Identifying product and process state drivers in manufacturing systems using supervised machine learning:
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis Electronic eBook |
| Language: | English |
| Published: |
Cham [u.a.]
Springer
2015
|
| Series: | Springer theses, recognizing outstanding Ph. D. research
|
| Subjects: | |
| Online Access: | TUM01 Volltext Inhaltsverzeichnis Abstract |
| Physical Description: | XVIII, 272 p. 139 illus., 10 illus. in color |
| ISBN: | 9783319176116 9783319176109 |
| ISSN: | 2190-5053 |
| DOI: | 10.1007/978-3-319-17611-6 |
Staff View
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Record in the Search Index
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| adam_text | IDENTIFYING PRODUCT AND PROCESS STATE DRIVERS IN MANUFACTURING SYSTEMS
USING SUPERVISED MACHINE LEARNING
/ WUEST, THORSTEN
: 2015
TABLE OF CONTENTS / INHALTSVERZEICHNIS
INTRODUCTION
DEVELOPMENTS OF MANUFACTURING SYSTEMS WITH A FOCUS ON PRODUCT AND
PROCESS QUALITY
CURRENT APPROACHES WITH A FOCUS ON HOLISTIC INFORMATION MANAGEMENT IN
MANUFACTURING
DEVELOPMENT OF THE PRODUCT STATE CONCEPT
APPLICATION OF MACHINE LEARNING TO IDENTIFY STATE DRIVERS
APPLICATION OF SVM TO IDENTIFY RELEVANT STATE DRIVERS
EVALUATION OF THE DEVELOPED APPROACH
RECAPITULATION
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
IDENTIFYING PRODUCT AND PROCESS STATE DRIVERS IN MANUFACTURING SYSTEMS
USING SUPERVISED MACHINE LEARNING
/ WUEST, THORSTEN
: 2015
ABSTRACT / INHALTSTEXT
THE BOOK REPORTS ON A NOVEL APPROACH FOR HOLISTICALLY IDENTIFYING THE
RELEVANT STATE DRIVERS OF COMPLEX, MULTI-STAGE MANUFACTURING SYSTEMS.
THIS APPROACH IS ABLE TO UTILIZE COMPLEX, DIVERSE AND HIGH-DIMENSIONAL
DATA SETS, WHICH OFTEN OCCUR IN MANUFACTURING APPLICATIONS, AND TO
INTEGRATE THE IMPORTANT PROCESS INTRA- AND INTERRELATIONS. THE APPROACH
HAS BEEN EVALUATED USING THREE SCENARIOS FROM DIFFERENT MANUFACTURING
DOMAINS (AVIATION, CHEMICAL AND SEMICONDUCTOR). THE RESULTS, WHICH ARE
REPORTED IN DETAIL IN THIS BOOK, CONFIRMED THAT IT IS POSSIBLE TO
INCORPORATE IMPLICIT PROCESS INTRA- AND INTERRELATIONS ON BOTH A PROCESS
AND PROGRAMME LEVEL BY APPLYING SVM-BASED FEATURE RANKING. IN PRACTICE,
THIS METHOD CAN BE USED TO IDENTIFY THE MOST IMPORTANT PROCESS
PARAMETERS AND STATE CHARACTERISTICS, THE SO-CALLED STATE DRIVERS, OF A
MANUFACTURING SYSTEM. GIVEN THE INCREASING AVAILABILITY OF DATA AND
INFORMATION, THIS SELECTION SUPPORT CAN BE DIRECTLY UTILIZED IN, E.G.,
QUALITY MONITORING AND ADVANCED PROCESS CONTROL. IMPORTANTLY, THE METHOD
IS NEITHER LIMITED TO SPECIFIC PRODUCTS, MANUFACTURING PROCESSES OR
SYSTEMS, NOR BY SPECIFIC QUALITY CONCEPTS
DIESES SCHRIFTSTUECK WURDE MASCHINELL ERZEUGT.
|
| any_adam_object | 1 |
| author | Wuest, Thorsten |
| author_GND | (DE-588)1067597085 |
| author_facet | Wuest, Thorsten |
| author_role | aut |
| author_sort | Wuest, Thorsten |
| author_variant | t w tw |
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| dewey-full | 670 |
| dewey-hundreds | 600 - Technology (Applied sciences) |
| dewey-ones | 670 - Manufacturing |
| dewey-raw | 670 |
| dewey-search | 670 |
| dewey-sort | 3670 |
| dewey-tens | 670 - Manufacturing |
| discipline | Elektrotechnik Werkstoffwissenschaften / Fertigungstechnik Maschinenbau |
| doi_str_mv | 10.1007/978-3-319-17611-6 |
| format | Thesis Electronic eBook |
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| indexdate | 2025-02-20T06:51:33Z |
| institution | BVB |
| isbn | 9783319176116 9783319176109 |
| issn | 2190-5053 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-027978573 |
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| publisher | Springer |
| record_format | marc |
| series2 | Springer theses, recognizing outstanding Ph. D. research |
| spellingShingle | Wuest, Thorsten Identifying product and process state drivers in manufacturing systems using supervised machine learning Engineering Computer aided design Industrial engineering Industrial and Production Engineering Computer-Aided Engineering (CAD, CAE) and Design Production/Logistics/Supply Chain Management Computational Intelligence Ingenieurwissenschaften |
| subject_GND | (DE-588)4113937-9 |
| title | Identifying product and process state drivers in manufacturing systems using supervised machine learning |
| title_auth | Identifying product and process state drivers in manufacturing systems using supervised machine learning |
| title_exact_search | Identifying product and process state drivers in manufacturing systems using supervised machine learning |
| title_full | Identifying product and process state drivers in manufacturing systems using supervised machine learning Thorsten Wuest |
| title_fullStr | Identifying product and process state drivers in manufacturing systems using supervised machine learning Thorsten Wuest |
| title_full_unstemmed | Identifying product and process state drivers in manufacturing systems using supervised machine learning Thorsten Wuest |
| title_short | Identifying product and process state drivers in manufacturing systems using supervised machine learning |
| title_sort | identifying product and process state drivers in manufacturing systems using supervised machine learning |
| topic | Engineering Computer aided design Industrial engineering Industrial and Production Engineering Computer-Aided Engineering (CAD, CAE) and Design Production/Logistics/Supply Chain Management Computational Intelligence Ingenieurwissenschaften |
| topic_facet | Engineering Computer aided design Industrial engineering Industrial and Production Engineering Computer-Aided Engineering (CAD, CAE) and Design Production/Logistics/Supply Chain Management Computational Intelligence Ingenieurwissenschaften Hochschulschrift |
| url | https://doi.org/10.1007/978-3-319-17611-6 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027978573&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=027978573&sequence=000003&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
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