Machine Learning for Sustainable Manufacturing in Industry 4.0

Concept, Concerns and Applications

• Author(s): Raman Kumar, Sita Rani, Sehijpal Singh Khangura,
• Publisher: CRC Press
• Pages: 250
• ISBN_10: 1000986195
  ISBN_13: 9781000986198
  
• Language: en
• Categories: Technology & Engineering / Industrial Engineering , Technology & Engineering / Mechanical , Technology & Engineering / Manufacturing , Technology & Engineering / Environmental / General , Technology & Engineering / Electrical , Technology & Engineering / Electronics / General , Technology & Engineering / Engineering (General) , Business & Economics / Production & Operations Management , Technology & Engineering / Hydraulics , Technology & Engineering / Automotive , Business & Economics / Purchasing & Buying ,

Description:...

The book focuses on the recent developments in the areas of error reduction, resource optimization, and revenue growth in sustainable manufacturing using machine learning. It presents the integration of smart technologies such as machine learning in the field of Industry 4.0 for better quality products and efficient manufacturing methods.

• Focusses on machine learning applications in Industry 4.0 ecosystem, such as resource optimization, data analysis, and predictions.
• Highlights the importance of the explainable machine learning model in the manufacturing processes.
• Presents the integration of machine learning and big data analytics from an industry 4.0 perspective.
• Discusses advanced computational techniques for sustainable manufacturing.
• Examines environmental impacts of operations and supply chain from an industry 4.0 perspective.

This book provides scientific and technological insight into sustainable manufacturing by covering a wide range of machine learning applications fault detection, cyber-attack prediction, and inventory management. It further discusses resource optimization using machine learning in industry 4.0, and explainable machine learning models for industry 4.0. It will serve as an ideal reference text for senior undergraduate, graduate students, and academic researchers in the fields including mechanical engineering, manufacturing engineering, production engineering, aerospace engineering, and computer engineering.