Interactive Manufacturability Analysis and Critiquing System

Main Participants: Satyandra K. Gupta, Dana S. Nau, and William C. Regli

Sponsors: This project was sponsored by the National Science Foundation. We also received in-kind support from Spatial Technologies and Ithaca Software.

Keywords: Manufacturability Analysis, Design for Manufacturing, Feature Recognition for Machining

Motivation

The ability to quickly introduce new quality products is a decisive factor in capturing market share. Because of pressing demands to reduce lead time, analyzing the manufacturability of the proposed design has become an important step in the design stage. In a typical CAD environment, the designer creates a design using solid-modeling software and uses analysis software to examine different aspects of the proposed design’s functionality.  The Interactive Manufacturability Analysis and Critiquing System (IMACS) project extends the design loop to incorporate a manufacturability analysis system that can be used once the geometry and/or tolerances have been specified. This will help in creating designs that not only satisfy the functional requirements but are also easy to manufacture.

We assume that the proposed design is available as a solid model, along with the tolerance and surface finish information as attributes of various faces of the solid model. We assume we have information about the available machining operations, including the process capabilities, dimensional constraints, etc. As shown below, our approach is to generate alternative interpretations of the part as collections of machining features, map these interpretations into operation plans, and evaluate the manufacturability of each operation plan.

We believe our work will help designers design products that are easier to manufacture. This will reduce the need for redesign, resulting in reduced lead time and product cost. In addition, it will help to speed up the evaluation of new product designs in order to decide how or whether to manufacture them. Such a capability will be especially useful in flexible manufacturing systems, which need to respond quickly to changing demands and opportunities in the marketplace.

Manufacturability Analysis

Given a computerized representation of the design (i.e. a solid model) and a set of manufacturing resources, the automated manufacturability analysis problem can be defined as follows:

  1. Determine whether or not the design attributes (e.g., shape, dimensions, tolerances, surface finishes) can be achieved.
  2. If the design is found to be manufacturable, determine a manufacturability rating, to reflect the ease (or difficulty) with which the design can be manufactured.
  3. If the design is not manufacturable, then identify the design attributes that pose manufacturability problems.

In general, a design’s manufacturability is a measure of the effort required to manufacture the part according to the design specifications. Our approach to measuring manufacturability is to estimate the manufacturing time and cost. Since all manufacturing operations have measurable time and cost, these can be used as an underlying basis to form a suitable manufacturability rating. Ratings based on time and cost can easily be combined into an overall rating. Moreover, they present a realistic view of the difficulty in manufacturing a proposed design and can be used to aid management in making make-or-buy decisions.

Modeling Machining Operations with Features

In a machining operation, a cutting tool is swept along a trajectory, and material is removed by the motion of the tool relative to the current workpiece. The volume resulting from a machining operation is called a machining feature. A machining feature corresponds to a single machining operation made on one machine setup. Each machining feature has a single approach direction (or orientation) for the tool. In IMACS, features are parameterized solids that correspond to various types of machining operations on a 3-axis machining center.

Approach

One of the fundamental objectives of IMACS was to develop a methodology for systematically generating and evaluating alternative operation plans for machined parts. This involves representing the design as a collection of machining features such as those shown above. Given this feature-based representation of the design, there may be, in general, several alternative representations of the design as different collections of machinable features, corresponding to different ways to machine the part. As described in the introduction, the basic idea is to generate alternative interpretations of the part as collections of machinable features, map these interpretations into operation plans, and evaluate the manufacturability of each operation plan. More specifically, our approach involves the steps shown below:

  • Build the set of all potential machining features by identifying various features which can be used to create the part from the stock. Each of these features represents a different possible machining operation that can be used to create various surfaces of the part.
  • Repeat the following steps until every promising feature-based model (FBM) has been examined:
    • Generate a promising FBM from the feature set. An FBM is a set of machining features that contains no redundant features and is sufficient to create the part. We consider an FBM unpromising if it is not expected to result in any operation plans better than the ones which have already been examined.
    • Do the following steps repeatedly, until every promising operation plan resulting from the particular FBM has been examined:
      • Generate a promising operation plan for the FBM. This operation plan represents a partially ordered set of machining operations. We consider an operation plan to be unpromising if it violates any common machining practices.
      • Estimate the achievable machining accuracy of the operation plan. If the operation plan cannot produce the required design tolerances and surface finishes, then discard it. Otherwise, estimate the production time and cost associated with the operation plan.
  • If no promising operation plans were found, then exit with failure. Otherwise exit with success, returning the operation plan that represents the best tradeoff among quality, cost, and time.

Anticipated Benefits

We anticipate that the results of our work will be useful in providing a way to speed up the evaluation of new product designs in order to decide how or whether to manufacture them. Such a capability will be especially useful in flexible manufacturing systems, which need to respond quickly to changing demands and opportunities in the marketplace. Some of the benefits of our approach include:

  1. Since we consider various alternative ways of machining the part, this allows us to consider how well each one balances the need for a quality product against the need for efficient manufacturing. This gives more accurate results than if we considered only one way to machine the part.
  2. The system operates on-line. Thus it helps in identifying potential manufacturing problems early in the design stage.
  3. Our approach is based on theoretical foundations which enable us to make rigorous statements about its soundness, completeness, efficiency, and robustness.

Related Publications

The following papers provide more details on the above described results.

  • S.K. Gupta, D.S. Nau, and W.C. Regli. IMACS: A case study in real-world planning. IEEE Intelligent Systems, 13(3):49–60, 1998.
  • S.K. Gupta. Using manufacturing planning to generate manufacturability feedback. Journal of Mechanical Design, 119:73–79, March 1997.
  • S.K. Gupta, D. Das, W.C. Regli, and D.S. Nau. Automated manufacturability analysis: A survey. Research in Engineering Design, 9(3):168–190, 1997.
  • W.C. Regli, S.K. Gupta, and D.S. Nau. Towards multiprocessor feature recognition. Computer Aided Design, 29(1):37–51, 1997.
  • D. Das, S.K. Gupta, and D.S. Nau. Generating redesign suggestions to reduce setup cost: A step towards automated redesign. Computer Aided Design, 28(10):763–782, 1996.
  • S.K. Gupta and D.S. Nau. Systematic approach to analyzing the manufacturability of machined parts. Computer Aided Design, 27(5):323–342, 1995.
  • W.C. Regli, S.K. Gupta, and D.S. Nau. Extracting alternative machining features: An algorithmic approach. Research in Engineering Design, 7(3):173–192, 1995.
  • S.K. Gupta, W.C. Regli, and D.S. Nau. Manufacturing feature instances: Which ones to recognize? In ACM Symposium on Solid Modeling and Applications, pages 141–152, Salt Lake City, Utah, May 1995.
  • S.K. Gupta, T.R. Kramer, D.S. Nau, W.C. Regli, and G. Zhang. Building MRSEV models for CAM applications. Advances in Engineering Software, 20(2-3):121–139, 1994.
  • S.K. Gupta, W.C. Regli, and D.S. Nau. Integrating DFM with CAD through design critiquing. Concurrent Engineering: Research and Applications, 2(2):85–95, 1994.
  • S.K. Gupta, D.S. Nau, and G.M. Zhang. Concurrent evaluation of machinability during product design. IEEE Computer, 26(1): 62–63, January 1993.

Contact

For additional information and to obtain copies of the above papers please contact:

Dr. Satyandra K. Gupta
Viterbi School of Engineering
University of Southern California
Los Angeles, California 90089-1453
Phone: 213-740-0491
Email: guptask [AT] usc [DOT] edu