Intelligent machine agent architecture for adaptive control optimization of manufacturing processes

  • Authors:

  • Grant H. Kruger;Albert J. Shih;Danie G. Hattingh;Theo I. van Niekerk

  • Affiliations:

  • Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA;Mechanical Engineering, University of Michigan, Ann Arbor, MI, USA;Automotive Components Technology Station, Nelson Mandela Metropolitan University, Port Elizabeth, Eastern Cape, South Africa;Department of Mechatronics, School of Engineering, Nelson Mandela Metropolitan University, Port Elizabeth, Eastern Cape, South Africa

  • Venue:
  • Advanced Engineering Informatics
  • Year:
  • 2011

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Abstract

Intelligent agents have been earmarked as the key enabling technology to provide the flexibility required by modern, competitive, customer-orientated manufacturing environments. Rational agent behavior is of paramount importance when interacting with these environments to ensure significant losses are not incurred. To achieve rationality, intelligent agents must constantly balance technical (process) and economic (enterprise wide) trade-offs through co-operation, learning and autonomy. The research presented in this manuscript integrates methodological commonalities in intelligent manufacturing research and prognostics to design and evaluate a generic architecture for the core services of self-learning, rational, machining process regulation agents. The proposed architecture incorporates learning, flexibility and rational decision making through the integration of heterogeneous intelligent algorithms (i.e. neural networks and genetic algorithms) from fields such as machine learning, data mining and statistics. The architecture's ability to perceive, learn and optimize is evaluated on a high-volume industrial gun drilling process.