Institute for Systems Research

Specifically, we used state-of-the-art concepts from morphology, namely the attern spectrum' of a shape, to map each shape to a point in n-dimensional space. Following [19, 36], we organized the n-d points in an R-tree. We showed that the L (= max) norm in the n-d space lower-bounds the actual distance. This guarantees no false dismissals for range queries. In addition, we developed a nearest neighbor algorithm that also guarantees no false dismissals.

Finally, we implemented the method, and we tested it against a testbed of realistic tumor shapes, using an established tumor-growth model of Murray Eden [15]. The experiments showed that our method is up to 27 times faster than straightforward sequential scanning.

Evaluating the manufacturability of a proposed design involves determining whether or not it is manufacturable with a given set of manufacturing operations - and if so, then finding the associated manufacturing efficiency. In this research, the design is represented as a solid model. The tolerance and surface finish information is represented as attributes of various faces of the solid model. Machining features are used to model the available machining operations Since there can be several different ways to manufacture a proposed design, this requires considering alternative ways to manufacture it, in order to determine which one best meets the design and manufacturing objectives.

The approach developed in this thesis is based on the systematic exploration of various machining plans. The first step is to identify all machining features which can potentially be used to machine the given design. Using these features, different machining plans are generated. Each time a new plan generated, it is examined to find whether it can produce the desired design tolerances. If a plan is found to be capable of meeting the tolerance specifications, then its rating is computed. If no machining plan can be found that is capable of producing the design, then the design cannot be machined using the given set of machining operations; otherwise, the manufacturability rating of the design is computed. Since various alternative ways of machining the part are considered in this approach, the conclusions about the manufacturability are more realistic compared to the approach where just one alternative is considered.

It is anticipated that this research will help in speeding up the evaluation of new product designs in order to decide how or whether to manufacture them. Such a capability will be useful in responding quickly to changing demands and opportunities in the marketplace.

As an example, in this paper we describe the techniques for manufacturing-operation planning used in IMACS (Interactive Manufacturability Analysis and Critiquing System). We compare and contrast them with the techniques used in classical AI planning systems, and point out that some of the techniques used in IMACS may also be useful in other kinds of planning problems.

Even after one has decided upon al abstract set of features to use for representing manufacturing operations, the set of feature instances used to represent a complex part is by no means unique. For a complex part, many (sometimes infinitely many) different manufacturing operations can potentially be used to manufacture various portions of the part - - and thus many different feature instances can be used to represent these portions of the part. Some of these feature instances will appear in useful manufacturing plans, and others will not. If the latter feature instances can be discarded at the outset, this will reduce the number of alternative manufacturing plans to be examined in order to find a useful one. Thus, what is required is a systematic means of specifying which feature instances are of interest.

This paper addresses the issue of alternatives by introducing the notion of primary feature instances, which we contend are sufficient to generate all manufacturing plans of interest. To substantiate our argument, we describe how various instances in the primary feature set can be used to produce the desired plans. Furthermore, we discuss how this formulation overcomes computational difficulties faced by previous work, and present some complexity results for this approach in the domain of machined parts.