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Research Papers: Design for Manufacture and the Life Cycle

Automated Manufacturing Process Selection During Conceptual Design

[+] Author and Article Information
Michael J. Hoefer

Industrial and Manufacturing
Systems Engineering,
Iowa State University,
Ames, IA 50011
e-mail: mjhoefer@gmail.com

Matthew C. Frank

Industrial and Manufacturing
Systems Engineering,
Iowa State University,
Ames, IA 50011
e-mail: mfrank@iastate.edu

Contributed by the Design for Manufacturing Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received April 19, 2017; final manuscript received November 29, 2017; published online January 10, 2018. Assoc. Editor: Rikard Söderberg.

J. Mech. Des 140(3), 031701 (Jan 10, 2018) (12 pages) Paper No: MD-17-1283; doi: 10.1115/1.4038686 History: Received April 19, 2017; Revised November 29, 2017

This paper presents a method for automated manufacturing process selection during conceptual design. It is helpful to know which manufacturing processes can produce a design at an early stage, when the overall design can be changed for less cost. Early during new product development, geometric dimensions and tolerances may not yet be specified, but a general three-dimensional (3D) model is often under development. In this work, algorithms are presented to interrogate 3D models to calculate machining-based manufacturability metrics. These algorithms are used on a dataset of 86 computer-aided design (CAD) models classified as machined or cast-then-machined. The metrics, such as visibility, reachability, and setup orientations, seek to characterize a part's manufacturability using machining domain knowledge. These metrics serve as inputs to machine learning models, which are used to classify parts by manufacturing process with 86% accuracy. Some of the incorrectly classified parts were instances that had robust designs capable of being manufactured using machining or casting. The results of the machine learning models indicate that the machining metrics can be used to provide process selection feedback during conceptual design.

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Figures

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Fig. 1

Example parts: (a) a part with many flat surfaces suitable for machining and (b) a part with curved surfaces suitable for casting

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Fig. 2

Composition of the model for predicting manufacturing process

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Fig. 9

Model which requires many setups to machine every facet

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Fig. 3

Slice-based machining analysis: (a) the original STL model, (b) 2D slices generated from the model, (c) machining-based manufacturability analysis resulting in numeric results for each slice segment, and (d) segment analysis values mapped back to the original surfaces

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Fig. 4

Slice-based method for visibility analysis: (a) the original STL file, (b) the slices generated from one principle axis, (c) the visibility calculations for a segment, and (d) the visibility scores mapped back to the original surface

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Fig. 5

A feature that requires a long tool for machining

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Fig. 6

Slice-based method for reachability analysis: (a) the original STL file, (b) slices generated from one principle axis, (c) reachability calculation for a single slice and angle, and (d) reachability scores mapped back to the original facet

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Fig. 7

Slice-based tool accessibility analysis: (a) the original STL model, (b) 2D slices from the model, (c) tool accessibility analysis on a single slice, and (d) segment values mapped back to the original surfaces

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Fig. 8

Tool space and obstacle space for a single segment consisting of points Pij and Pij+1. Source from Ref. [49].

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Fig. 10

Visibility set cover problem; (a) the array of n facets containing the visible angles for each axis of rotation and (b) the completed set cover of selected axes and angles

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Fig. 11

A tessellated model indicating the unit normal vectors

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Fig. 12

Variable importance plot for random forest model

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Fig. 13

Probability distribution for minimum tool accessibility orientation angle

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Fig. 14

Probability distribution for median angle deviation

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Fig. 18

Casting parts misclassified as machined: (a) glass-1 and (b) glass-2

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Fig. 19

The part assembly five, a machined part misclassified as a casting

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Fig. 20

The MyCami2 machined part misclassified as a casting

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Fig. 15

Probability distribution for 75th percentile of visibility

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Fig. 16

Visibility map shaded based on the visibility scores of the surfaces; (a) a machined part (“part 10”) and (b) a cast part (“cross”)

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Fig. 17

Probability distribution of median reachability depth

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