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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Boothroyd, G. , 1994, “Product Design for Manufacture and Assembly,” Comp. Aided Des., 26(7), pp. 505–520. [CrossRef]
Lovatt, A. , and Shercliff, H. , 1998, “Manufacturing Process Selection in Engineering Design—Part 1: The Role of Process Selection,” Mater. Des., 19(5–6), pp. 205–215. [CrossRef]
Pahl, G. , Beitz, W. , Feldhusen, J. , and Grote, K. , 2007, Engineering Design: A Systematic Approach, Springer-Verlag, London.
Corbett, J. , and Crookall, P. R. , 1986, “Design for Economic Manufacture,” CIRP Ann. Manuf. Technol., 35(1), pp. 93–97. [CrossRef]
Wetzel, S. , 2014, “When to Cast, When to Machine,” Met. Casting Des. Purchasing, Sept./Oct., pp. 29–32. http://www.afsinc.org/multimedia/contentMC.cfm?ItemNumber=17364
Ip, C. , and Regli, W. , 2006, “A 3D Object Classifier for Discriminating Manufacturing Processes,” Comput. Graph. (Pergamon), 30(6), pp. 903–916. [CrossRef]
Swift, K. , and Booker, J. , 2013, Manufacturing Process Selection Handbook, Elsevier, Waltham, MA.
Gupta, S. , Regli, W. , Das, D. , and Nau, D. , 1997, “Automated Manufacturability Analysis: A Survey,” Res. Eng. Des., 9(3), pp. 168–190. [CrossRef]
Esawi, A. , and Ashby, M. , 2000, “The Development and Use of a Software Tool for Selecting Manufacturing Processes at the Early Stages of Design,” J. Integr. Des. Process Sci., 4(2), pp. 27–43. https://content.iospress.com/articles/journal-of-integrated-design-and-process-science/jid4-2-03
Hummel, K. , 1989, “Coupling Rule-Based and Object-Oriented Programming for the Classification of Machined Features,” ASME Computers in Engineering Conference, Anaheim, CA, Aug. 2, pp. 409–418. https://www.osti.gov/scitech/biblio/5654389
Nau, D. , 1987, “Automated Process Planning Using Hierarchical Abstraction,” Texas Inst. Tech. J., Winter, 1, pp. 39–46. http://www.cs.umd.edu/~nau/papers/nau1987automated.pdf
Giachetti, R. , 1998, “A Decision Support System for Material and Manufacturing Process Selection,” J. Intell. Manuf., 9(3), pp. 265–276. [CrossRef]
Yurdakul, M. , Arslan, E. , Ic, Y. , and Tuerkbas, O. , 2014, “A Decision Support System for Selection of Net-Shape Primary Manufacturing Process,” Int. J. Prod. Res., 52(5), pp. 1528–1541. [CrossRef]
Zha, X. , 2005, “A Web-Based Advisory System for Process and Material Selection in Concurrent Product Design for a Manufacturing Environment,” Int. J. Adv. Manuf. Technol., 25(3–4), pp. 233–243. [CrossRef]
Giess, M. , McMahon, C. , Booker, J. , and Stewart, D. , 2009, “Application of Faceted Classification in the Support of Manufacturing Process Selection,” Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf., 223(6), pp. 597–608. [CrossRef]
Smith, C. , Wright, P. , and Séquin, C. , 2003, “The Manufacturing Advisory Service: Web-Based Process and Material Selection,” Int. J. Comput. Integr. Manuf., 16(6), pp. 373–381. [CrossRef]
Djassemi, M. , 2009, “A Computer-Based Economic Analysis for Manufacturing Process Selection,” Int. J. Agile Manuf., 11(1), pp. 11–18. http://ijme.us/cd_08/PDF/107%20IT%20302.pdf
Esawi, A. , and Ashby, M. , 2003, “Cost Estimates to Guide Pre-Selection of Processes,” Mater. Des., 24(8), pp. 605–616. [CrossRef]
Lee, C. , 1992, “A Knowledge-Based Systems Approach for Manufacturing Process Selection in Design,” Ph.D. dissertation, Ohio State University, Columbus, OH. https://dl.acm.org/citation.cfm?id=143071
Allen, A. , and Swift, K. , 1990, “Manufacturing Process Selection and Costing,” Proc. Inst. Mech. Eng., Part B: J. Eng. Manuf., 204(143), pp. 143–148. [CrossRef]
Loyer, J. , Henriques, E. , Fontul, M. , and Wiseall, S. , 2016, “Comparison of Machine Learning Methods Applied to the Estimation of Manufacturing Cost of Jet Engine Components,” Int. J. Prod. Econ., 178, pp. 109–119. [CrossRef]
