Institute for Systems Research

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.

In this paper, we propose an architecture for integrating CAD with DFM. This involves the use of multiple critiquing systems, each one dedicated to one type of manufacturing domain. In the proposed framework, as the designer creates a design, a number of critiquing systems analyze its manufacturability with respect to different manufacturing domains (machining, fixturing, assembly, inspection, and, so forth), and offer advice about potential ways of improving the design.

We anticipate that this approach can be used to build an environment that will allow designers to create high-quality products that can be manufactured more economically. This will reduce the need for redesign, thus reducing product cost and lead time.

In particular, we present a methodology for taking a CAD model, extracting alternative interpretations of the model as collections of MRSEVs (Material Removal Shape Element Volumes, a STEP-based library of machining features), and evaluating these interpretations to determine which one is optimal. The evaluation criteria may be defined by the user, in order to select the best interpretation for the particular application at hand.

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. Since there can be several different ways to manufacture a proposed design, this requires us to consider different ways to manufacture it, in order to determine which one best meets the design and manufacturing objectives.

The first step in our approach is to identify all machining operations which can potentially be used to create the given design. Using these operations, we generate different operation plans for machining the part. Each time we generate a new operation plan, we examine whether it can produce the desired shape and tolerances, and calculate its manufacturability rating. If no operation plan can be found that is capable of producing the design, then the given design is considered unmachinable; otherwise, the manufacturability rating for the design is the rating of the best operation plan.

We anticipate that by providing feedback about possible problems with the design, this work 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.

In this paper, we develop a formalization of the problem of recognizing a class of machinable features expressed as MRSEVs ( a PDES/STEP library of machining features) [19], and an algorithm for solving this problem. Some of the characteristics of this approach are: the algorithm handles a large variety of hole and pocket features along with elementary accessibility constraints and blends for those features; it is provably complete, even if the features interest with each other in complex ways; it has O(n4) worst-case time complexity,

In this paper we present the following results: 1. We give general mathematical definitions of machining features and FBMs.

2. We present a systematic way to generate the alternative FBMs for a part, given an initial FBM for the part.

3. For each FBM, interactions among the features will impose precedence constraints on the possible orderings in which these features can be machined. We show how to generate these precedence constraints automatically for each interpretation.

4. We show how to organize the above precedence constraints into a time-order graph that represents all feasible orderings in which the features can be machined, and examine the time-order graph to see if it is consistent. If it is not consistent, then there is no way to machine this particular interpretation.

This work represents a step toward our overall approach of developing ways for automatically generating the alternative ways in which a part can be machined, and evaluating them to see how well they can do at creating the desired part. We anticipate that the information provided by this analysis will be useful both for process planning and concurrent design.