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Sofiane Achiche

Sofiane Achiche, B.Eng, M.Sc.A., Ph.D., is a professor at Polytechnique Montréal. He specializes in artificial intelligence applied to manufacturing processes, decision support, product development and multi-domain system design.

During his career, he was an associate professor in the Engineering Design and Product Development section of the Department of Mechanical Engineering at the Technical University of Denmark ( between 2006 and 2011. During that period, he began working in the field of new product development and emotional design.

He has published his research in various international journals such as Research in Engineering Design and AIEDAM and has been invited to present his results internationally (ICED, ASME/DETC, NAFIPS, etc.).

He is currently working on several ongoing projects. One of the projects is being conducted in collaboration with the Technical University of Denmark and involves the follow-up and automatic and intelligent diagnosis of machines (numerically controlled machines, wind turbines, etc.).


  • M. Schlechtingen, I.F. Santos, S. Achiche, « Wind turbine condition monitoring based on SCADA data: Part 1 – System Description», Journal. of App. Soft Computing, V13, p. 259-270, 2012.

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  • S. Achiche, F.P. Appio, T. McAloone, A. Di Minin, « Fuzzy Decision Support for Tool Selection in the Core Front End Activities of NPD», Research in Engineering Design, DOI 10.1007/s00163-012-0130-4, 2012.

  • S. Achiche, T.J.Howard, T.C. McAloone, L. Baron. « Investigating features influence in fuzzy modelling of mass perception of non-functional 3D CAD forms», Int. J. Prod. Dev., 16:2, 112–139, 2012.


  • associate professor, Polytechnique Montréal



  • Design automation and decision support
  • Mechatronics, artificial intelligence and design
  • Innovation
  • Use and optimization of Design for X Methods
  • Analysis, design, modeling and control of mechanical and mechatronic systems


  • Understanding open innovation practices
  • Design for X and Open Source methods
  • Multidisciplinary considerations in mechatronic design
  • Emotional design and Kansei Engineering; machine learning through evolutionary algorithms and approximate reasoning
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