Lovatt, A. , and Shercliff, H. , 1998, “Manufacturing Process Selection in Engineering Design—Part 2: A Methodology for Creating Task-Based Process Selection Procedures,” Mater. Des., 19(5–6), pp. 217–230. [CrossRef]
Lovatt, A. , Bassetti, D. , Shercliff, H. , and Bréchet, Y. , 2000, “Process and Alloy Selection for Aluminium Casting,” Int. J. Cast Met. Res., 12(4), pp. 211–225. [CrossRef]
Thompson, M. , Stolfi, A. , and Mischkot, M. , 2016, “Process Chain Modeling and Selection in an Additive Manufacturing Context,” CIRP J. Manuf. Sci. Technol., 12, pp. 25–34. [CrossRef]
Musti, S. , 1988, “"Automated Group Technology Part Coding From a Three-Dimensional CAD Database,” ASME J. Eng. Ind., 110(3), pp. 278–287. [CrossRef]
Zehtaban, L. , Elazhary, O. , and Roller, D. , 2016, “A Framework for Similarity Recognition of CAD Models,” J. Comput. Des. Eng., 3(3), pp. 274–285.
Biasotti, S. , Cerri, A. , Bronstein, A. , and Bronstein, M. , 2016, “Recent Trends, Applications, and Perspectives in 3D Shape Similarity Assessment,” Comput. Graph. Forum, 35(6), pp. 87–119. [CrossRef]
Iyer, N. , Jayanti, S. , Lou, K. , Kalyanaraman, Y. , and Ramani, K. , 2005, “Three-Dimensional Shape Searching: State-of-the-Art Review and Future Trends,” Comput. Aided Des., 37(5), pp. 509–530. [CrossRef]
Chen, X. , Gao, S. , Guo, S. , and Bai, J. , 2012, “A Flexible Assembly Retrieval Approach for Model Reuse,” Comput. Aided Des., 44(6), pp. 554–574. [CrossRef]
Deshmukh, A. , Banerjee, A. , Gupta, S. , and Sriram, R. , 2008, “Content-Based Assembly Search: A Step Towards Assembly Reuse,” Comput. Aided Des., 40(2), pp. 244–261. [CrossRef]
Deshmukh, A. , Gupta, S. , Karnik, M. , and Sriram, R. , 2005, “A System for Performing Content-Based Searches on a Database of Mechanical Assemblies,” Des. Eng., Parts A and B, 2005, pp. 411–423.
Liu, Z. , Bu, S. , Zhou, K. , Gao, S. , Han, J. , and Wu, J. , 2013, “A Survey on Partial Retrieval of 3D Shapes,” J. Comput. Sci. Technol., 28(5), pp. 836–851. [CrossRef]
Bai, J. , Luo, H. , and Qin, F. , 2016, “Design Pattern Modeling and Extraction for CAD Models,” Adv. Eng. Software, 93, pp. 30–43. [CrossRef]
Kim, D. , Yun, I. D. , and Uk Lee, S. , 2004, “Interactive 3-D Shape Retrieval System Using the Attributed Relational Graph,” IEEE Conference on Computer Vision and Pattern Recognition Workshop (CVPRW), Washington, DC, June 27–July 2, p. 147.
Gao, W. , Gao, S. , Liu, Y. , Bai, J. , and Hu, B. , 2006, “Multiresolutional Similarity Assessment and Retrieval of Solid Models Based on DBMS,” Comput. Aided Des., 38(9), pp. 985–1001. [CrossRef]
Pu, J. , Kalyanaraman, Y. , Jayanti, S. , Ramani, K. , and Pizlo, Z. , 2007, “Navigation and Discovery in 3D CAD Repositories,” IEEE Comput. Graph. Appl., 27(4), pp. 38–47. [CrossRef] [PubMed]
Qin, F. , 2014, “A Deep Learning Approach to the Classification of 3D CAD Models,” J. Zhejiang Univ. Sci. C: Comput. Electron., 15(2), pp. 91–106. [CrossRef]
Chakraborty, T. , 2005, “Shape-Based Clustering of Enterprise CAD Databases,” Comput. Aided Des. Appl., 2(1–4), pp. 145–154. [CrossRef]
Jayanti, S. , Kalyanaraman, Y. , and Ramani, K. , 2009, “Shape-Based Clustering for 3D CAD Objects: A Comparative Study of Effectiveness,” Comput. Aided Des., 41(12), pp. 999–1007. [CrossRef]
Bespalov, D. , Ip, C. , Regli, W. , and Shaffer, J. , 2005, “Benchmarking CAD Search Techniques,” ACM Symposium on Solid and Physical Modeling (SPM), Cambridge, MA, June 13–15, pp. 275–286.
Peabody, M. , and Regli, W. , 2001, “Clustering Techniques for Databases of CAD Models,” Drexel University, Philadelphia, PA, Technical Report No. DU-MCS-01-01. http://citeseerx.ist.psu.edu/viewdoc/download?doi=
Bespalov, D. , Shokoufandeh, A. , Regli, W. , and Sun, W. , 2003, “Scale-Space Representation of 3D Models and Topological Matching,” Eighth ACM Symposium on Solid Modeling and Applications, pp. 208–215.
