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
plans.
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:
- Determine whether or not the design attributes (e.g., shape, dimensions,
tolerances, surface finishes) can be achieved.
- 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.
- 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 a 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 which 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 has 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 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:
- 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.
- The system operates on-line. Thus it helps in identifying potential
manufacturing problems early in the design stage.
- 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
Department of Mechanical Engineering and Institute for Systems Research
2135 Martin Hall
University of Maryland
College Park, Md-20742
Phone: 301-405-5306
FAX: 301-314-9477
WWW: http://www.glue.umd.edu/~skgupta/