Optimizing tool selection

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

End mill tools integration in CNC machining for rapid manufacturing processes: simulation studies

“…The region that is accessible to different tool sizes can be calculated through the Tool Access Volume (TAV) method. Then, the analysis is extended to identify appropriate tool selection and sequencing by using a method known as Relative Delta Volume Clearance (RDVC) (Lim, Corney, Ritchie, & Clark, 2001). Based on information gained from these analyses, tool sizes and a set of cutting orientations can be identified by applying a genetic algorithm technique (Renner, 2008).…”

An offset algorithm for polyline curves

“…Lim et al [24] discuss the automatic tool selection for 2.5D machining and give an example for pocket machining (Fig. 10 in Ref.…”

Agent-based distributed manufacturing process planning and scheduling: a state-of-the-art survey

“…Research studies on process planning include object-oriented approaches [105], [132], GA-based approaches [70], [131], neural-network-based approaches [21], [69], Petri net-based approaches [53], feature recognition or feature-driven approaches [114], [119], and knowledge-based approaches [108], [118]. These approaches and their combinations have been applied to some specific problem domains, such as tool selection [24], [56], tool path planning [7], [45], machining parameters selection [3], [37], process sequencing [129], and setup planning [75], [125].…”