Institute for Systems Research Technical Reports

Permanent URI for this collectionhttp://hdl.handle.net/1903/4376

This archive contains a collection of reports generated by the faculty and students of the Institute for Systems Research (ISR), a permanent, interdisciplinary research unit in the A. James Clark School of Engineering at the University of Maryland. ISR-based projects are conducted through partnerships with industry and government, bringing together faculty and students from multiple academic departments and colleges across the university.

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    AI Planning Versus Manufacturing-Operation Planning: A Case Study
    (1995) Nau, D.S.; Gupta, Sandeep K.; Regli, W.C.; ISR
    Although AI planning techniques can potentially be useful in several manufacturing domains, this potential remains largely unrealized. In order to adapt AI planning techniques to manufacturing, it is important to develop more realistic and robust ways to address issues important to manufacturing engineers. Furthermore, by investigating such issues, AI researchers may be able to discover principles that are relevant for AI planning in general. As an example, in this paper we describe the techniques for manufacturing-operation planning used in IMACS (Interactive Manufacturability Analysis and Critiquing System), and compare and contrast them with the techniques used in classical AI planning systems. We describe how one of IMACS's planning techniques may be useful for AI planning in general -- and as an example, we describe how it helps to explain a puzzling complexity result in AI planning.
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    Current Trends and Future Challenges in Automated Manufacturability Analysis
    (1995) Gupta, Satyandra K.; Das, Diganta; Regli, W.C.; Nau, Dana S.; ISR
    In the marketplace of the 21st century, there is no place for traditional communications between design and manufacturing. In order to ``design it right the first time,'' designers must ensure that their products are both functional and easy to manufacture. Software tools have had some successes in reducing the barriers between design and manufacturing. Manufacturability analysis systems are emerging as one such tool---enabling identification of potential manufacturing problems during the design phase and providing suggestions to designers on how to eliminate them.

    In this paper, we survey of current state of the art in automated manufacturability analysis. We describe the two dominant approaches to automated manufacturability analysis and overview representative systems based on their application domain. Finally, we attempt to expose some of the existing research challenges and future directions.

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    Automated Manufacturability Analysis: A Survey
    (1995) Das, Diganta; Gupta, Satyandra K.; Regli, W.C.; Nau, Dana S.; ISR
    In the marketplace of the 21st century, there is no place for traditional ``over-the-wall'' communications between design and manufacturing. In order to ``design it right the very first time,'' designers must ensure that their products are both functional and easy to manufacture. Software tools have had some successes in reducing the barriers between design and manufacturing. Manufacturability analysis systems are emerging as one such tool---enabling identification of potential manufacturing problems during the design phase and providing suggestions to designers on how to eliminate them.

    In this paper, we provide a survey of current state of the art in automated manufacturability analysis. We present the historical context in which this area has emerged and outline characteristics to compare and classify various systems. We describe the two dominant approaches to automated manufacturability analysis and overview representative systems based on their application domain. We describe support tools that enhance the effectiveness of manufacturability analysis systems. Finally, we attempt to expose some of the existing research challenges and future directions.

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    "Manufacturing-Operation Planning Versus AI Planning
    (1995) Nau, D.S.; Gupta, Sandeep K.; Regli, W.C.; ISR
    Although AI planning techniques can potentially be useful in several manufacturing domains, this potential remains largely unrealized. Many of the issues important to manufacturing engineers have not seemed interesting to AI researchers---but in order to adapt AI planning techniques to manufacturing, it is important to address these issues in a realistic and robust manner. Furthermore, by investigating these issues, AI researchers may be able to discover principles that, are relevant for AI planning in general.

    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.

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    An Application of Distributed Solid Modeling: Feature Recognition
    (1994) Regli, W.C.; Gupta, Satyandra K.; Nau, D.S.; ISR
    The availability of low-cost computational power is a driving force behind the growing sophistication of CAD software. Tools designed to reduce time-consuming build-test-redesign iterations are essential for increasing engineering quality and productivity. However, automation of the design process poses many difficult computational problems. As more downstream engineering activities are being considered during the design phase, guaranteeing reasonable response times within design systems becomes problematic. Design is an interactive process and speed is a critical factor in systems that enable designers to explore and experiment with alternative ideas during the design phase. Achieving interactivity requires an increasingly sophisticated allocation of computational resources in order to perform realistic design analyses and generate feedback in real time.

