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Research Papers

Optimization of Constraint Location, Orientation, and Quantity in Mechanical Assembly

[+] Author and Article Information
Leonard Rusli

e-mail: rusli.10@osu.edu

Anthony Luscher

e-mail: luscher.3@osu.edu
Department of Mechanical and
Aerospace Engineering,
The Ohio State University,
201 W 19th Avenue,
Columbus, OH 43210

James Schmiedeler

Department of Aerospace and
Mechanical Engineering,
University of Notre Dame,
257 Fitzpatrick Hall,
Notre Dame, IN 46556
e-mail: schmiedeler.4@nd.edu

Contributed by the Design for Manufacturing Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received October 21, 2012; final manuscript received April 11, 2013; published online xx xx, xxxx. Assoc. Editor: Rikard Söderberg.

J. Mech. Des 135(7), 071007 (May 24, 2013) (11 pages) Paper No: MD-12-1529; doi: 10.1115/1.4024314 History: Received October 21, 2012; Revised April 11, 2013

A mechanical assembly aims to remove 6 degree-of-freedom (DOF) motion between two or more parts using features such as fasteners, integral attachments, and mating surfaces, all of which act as constraints. The locations, orientations, and quantity of these constraints directly influence the effectiveness of a constraint configuration to eliminate DOF; therefore, constraint design decisions are crucial to the performance of a mechanical assembly. The design tool presented in this paper uses an analysis tool developed by the authors to explore a user-specified constraint design space and help the designer make informed decisions based on quantitative data so as to optimize constraint locations and orientations. The utility of the design tool is demonstrated with an assembly case study that contains both threaded fasteners and integral attachments. The results identify the opportunity for significant improvements by separately exploring individual design spaces associated with some constraints and further gains through a search of a multidimensional design space that leverages interaction effects between the location and orientation variables. The example also highlights how the tool can help identify nonintuitive solutions such as nonrectilinear, nonplanar parting lines. A trade-off study demonstrates how the design tool can quantitatively aid in optimizing the total number of constraints. Adding constraints generally improves an assembly's performance at the expense of increased redundancy, which can cause locked-in stresses and assembly inaccuracies, so the design tools helps identify new/removable constraints that offer the greatest/least contribution to the overall part constraint configuration. Through these capabilities, this design tool provides useful data to optimize and understand mechanical assembly performance variables.

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Figures

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Fig. 1

Simplified flowchart for constraint modification algorithm

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Fig. 2

Printer housing geometry and assembly constraints

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Fig. 3

Response surface plot for fastener location optimization

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Fig. 4

Response surface plot for fastener orientation optimization

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Fig. 5

Response surface plot for snap-fit location optimization

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Fig. 6

Response surface plot for snap-fit orientation optimization

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Fig. 7

Response surface plot for parting line optimization

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Fig. 8

Cube constraint configuration

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Fig. 9

Rating change due to constraint removal (one at a time)

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Fig. 10

Rating change due to constraint removal (two at a time)

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Fig. 11

Overall rating change as constraints are removed

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Fig. 12

Endcap geometry and constraint feature additions

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Fig. 13

Rating change as the number of snap-fits is increased

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