Research Papers

An Approach to Automate and Optimize Concept Generation of Sheet Metal Parts by Topological and Parametric Decoupling

[+] Author and Article Information
Jay Patel

Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712-0292pateljk@mail.utexas.edu

Matthew I. Campbell

Department of Mechanical Engineering, University of Texas at Austin, Austin, TX 78712-0292mc1@mail.utexas.edu

J. Mech. Des 132(5), 051001 (Apr 29, 2010) (11 pages) doi:10.1115/1.4001409 History: Received April 02, 2009; Revised March 06, 2010; Published April 29, 2010; Online April 29, 2010

This paper describes an approach to automate the design for sheet metal parts that satisfy multiple objective functions such as material cost and manufacturability. Unlike commercial software tools such as PRO/SHEETMETAL , which aids the user in finalizing and determining the sequence of manufacturing operations for a specified component, our approach starts with spatial constraints in order to create the component geometries and helps the designer design. While there is an infinite set of parts that can feasibly be generated with sheet metal, it is difficult to define this space systematically. To solve this problem, we have created 108 design rules that have been developed for five basic sheet metal operations: slitting, notching, shearing, bending, and punching. A recipe of the operations for a final optimal design is then presented to the manufacturing engineers thus saving them time and cost. The technique revealed in this paper represents candidate solutions as a graph of nodes and arcs where each node is a rectangular patch of sheet metal, and modifications are progressively made to the sheet to maintain the parts manufacturability. This paper also discusses a new topological optimization technique to solve graph-based engineering design problems by decoupling parameters and topology changes. This paper presents topological and parametric tune and prune ((TP)2) as a topology optimization method that has been developed specifically for domains representable by a graph grammar schema. The method is stochastic and incorporates distinct phases for modifying the topologies and modifying parameters stored within topologies. Thus far, with abovementioned sheet metal problem, (TP)2 had proven better than genetic algorithm in terms of the quality of solutions and time taken to acquire them.

Copyright © 2010 by American Society of Mechanical Engineers
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Figure 1

Tree formation leading to different designs depending on the rule and parameter choice

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Figure 2

Generic flowchart for computational synthesis has four divisions

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Figure 3

Initial seed representation as a sheet metal blank to adhere with production

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Figure 12

Bars represents the average fitness and lines indicates the best fitness value for each fitness

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Figure 13

Best possible topologies resulting from using genetic algorithm

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Figure 14

Average and the best fitness per iteration for problem 1 (P1) using genetic algorithm

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Figure 4

The design specifications for a sheet metal component is embodied as spatial constraints

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Figure 5

Graph showing different kinds of nodes and arcs along with a sheet metal part resulting from it

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Figure 6

Corner notching rule showing left-hand side and right-hand side of grammar rules, which requires two variables, namely, height and depth of the notch

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Figure 7

Topological engineering design problems can be divided six categories

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Figure 8

(TP)2 has four phases including create neighbors, create percentage new, parametric tuning, and pruning

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Figure 9

Flowchart detailing the (TP)2 search process

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Figure 10

Depth of tree as a function of iteration

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Figure 11

Test problem showing the spatial constraints and the resulting topologies using the (TP)2 method



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