MARC보기
LDR00000nam u2200205 4500
001000000432979
00520200225105934
008200131s2019 ||||||||||||||||| ||eng d
020 ▼a 9781088352168
035 ▼a (MiAaPQ)AAI22587311
040 ▼a MiAaPQ ▼c MiAaPQ ▼d 247004
0820 ▼a 658
1001 ▼a Bui, Anh Tuan.
24510 ▼a Statistical Process Control of Stochastic Textured Surfaces.
260 ▼a [S.l.]: ▼b Northwestern University., ▼c 2019.
260 1 ▼a Ann Arbor: ▼b ProQuest Dissertations & Theses, ▼c 2019.
300 ▼a 116 p.
500 ▼a Source: Dissertations Abstracts International, Volume: 81-05, Section: B.
500 ▼a Advisor: Apley, Daniel W.
5021 ▼a Thesis (Ph.D.)--Northwestern University, 2019.
506 ▼a This item must not be sold to any third party vendors.
520 ▼a This dissertation develops a new framework and algorithms for statistical process control of stochastic textured surface data that have no distinct features other than stochastic characteristics that vary randomly (e.g., image data of textiles or material microstructures and surface metrology data of metal parts). All methods are general and nonparametric in that they require no prior knowledge of the types of abnormalities that might occur nor the extraction of specific predefined features. The methods are applicable to a wide range of materials and address unsolved problems regarding monitoring and diagnosing quality-related issues that can lead to early damage, reduced lifetime, or compromised aesthetics of the manufactured materials. Specifically, the first problem is detecting defects on the surfaces (e.g., microstructure porosities)
590 ▼a School code: 0163.
650 4 ▼a Industrial engineering.
690 ▼a 0546
71020 ▼a Northwestern University. ▼b Industrial Engineering and Management Sciences.
7730 ▼t Dissertations Abstracts International ▼g 81-05B.
773 ▼t Dissertation Abstract International
790 ▼a 0163
791 ▼a Ph.D.
792 ▼a 2019
793 ▼a English
85640 ▼u http://www.riss.kr/pdu/ddodLink.do?id=T15492986 ▼n KERIS ▼z 이 자료의 원문은 한국교육학술정보원에서 제공합니다.
980 ▼a 202002 ▼f 2020
990 ▼a ***1008102
991 ▼a E-BOOK