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This paper describes a system for the automatic recognition of assembly features and the generation of disassembly sequences. The paper starts by reviewing the nature and use of assembly features. One of the conclusions drawn from this survey is that the majority of assembly features involve sets of spatially adjacent faces. Two principle types of adjacency relationships are identified and an algorithm is presented for identifying assembly features which arise from “spatial” and “contact” face adjacency relationships (known as s-adjacency and c-adjacency respectively). The algorithm uses an octree representation of a B-rep model to support the geometric reasoning required to locate assembly features on disjoint bodies. A pointerless octree representation is generated by recursively sub-dividing the assembly model’s bounding box into octants which are used to locate: 1. Those portions of faces which are c-adjacent (i.e. they effectively touch within the tolerance of the octree). 2. Those portions of faces which are s-adjacent to a nominated face. The resulting system can locate and partition spatially adjacent faces in a wide range of situations and at different resolutions. The assembly features located are recorded as attributes in the B-rep model and are then used to generate a disassembly sequence plan for the assembly. This sequence plan is represented by a transition state tree which incorporates knowledge of the availability of feasible gripping features. By way of illustration, the algorithm is applied to several trial components

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

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