes the design and implementation of a model-based torque control strategy for drilling. During drilling, the torque often increases due to difficulties with chip evacuation from the drill flutes. Excessive torque can accelerate tool wear or cause torsional failure of the drill. To avoid problems associated with excessive torque, closed loop torque control by manipulation of feedrate was pursued. This strategy simultaneously avoids tool breakage and decreases the cycle time compared to conventional practice. There can be significant cost benefits of torque control due to eliminating tool breakage. For example, reductions in scrap, rework, and machine maintenance costs may be realized. Dynamic models were developed for the drive system, sensing system, and drilling process. These models were subsequently used to design a model-based torque control strategy. Experimental results are presented for conventional twist drilling and form tool drilling applications.

This paper presents a description of intelligent sensor-based manufacturing, reviews previous research in this area, and identifies control system requirements necessary to support successful application of this technology. Current production control systems inhibit the successful implementation of advanced manufacturing control technologies. It is often difficult, if not impossible, to integrate new sensing technologies and advanced control algorithms with existing control platforms. To address this difficulty, hardware and software needs to support intelligent sensor-based manufacturing are discussed.

The semantic differential (SD) method was used to characterize the size and shape of burrs created under various cutting conditions, drill size, tool geometry and coatings. Human subjects visually rated the burr using a SD evaluation form. Significant differences were found in tool type and feed rate. A high performance drill with titanium-nitride coating and a high depth/diameter ratio, yielded minimum burr. A lower feed rate resulted in less burr formation in the majority of the cases. Three primary factors emerged, and accounted for 83% of the variances. Factor scores were mapped into the SD space to show the effect of treatments.

All dimensional measurement systems, including Coordinate Measuring Machines (CMMs), share certain common characteristic elements. These systems include one or more of the following items: a sensor, a mechanism for supporting the sensor, a device for moving the support mechanism, a technique for planning how the support mechanism will be moved, and a device for analyzing sensor data. By recognizing these common features and capitalizing on the benefits of modularity and interchangeability of these features, the next generation of measuring machines will be able to easily accommodate new sensor technology and to meet the changing demands placed on dimensional inspection processes quickly and cost effectively.