國立虎尾科技大學 |

Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning./
作者:	Hoefer, Michael Jeffrey Daniel.
面頁冊數:	1 online resource (57 pages)
附註:	Source: Masters Abstracts International, Volume: 56-05.
Contained By:	Masters Abstracts International56-05(E).
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9781369878851

Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning.
Hoefer, Michael Jeffrey Daniel.

Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning. - 1 online resource (57 pages)

Source: Masters Abstracts International, Volume: 56-05.

Thesis (M.S.)

Includes bibliographical references

This thesis presents an automated method for assessing conceptual designs with respect to manufacturing and supply chain, using geometric data mining and machine learning algorithms. It is important for designers to understand how design decisions will impact downstream manufacturing and sourcing. Many critical decisions are made during conceptual design that impact production cost even before detailed design is finalized; however, the effects of these decisions are not known until later. Design for manufacturing and design for supply chain are methods that provide feedback to the user in a way that enables proactive design changes.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9781369878851Subjects--Topical Terms:

679492
Industrial engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning.
LDR:02734ntm a2200373Ki 4500 001 911186
005 20180529081858.5
006 m o u
007 cr mn||||a|a||
008 190606s2017 xx obm 000 0 eng d
020 $a 9781369878851
035 $a (MiAaPQ)AAI10259997
035 $a (MiAaPQ)iastate:16296
035 $a AAI10259997
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Hoefer, Michael Jeffrey Daniel. $3 1182864
245 1 0 $a Automated Design for Manufacturing and Supply Chain Using Geometric Data Mining and Machine Learning.
264 0 $c 2017
300 $a 1 online resource (57 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Masters Abstracts International, Volume: 56-05.
500 $a Adviser: Matthew C. Frank.
502 $a Thesis (M.S.) $c Iowa State University $d 2017.
504 $a Includes bibliographical references
520 $a This thesis presents an automated method for assessing conceptual designs with respect to manufacturing and supply chain, using geometric data mining and machine learning algorithms. It is important for designers to understand how design decisions will impact downstream manufacturing and sourcing. Many critical decisions are made during conceptual design that impact production cost even before detailed design is finalized; however, the effects of these decisions are not known until later. Design for manufacturing and design for supply chain are methods that provide feedback to the user in a way that enables proactive design changes.
520 $a A conceptual design is largely defined by the geometry found in CAD files. In this work, feature-free geometric algorithms were used to extract meaningful manufacturability metrics from 3D models, which were classified as either castings or machined parts. The developed metrics serve as useful attributes for a machine learning model that can help select the manufacturing process of a conceptual design. A classification accuracy of 86% was achieved using a random forest algorithm, which is comparable to other approaches in the literature, while only using geometry as input. The work in this thesis provides methods for using geometry to evaluate a design for manufacturability and supply chain, enabling proactive design decisions early during new product development.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Industrial engineering. $3 679492
650 4 $a Mechanical engineering. $3 557493
650 4 $a Design. $3 595500
655 7 $a Electronic books. $2 local $3 554714
690 $a 0546
690 $a 0548
690 $a 0389
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Iowa State University. $b Industrial and Manufacturing Systems Engineering. $3 1182174
773 0 $t Masters Abstracts International $g 56-05(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10259997 $z click for full text (PQDT)