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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.