    This paper presents our initial efforts to develop techniques to apply distributed algorithms to the problem of recognizing machining features from solid models. Existing work on recognition of features has focused exclusively on serial computer architectures. Our objective is to show that distributed algorithms can be employed on realistic parts with large numbers of features and many geometric and topological entities to obtain significant improvements in computation time using existing hardware and software tools. Migrating solid modeling applications toward a distributed computing frame-work enables interconnection of many of the autonomous and geographically diverse software tools used in the modern manufacturing enterprise.

    This has been implemented on a network of SUN workstations using the ACIS solid modeler and the NIH C++ class library; inter-processor communication is handled with TCP/IP- based network communication tools.

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    Manufacturing Feature Instances: Which Ones to Recognize?
    (1994) Gupta, Satyandra K.; Regli, W.C.; Nau, D.S.; ISR
    Manufacturing features and feature-based representations have become an integral part of research on manufacturing systems, largely due to their ability to model correspondences between design information and manufacturing operations. However, several research challenges still must be addressed in order to place feature technologies into a solid scientific and mathematical framework: One challenge is the issue of alternatives in feature- based planning.

    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.

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    Extracting Alternative Machining Features: Al Algorithmic Approach
    (1994) Regli, W.C.; Gupta, Satyandra K.; Nau, D.S.; ISR
    Automated recognition of features from CAD models has been attempted for a wide range of application domains. In this paper we address the problem of representing and recognizing the complete class of features in alternative interpretations for a given design. We present a formalism for representing feature- based design alternatives and a methodology for recognizing a class of machinable features. Our approach handles a class of volumetric features that describe material removal volumes made by operations on the three-axis vertical machining centers including: drilling, pocket-, slot-, and face-miling, chamfering, filleting, and blended surfaces. Our approach recognizes intersecting features, and is complete over all features in our class, i.e. for any given part, the algorithm produces a set containing all features in our class that correspond to possible operations for machining that part. This property is of particular significance in applications where consideration of different manufacturing alternatives is crucial. In addition, we have shown that the algorithms are, in the worst-case, euqdratic in the number solid modeling operations. This approach employs a class of machinable features expressible as MRSEVs ( a STEP- based library of machining features). An implementation of these algorithms has been done using the ACISsolid modeler and the NIH C++ class library.
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    Integrating DFM with CAD through Design Critiquing
    (1994) Gupta, Satyandra K.; Regli, W.C.; Nau, D.S.; ISR
    In research on concurrent engineering and engineering design, the increasing use of design for manufacturability(DFM) is expanding the scope of traditional design activities in order to identify and eliminate manufacturing problems during the design stage. However, manufacturing a product generally involves many different kinds of manufacturing activities, each having different characteristics. A design that is good for one kind of activity may not be good for another; for example, a design that is easy to assemble may not be easy to machine. One obstacle to DFM is the difficulty involved in building a single system that can handle the various manufacturing domains relevant to a design.

    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.

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    Feature Recognition for Manufacturability Analysis
    (1994) Regli, W.C.; Gupta, Satyandra K.; Nau, D.S.; ISR
    While automated recognition of features has been attempted for a wide range of applications, no single existing approach possesses the functionality required to perform manufacturability analysis. In this paper, we present a methodology for taking a CAD model and extracting a set of machinable features suitable for generating all alternative interpretations of the model as collections of MRSEVs (Material Removal Shape Element Volumes, a STEP-based library of machining, features). This set of MRSEVs is to be employed for manufacturability analysis. The algorithm handles a variety of features including those describing holes, pockets, slots, and chamfering and filleting operations. In addition, it considers elementary accessibility constraints for these features and is provably complete over a, significant class of machinable parts the features describe. Further, the approach has low-order polynomial-time worst-case complexity.
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    Building MRSEV Models for CAM Applications
    (1993) Gupta, Satyandra K.; Kramer, Thomas R.; Nau, D.S.; Regli, W.C.; Zhang, G.M.; ISR
    Integrating CAD and CAM applications, one major problems is how to interpret CAD information in a manner that makes sense for CAM. Our goal is to develop a general approach that can be used with a variety of CAD and CAM applications for the manufacture of machined parts.

    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.