Al-Mubaid, H. , Abouel Nasr, E. , and Kamrani, A. , 2008, “Using Data Mining in the Manufacturing Systems for CAD Model Analysis and Classification,” Int. J. Agile Syst. Manage., 3(1/2), pp. 147–162. [CrossRef]
Regli, W. C. , Foster, C. , Hayes, E ., Ip, C. Y. , McWherter, D. , Peabody, M. , Shapirsteyn, Y. , and Zaychik, V. , 2001, “National Design Repository Project: A Status Report,” International Joint Conferences on Artificial Intelligence (IJCAI), Seattle, WA, Aug. 4–10 https://pdfs.semanticscholar.org/11ef/a802666520bb6a276bdd977c7d2bd2170c9c.pdf.
Hoefer, M. , Chen, N. , and Frank, M. , 2017, “Automated Manufacturability Analysis for Conceptual Design in New Product Development,” Industrial and Systems Engineering Research Conference (ISERC), Pittsburgh, PA, May 20–23, pp. 860–865.
Frank, M. , Wysk, R. , and Joshi, S. , 2006, “Determining Setup Orientations From the Visibility of Slice Geometry for Rapid Computer Numerically Controlled Machining,” ASME J. Manuf. Sci. Eng., 128(1), pp. 228–238. [CrossRef]
Arabia, S. , 2008, “The Relationship Between Tool Length/Diameter Radio and Surface Roughness in End Milling Applications,” International Congress on Sound and Vibration (ICSV), Daejeon, Korea, July 6–10, pp. 1382–1389. http://studylib.net/doc/18185967/the-relationship-between-tool-length-diameter-ratio
Chay, J. , Jackman, J. , Frank, M. , and Peters, F. , 2017, “A New Metric for Evaluating Machinability of a Design,” Industrial and Systems Engineering Research Conference (ISERC), Pittsburgh, PA, May 20–23, pp. 1840–1845.
Li, Y. , and Frank, M. , 2006, “Machinability Analysis for 3-Axis Flat End Milling,” ASME J. Manuf. Sci. Eng., 128(2), pp. 454–464. [CrossRef]
Hoefer, M. , Frank, M. , and Dorneich, M. , 2017, “Geometric Analysis to Automate Design for Supply Chain,” Industrial and Systems Engineering Research Conference (ISERC), Pittsburgh, PA, May 20–23, pp. 866–871. https://www.researchgate.net/profile/Michael_Hoefer4/publication/318783066_Geometric_Analysis_to_Automate_Design_for_Supply_Chain/links/597e7ecf0f7e9b8802eaf13b/Geometric-Analysis-to-Automate-Design-for-Supply-Chain.pdf
Venables, W. , and Ripley, B. , 2002, Modern Applied Statistics With S, 4th ed., Springer, New York. [CrossRef]
Therneau, T. , Atkinson, B. , and Ripley, B. , 2015, “RPART: Recursive Partitioning and Regression Trees,” R package version 4.1-10, https://CRAN.R-project.org/package=rpart
Liaw, A. , and Wiener, M. , 2002, “Classification and Regression by randomForest,” R News, 2(3), pp. 18–22. http://www.bios.unc.edu/~dzeng/BIOS740/randomforest.pdf
Breiman, L. , 2001, “Random Forests,” Mach. Learn., 45(1), pp. 5–32. [CrossRef]


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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. 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. 9

Model which requires many setups to machine every facet

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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. 20

The MyCami2 machined part misclassified as a casting

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Fig. 19

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

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Fig. 18

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

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Fig. 17

Probability distribution of median reachability depth

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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. 15

Probability distribution for 75th percentile of visibility

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Fig. 14

Probability distribution for median angle deviation

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Fig. 13

Probability distribution for minimum tool accessibility orientation angle

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Fig. 12

Variable importance plot for random forest model




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