rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Modelling and Optimisation of Laser Assisted Oxygen (LASOX) Cutting: A Soft Computing Based Approach
  name:
    name part: Chaki, Sudipto.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Ghosal, Sujit.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: IX, 56 p. online resource.
  abstract: This book presents the basics of the Laser Assisted Oxygen (LASOX) cutting process, its development, advantages and shortcomings, together with detailed information on the research work carried out to date regarding the modelling and optimization of the process. It introduces two integrated soft computing-based models consisting of Artificial Neural Networks (ANN-GA and ANN SA) for the modelling and optimization of LASOX cutting. It also includes an in-depth discussion on the basic working algorithms of soft computing tools such as Artificial Neural Networks, Genetic Algorithms, Simulated Annealing etc. The book not only provides an approach to optimizing LASOX by means of soft computing-based integrated models, but also illustrates the practical implementation of the proposed models.
  table of contents: Preface -- Chapter 1. LASOX Cutting: Principles and Evolution -- Chapter 2. Integrated Soft Computing Based Methodologies for Modelling -- Chapter 3. Modelling and Optimisation of LASOX Cutting of Mild Steel: A Case Study.
  note: by Sudipto Chaki, Sujit Ghosal.
  subject:
    topic: Computational intelligence
  subject:
    topic: Manufactures
  subject:
    topic: Artificial intelligence
  subject:
    topic: Computational Intelligence
  subject:
    topic: Manufacturing, Machines, Tools, Processes
  subject:
    topic: Artificial Intelligence
  classification: Q342
  classification: 006.3
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Computational Intelligence
  identifier: 9783030049034
  identifier: https://doi.org/10.1007/978-3-030-04903-4
  record info:
    record creation date: 181215
    record change date: 20191022091911.0
    record identifier: 978-3-030-04903-4