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The Resource Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning

Label
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Title
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Creator
Subject
Language
eng
Summary
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
Member of
Cataloging source
MiAaPQ
Literary form
non fiction
Nature of contents
dictionaries
Series statement
Springer Theses Ser
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Label
Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning
Link
http://libproxy.rpi.edu/login?url=https://ebookcentral.proquest.com/lib/rpi/detail.action?docID=2120627
Publication
Copyright
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Carrier category
online resource
Carrier category code
cr
Carrier MARC source
rdacarrier
Color
multicolored
Content category
text
Content type code
txt
Content type MARC source
rdacontent
Contents
  • Supervisor's Foreword -- Acknowledgments -- Contents -- Abbreviations -- 1 Introduction -- 1.1 Motivation -- 1.2 Problem Statement -- 1.3 Research Goal and Research Methodology -- 1.4 Structure of the Dissertation -- References -- 2 Developments of Manufacturing Systems with a Focus on Product and Process Quality -- 2.1 Manufacturing Terms, Definitions and Developments -- 2.1.1 Manufacturing Processes -- 2.1.2 Process Monitoring -- 2.1.3 Product in Manufacturing -- 2.1.4 Quality in Manufacturing -- 2.1.4.1 Product Quality -- 2.1.4.2 Process Quality -- 2.1.5 Example of a Manufacturing Programme -- 2.2 Developments of Manufacturing System -- 2.2.1 System View on Manufacturing -- 2.2.2 Intelligent Manufacturing Systems -- 2.2.3 Holonic Manufacturing Systems -- 2.3 Developments in Information and Data Management in Manufacturing -- 2.3.1 Information Management (Systems) in Manufacturing -- 2.3.2 Data and Information Quality -- 2.3.2.1 Data and Information Quality Dimensions -- 2.3.2.2 Avoiding Data Errors -- 2.4 Challenges of MS from a Product and Process Information Perspective -- References -- 3 Current Approaches with a Focus on Holistic Information Management in Manufacturing -- 3.1 Product Lifecycle Management in Manufacturing -- 3.1.1 Product Data Management -- 3.1.2 Product Lifecycle Management -- 3.1.3 Closed-Loop and Item-Level PLM -- 3.2 Quality Monitoring in Manufacturing -- 3.2.1 Quality Management in the Manufacturing Domain -- 3.2.2 Quality Monitoring in Manufacturing Programmes -- 3.3 Limitations of Current Approaches for Holistic Information Management in Manufacturing Systems -- References -- 4 Development of the Product State Concept -- 4.1 Rationale for the Product State Concept -- 4.2 Product State -- 4.3 Relevant State Characteristics -- 4.3.1 Product State Characteristics -- 4.3.2 Product State Transformation
  • 4.3.3 Categorization of Product State Transformation -- 4.3.4 Relevant State Characteristics -- 4.3.5 Identification of Relevant State Characteristics -- 4.4 Process Intra- and Inter-relations Among State Characteristics -- 4.4.1 Describing Process Intra- and Inter-relations of State Characteristics -- 4.4.2 Visualization of Relations -- 4.4.3 Limitations of Describing Process Intra- and Inter-relations -- 4.5 Requirements of State Driver Identification -- 4.5.1 NP Complete Nature of Product State Concept -- 4.5.2 Suitability of Machine Learning Methods -- 4.6 Derived Research Hypothesis of the Application of ML Within the Product State Concept -- References -- 5 Application of Machine Learning to Identify State Drivers -- 5.1 Machine Learning in Manufacturing -- 5.1.1 Machine Learning -- 5.1.2 Supervised Machine Learning -- 5.2 Selection of Suitable Machine Learning Technique -- 5.2.1 Support Vector Machines (SVM) -- 5.2.1.1 Theoretical Background -- 5.2.1.2 Application Fields -- 5.2.2 Rationale of SVM Application for Identification of State Drivers in Manufacturing Systems -- 5.3 Application of SVM for Identification of State Drivers -- References -- 6 Application of SVM to Identify Relevant State Drivers -- 6.1 Introducing Scenarios I, II and III -- 6.2 Scenario I-Rolls-Royce -- 6.2.1 SVM Kernel and Parameters for Hyperplane by X-Validation -- 6.2.2 Feature Ranking Using SVM Classifier -- 6.2.3 Classification on Previously Unknown Data -- 6.3 Scenario II-Chemical Manufacturing Process -- 6.3.1 SVM Kernel and Parameters for Hyperplane by X-Validation -- 6.3.2 Classification on Previously Unknown Data -- 6.3.2.1 Definition of Learning Set-Random -- 6.3.2.2 Definition of Learning Set-Timely -- 6.3.2.3 Definition of Learning Set-Cluster Analysis -- 6.3.3 Compilation of SVM Operation and Output Data -- 6.3.4 Feature Ranking Using SVM Classifier
  • 6.4 Scenario III-SECOM -- 6.4.1 SVM Kernel and Parameters for Hyperplane by X-Validation -- 6.4.1.1 Under- and Oversampling -- Undersampling -- Oversampling -- 6.4.1.2 Feature Ranking Using SVM Classifier -- References -- 7 Evaluation of the Developed Approach -- 7.1 Evaluation Results -- 7.1.1 Data Pre-processing -- 7.1.2 Cross-validation Performance of SVM Classifier -- 7.1.3 Unbalanced Data -- 7.1.4 Feature Selection and Feature Ranking -- 7.1.5 Classification Performance on Previously Unknown Data -- 7.2 Discussion of Evaluation Results -- 7.3 Limitations -- References -- 8 Recapitulation -- 8.1 Conclusion -- 8.2 Outlook and Future Work -- References -- Annex -- Appreciation of Student Contribution -- About the Author
http://library.link/vocab/cover_art
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Dimensions
unknown
http://library.link/vocab/discovery_link
{'f': 'http://opac.lib.rpi.edu/record=b4383391'}
Extent
1 online resource (284 pages)
Form of item
online
Isbn
9783319176116
Media category
computer
Media MARC source
rdamedia
Media type code
c
Sound
unknown sound
Specific material designation
remote

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