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The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs.

Strathprints serves world leading Open Access research by the University of Strathclyde, including research by the Strathclyde Institute of Pharmacy and Biomedical Sciences (SIPBS), where research centres such as the Industrial Biotechnology Innovation Centre (IBioIC), the Cancer Research UK Formulation Unit, SeaBioTech and the Centre for Biophotonics are based.

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Optimizing tool selection

Lim, T.C. and Corney, J.R. and Ritchie, J.M. and Clark, D. (2001) Optimizing tool selection. International Journal of Production Research, 39 (6). pp. 1239-1256. ISSN 0020-7543

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Abstract

Selecting optimal cutting tools that can answer to the performance criteria of manufacturing economics (quality, productivity, cost, etc) is an important step in planning the manufacture of components. Achieving this, however, is difficult because of the many constraints involved in the tool selection process. This paper describes a method for determining a theoretical optimal combination of cutting tools given a set of 3D volumes or 2D profiles. Optimal tools are selected by considering residual material that is inaccessible to oversized cutters and the relative clearance rates of cutters that can access these regions of the selected machining features. The current implementation described does not give exact results because several machining parameters have been ignored during the selection process, such as tool path length, plunge rates, etc. However, the experimental studies carried out to verify the theoretical results suggest that while these factors may influence the absolute values calculated, in general, their influence on the relative ranking of the tools is insignificant. The results presented here suggest that the 'correct' combination of tools could significantly reduce machining times. Consequently, the paper concludes with a discussion of how modifications to typical tool path generation routines in commercial CAM systems could improve productivity.