Technical Brief

Probabilistic Factor Graph Based Approach for Automatic Material Assignments to Three-Dimensional Objects

[+] Author and Article Information
Binbin Zhang

Manufacturing and Design Lab (MADLab),
Department of Mechanical and Aerospace Engineering,
University at Buffalo,
Buffalo, NY 14260
e-mail: bzhang25@buffalo.edu

Rahul Rai

Manufacturing and Design Lab (MADLab),
Department of Mechanical and Aerospace Engineering,
University at Buffalo,
Buffalo, NY 14260
e-mail: rahulrai@buffalo.edu

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received September 9, 2015; final manuscript received September 19, 2016; published online October 14, 2016. Assoc. Editor: Kristina Shea.

J. Mech. Des 139(1), 014501 (Oct 14, 2016) (8 pages) Paper No: MD-15-1635; doi: 10.1115/1.4034838 History: Received September 09, 2015; Revised September 19, 2016

There are strong correlations between material assignment, shape, and functionality of a part in an overall product/assembly. However, these strong correlations are rarely exploited for automated material assignment. We present a probabilistic graphical model to assign materials to the parts (components) of a 3D object (assembly) by identifying the relations between shape, functionality, and material of the parts. By learning the context-dependent correlation with supervision from a set of objects and their segmented parts, the learned model can be used to assign engineering materials to the parts of a query object. Our primary contributions are (a) the engineering materials definition and assignment and (b) assigning engineering materials based on the behavior and form of the parts in the object. The performance of the proposed computational approach is demonstrated by the results of material assignment on various query objects with prespecified engineering performance requirements.

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Fig. 1

Automatic material assignment: office chair. (a) Input 3D model with desired behavior and (b) result of our automatic material assignment process based on shape and behavior.

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Fig. 2

Technical approach overview

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Fig. 3

Segmentation and database memex graph examples

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Fig. 4

Steps for defining the behavior of a database object

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Fig. 5

Database object: office chair. (a) Material allocation and the maximum allowable loads on the seat and seat back and (b) Von Mises stress (the yield strength of alloy steel is 6.20422 × 108 N/m2).

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Fig. 6

Steps for defining the behavior of the query object

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Fig. 7

Verification process for the query object

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Fig. 8

Query memex graph and factor graph example. (This is an extension of Jain et al. [1]. For the query memex graph, the behavioral similarity edge is added, which takes the behavior aspect of parts into consideration. Correspondingly, in the factor graph, a pairwise factor ψB is included.)

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Fig. 9

The marginal probability values for assigning different materials to chair seat, one-layer house roof, and two-layer house roof

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Fig. 10

Eight different query objects

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Fig. 11

Material assignment result for objects depicted in Fig. 10

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Fig. 12

Material assignment result on a bicycle model: (a) model without material and (b) result of automatic material assignment




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