Journal article 1355 views
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects
R.S Ransing,
C Giannetti,
M.R Ransing,
M.W James,
Rajesh Ransing 
,
Cinzia Giannetti
,
Cinzia Giannetti
Computers in Industry, Volume: 64, Issue: 5, Pages: 514 - 523
Swansea University Authors:
Rajesh Ransing , Cinzia Giannetti
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DOI (Published version): 10.1016/j.compind.2013.02.009
Abstract
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects
| Published in: | Computers in Industry |
|---|---|
| ISSN: | 0166-3615 |
| Published: |
2013
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa14574 |
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2013-07-23T12:13:29Z |
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2018-02-09T04:46:02Z |
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<?xml version="1.0"?><rfc1807><datestamp>2015-05-18T13:54:36.7346069</datestamp><bib-version>v2</bib-version><id>14574</id><entry>2013-09-03</entry><title>A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects</title><swanseaauthors><author><sid>0136f9a20abec3819b54088d9647c39f</sid><ORCID>0000-0003-4848-4545</ORCID><firstname>Rajesh</firstname><surname>Ransing</surname><name>Rajesh Ransing</name><active>true</active><ethesisStudent>false</ethesisStudent></author><author><sid>a8d947a38cb58a8d2dfe6f50cb7eb1c6</sid><ORCID>0000-0003-0339-5872</ORCID><firstname>Cinzia</firstname><surname>Giannetti</surname><name>Cinzia Giannetti</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2013-09-03</date><deptcode>MECH</deptcode><abstract></abstract><type>Journal Article</type><journal>Computers in Industry</journal><volume>64</volume><journalNumber>5</journalNumber><paginationStart>514</paginationStart><paginationEnd>523</paginationEnd><publisher/><issnPrint>0166-3615</issnPrint><issnElectronic/><keywords/><publishedDay>31</publishedDay><publishedMonth>12</publishedMonth><publishedYear>2013</publishedYear><publishedDate>2013-12-31</publishedDate><doi>10.1016/j.compind.2013.02.009</doi><url/><notes>Traditional techniques are unable to discover correlations among factors in the ‘noisy’ in-process data. The proposed technique of discovering correlations in the reduced space defined by the principal components is shown to be a novel and robust method. It allows process engineers to view limited number of important penalty matrices from the thousands of possible combinations. The approach has been embedded training courses offered by the American Foundrymen and Institute of Cast Metal Engineers in UK. Elsevier publishers have chosen to make this paper open source, for a period of three months, for the benefit foundry engineers around the world.</notes><college>COLLEGE NANME</college><department>Mechanical Engineering</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MECH</DepartmentCode><institution>Swansea University</institution><apcterm/><lastEdited>2015-05-18T13:54:36.7346069</lastEdited><Created>2013-09-03T06:10:20.0000000</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering</level></path><authors><author><firstname>R.S</firstname><surname>Ransing</surname><order>1</order></author><author><firstname>C</firstname><surname>Giannetti</surname><order>2</order></author><author><firstname>M.R</firstname><surname>Ransing</surname><order>3</order></author><author><firstname>M.W</firstname><surname>James</surname><order>4</order></author><author><firstname>Rajesh</firstname><surname>Ransing</surname><orcid>0000-0003-4848-4545</orcid><order>5</order></author><author><firstname>Cinzia</firstname><surname>Giannetti</surname><orcid>0000-0003-0339-5872</orcid><order>6</order></author></authors><documents/><OutputDurs/></rfc1807> |
| spelling |
2015-05-18T13:54:36.7346069 v2 14574 2013-09-03 A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects 0136f9a20abec3819b54088d9647c39f 0000-0003-4848-4545 Rajesh Ransing Rajesh Ransing true false a8d947a38cb58a8d2dfe6f50cb7eb1c6 0000-0003-0339-5872 Cinzia Giannetti Cinzia Giannetti true false 2013-09-03 MECH Journal Article Computers in Industry 64 5 514 523 0166-3615 31 12 2013 2013-12-31 10.1016/j.compind.2013.02.009 Traditional techniques are unable to discover correlations among factors in the ‘noisy’ in-process data. The proposed technique of discovering correlations in the reduced space defined by the principal components is shown to be a novel and robust method. It allows process engineers to view limited number of important penalty matrices from the thousands of possible combinations. The approach has been embedded training courses offered by the American Foundrymen and Institute of Cast Metal Engineers in UK. Elsevier publishers have chosen to make this paper open source, for a period of three months, for the benefit foundry engineers around the world. COLLEGE NANME Mechanical Engineering COLLEGE CODE MECH Swansea University 2015-05-18T13:54:36.7346069 2013-09-03T06:10:20.0000000 Faculty of Science and Engineering School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering R.S Ransing 1 C Giannetti 2 M.R Ransing 3 M.W James 4 Rajesh Ransing 0000-0003-4848-4545 5 Cinzia Giannetti 0000-0003-0339-5872 6 |
| title |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects |
| spellingShingle |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects Rajesh Ransing Cinzia Giannetti |
| title_short |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects |
| title_full |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects |
| title_fullStr |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects |
| title_full_unstemmed |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects |
| title_sort |
A coupled penalty matrix approach and principal component based co-linearity index technique to discover product specific foundry process knowledge from in-process data in order to reduce defects |
| author_id_str_mv |
0136f9a20abec3819b54088d9647c39f a8d947a38cb58a8d2dfe6f50cb7eb1c6 |
| author_id_fullname_str_mv |
0136f9a20abec3819b54088d9647c39f_***_Rajesh Ransing a8d947a38cb58a8d2dfe6f50cb7eb1c6_***_Cinzia Giannetti |
| author |
Rajesh Ransing Cinzia Giannetti |
| author2 |
R.S Ransing C Giannetti M.R Ransing M.W James Rajesh Ransing Cinzia Giannetti |
| format |
Journal article |
| container_title |
Computers in Industry |
| container_volume |
64 |
| container_issue |
5 |
| container_start_page |
514 |
| publishDate |
2013 |
| institution |
Swansea University |
| issn |
0166-3615 |
| doi_str_mv |
10.1016/j.compind.2013.02.009 |
| college_str |
Faculty of Science and Engineering |
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|
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facultyofscienceandengineering |
| hierarchy_top_title |
Faculty of Science and Engineering |
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facultyofscienceandengineering |
| hierarchy_parent_title |
Faculty of Science and Engineering |
| department_str |
School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Aerospace, Civil, Electrical, General and Mechanical Engineering - Mechanical Engineering |
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0 |
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0 |
| published_date |
2013-12-31T03:16:41Z |
| _version_ |
1763750341321424896 |
| score |
11.016258 |