AI-based modeling and data-driven evaluation for smart manufacturing processes

Mohammadhossein Ghahramani, Yan Qiao, Meng Chu Zhou, Adrian O. Hagan, James Sweeney

Research output: Contribution to journalArticlepeer-review

181 Scopus citations


Smart manufacturing refers to optimization techniques that are implemented in production operations by utilizing advanced analytics approaches. With the widespread increase in deploying industrial internet of things (IIOT) sensors in manufacturing processes, there is a progressive need for optimal and effective approaches to data management. Embracing machine learning and artificial intelligence to take advantage of manufacturing data can lead to efficient and intelligent automation. In this paper, we conduct a comprehensive analysis based on evolutionary computing and neural network algorithms toward making semiconductor manufacturing smart. We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes and to address various challenges. We elaborate on the utilization of a genetic algorithm and neural network to propose an intelligent feature selection algorithm. Our objective is to provide an advanced solution for controlling manufacturing processes and to gain perspective on various dimensions that enable manufacturers to access effective predictive technologies.

Original languageEnglish (US)
Article number9049451
Pages (from-to)1026-1037
Number of pages12
JournalIEEE/CAA Journal of Automatica Sinica
Issue number4
StatePublished - Jul 2020

All Science Journal Classification (ASJC) codes

  • Control and Systems Engineering
  • Information Systems
  • Artificial Intelligence


  • Artificial intelligence (AI)
  • cyber physical systems
  • feature selection
  • genetic algorithms (GA)
  • industrial internet of things (IIOT)
  • machine learning
  • neural network (NN)
  • smart manufacturing


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