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Research Papers: Design for Manufacture and the Life Cycle

Automatic Tolerance Analysis for Assessing Manufacturing Errors in Machining Plans

[+] Author and Article Information
Wentao Fu

Automated Design Laboratory,
Department of Mechanical Engineering,
The University of Texas at Austin,
Austin, TX 78712
e-mail: wentao.fu@mail.utexas.edu; wentao.fu@siemens.com

Saigopal Nelaturi

Automation for Engineered Systems,
Intelligent Systems Laboratory,
Palo Alto Research Center,
Palo Alto, CA 94304
e-mail: Saigopal.Nelaturi@parc.com

1Corresponding author.

2Present address: Siemens Energy, Inc., 11842 Corporate Blvd., Orlando, FL 32817.

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received August 4, 2016; final manuscript received January 7, 2017; published online February 9, 2017. Assoc. Editor: Rikard Söderberg.

J. Mech. Des 139(4), 041701 (Feb 09, 2017) (10 pages) Paper No: MD-16-1554; doi: 10.1115/1.4035826 History: Received August 04, 2016; Revised January 07, 2017

In machining process planning, it is critical to ensure that the part created following the manufacturing steps complies with the designated design tolerances. However, the challenge is that manufacturing errors are stochastic in nature and are introduced at almost every step of executing a plan, for example, due to inaccuracy of tooling, misalignment of location, etc. Furthermore, these errors accumulate or “stack up” as the machining process progresses to inevitably produce a part that varies from the original design. The resulting variations should be within prescribed design tolerances for the manufactured part to be acceptable. In this work, we present a novel approach for assessing the manufacturing errors by representing variations of nominal features with transformations that are defined in terms of extents of the features' degrees-of-freedom (DOFs) within their design and manufacturing tolerance zones (MTZs). We show how the manufacturing errors stackup can be effectively represented by the composition and intersection of these transformations. Several examples representing scenarios of different complexities are demonstrated to show the applicability of our approach in assessing the influence of manufacturing errors on the design tolerances following a machining plan. Discussions of our approach are provided to address concerns with the accuracy and efficiency as well as to disclose the potential of our approach to enable a tolerance-aware process planning system.

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References

Figures

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Fig. 1

DTZ of the cylindricity for a cylindrical feature

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Fig. 2

Tolerance analysis for a datum-dependent tolerance with only local manufacturing error involved

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Fig. 3

MTZ for a planar feature

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Fig. 4

An illustrative example. (a) the initial design with the tolerance specified, (b) manufacturing step 1, (c) manufacturing step 2: V1 is stacked up onto all other faces, and (d) inspection of the tolerance: only V0 contributes to the error stackup from design reference A to the feature.

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Fig. 5

Dependency graph of the machining plan in Fig. 4

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Fig. 6

Dependency graph for the second example

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Fig. 7

Cross-sectional view of the first example part—a cuboid with a through slot in middle and a hole on side. Parallelism and flatness tolerances are specified for face D, and perpendicularity and cylindricity tolerances are imposed on cylindrical face C.

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Fig. 8

The process plan for creating the part in Fig. 7. Face B is used as the initial manufacturing reference to create face A, face D, and other faces omitted. Then, face A is used as the manufacturing reference to create cylindrical face C and other faces omitted.

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Fig. 9

A view of the estimated locations of a single sampled point on face D after adding two types of errors. The stochastic distribution is shown. The middle line is the nominal position of the feature, and the two side lines are the DTZ bounds.

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Fig. 10

Convergence of sampling for the four tolerances

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Fig. 11

Second example part

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Fig. 12

Dependency graph of the process plan 3

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Fig. 13

A tree structure of the search space in process planning

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