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Data-Driven Optimization of Manufacturing Processes

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Title: Data-Driven Optimization of Manufacturing Processes
Description: All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
Authors: Kanak Kalita, Ranjan Kumar Ghadai, Xiao-Zhi Gao
Resource Type: eBook.
Subjects: Manufacturing processes--Data processing, Engineering economy--Data processing
Categories: TECHNOLOGY & ENGINEERING / Manufacturing, BUSINESS & ECONOMICS / Industries / Manufacturing, COMPUTERS / Data Science / General
Database: eBook Collection (EBSCOhost)
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  – Type: ebook-pdf
  – Type: ebook-epub
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Header DbId: nlebk
DbLabel: eBook Collection (EBSCOhost)
An: 2746548
RelevancyScore: 1103
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 1103.19409179688
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  Data: Data-Driven Optimization of Manufacturing Processes
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  Data: All machining process are dependent on a number of inherent process parameters. It is of the utmost importance to find suitable combinations to all the process parameters so that the desired output response is optimized. While doing so may be nearly impossible or too expensive by carrying out experiments at all possible combinations, it may be done quickly and efficiently by using computational intelligence techniques. Due to the versatile nature of computational intelligence techniques, they can be used at different phases of the machining process design and optimization process. While powerful machine-learning methods like gene expression programming (GEP), artificial neural network (ANN), support vector regression (SVM), and more can be used at an early phase of the design and optimization process to act as predictive models for the actual experiments, other metaheuristics-based methods like cuckoo search, ant colony optimization, particle swarm optimization, and others can be used to optimize these predictive models to find the optimal process parameter combination. These machining and optimization processes are the future of manufacturing. Data-Driven Optimization of Manufacturing Processes contains the latest research on the application of state-of-the-art computational intelligence techniques from both predictive modeling and optimization viewpoint in both soft computing approaches and machining processes. The chapters provide solutions applicable to machining or manufacturing process problems and for optimizing the problems involved in other areas of mechanical, civil, and electrical engineering, making it a valuable reference tool. This book is addressed to engineers, scientists, practitioners, stakeholders, researchers, academicians, and students interested in the potential of recently developed powerful computational intelligence techniques towards improving the performance of machining processes.
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  Data: <searchLink fieldCode="DE" term="%22Manufacturing+processes--Data+processing%22">Manufacturing processes--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Engineering+economy--Data+processing%22">Engineering economy--Data processing</searchLink>
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  Data: <searchLink fieldCode="ZK" term="%22TECHNOLOGY+%26+ENGINEERING+%2F+Manufacturing%22">TECHNOLOGY & ENGINEERING / Manufacturing</searchLink><br /><searchLink fieldCode="ZK" term="%22BUSINESS+%26+ECONOMICS+%2F+Industries+%2F+Manufacturing%22">BUSINESS & ECONOMICS / Industries / Manufacturing</searchLink><br /><searchLink fieldCode="ZK" term="%22COMPUTERS+%2F+Data+Science+%2F+General%22">COMPUTERS / Data Science / General</searchLink>
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RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 670.285
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Manufacturing processes--Data processing
        Type: general
      – SubjectFull: Engineering economy--Data processing
        Type: general
    Titles:
      – TitleFull: Data-Driven Optimization of Manufacturing Processes
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Kanak Kalita
      – PersonEntity:
          Name:
            NameFull: Ranjan Kumar Ghadai
      – PersonEntity:
          Name:
            NameFull: Xiao-Zhi Gao
      – PersonEntity:
          Name:
            NameFull: Kanak Kalita
      – PersonEntity:
          Name:
            NameFull: Ranjan Kumar Ghadai
      – PersonEntity:
          Name:
            NameFull: Xiao-Zhi Gao
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2021
            – D: 06
              M: 02
              Type: profile
              Y: 2021
          Identifiers:
            – Type: isbn-print
              Value: 9781799872061
            – Type: isbn-electronic
              Value: 9781799872085
            – Type: isbn-electronic
              Value: 9781799872092
          Titles:
            – TitleFull: Data-Driven Optimization of Manufacturing Processes
              Type: main
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