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Biography


Prof. David Bassir
University of Technology of Belfort-Montbeliard, France
Title:
What future for natural fibres in structured materials
Abstract:

During the last decade investigation of the mechanical properties of bio-composites materials using natural fiber has considerably evolved from basic engineering studies to more fundamental topics [1, 2]. For instance, the bio-polymer materials are considered as unavoidable alternatives for the replacement of oil based materials. The limited development of the bio-plastics market is particularly affected by the weak mechanical performance of these materials. To overcome these difficulties, one should focus on both: manufacturing process and modelling aspect. The second aspect related to simulation is an important task in the loop of integrating new fibers in structured material. The parameters of the numerical model require indeed to be identified in order to fit the experimental testing [3,4]. 

In this report two aspects will be discussed: presentation of industrial applications using natural fibers to produce advances materials structural concepts (such as the blades of offshore wind-turbine, and thermal protection panels) will be performed. Then, focus on the recent numerical tools to perform parameter identification included hybrid approaches that combine heuristic methods and neural network surface response [5]. Elasticity properties of a fully bio-degradable composite model will be defined as a numerical application.


References: 

(1) S. Guessasma, D. Bassir, L. Hedjazi, “Influence of interphase properties on the effective behaviour of a starch-hemp composite”, Materials & Design, Vols, 65, Pages 1053–1063, (Elsevier), 2015. 

(2) S. Guessasma, ,M. Hbib and D. Bassir, “Identification Scheme to Assess the role of interfacial damage in a Hemp-Starch Biocomposite”, Advanced Materials Research, Vols. 875-877, pp. 524-528, 2014. 

(3) S. Guessasma, D. Bassir, Identification of mechanical properties of biopolymer composites sensitive to interface effect using hybrid approach, Mechanics of Materials (Elsevier), Vol. 42, pp. 344-353, 2010. 

(4) Guessasma, D. Bassir, Optimisation of mechanical properties of virtual porous solids using a hybrid approach, ActaMateriala, (Elsevier), Vol. 58, pp. 716-725, 2010. 

(5) D. Bassir, S. Guessasma and M.L. Boubakar, Hybrid computational strategy based on ANN and GAPS: Application for identification of a non linear composite material, Journal of Composite Structures, (Elsevier), V 88, 2, pp. 262-270, 2009.

Biography:

David H. BASSIR is as Professor at the French University of Technology UTBM and also a foreign expert for advances material and structures at GZIIT-Chinese academy of sciences (China). Previously, he was the dean of IUT at the University of Lorraine (France), Consult for Science and Technology at the French Embassy to serve at the Consulate General of France in Guangzhou (China), General Director of Research at the Ecole Spéciale des Travaux Publics, du Batiment et de l'Industrie (Paris) and Space Craft engineer at GECI Technology in different space agencies such as Arianespace (France) and Matra Marconi Space (Astrium Group). He joined the Mechanical Department of the UTBM as Associate professor in 2001 and the Chair ? Aerospace Structures ? in 2008 at Technical University of Delft as visiting professor.

He holds a Master and a PhD degree in structural optimization from the University of Franche-Comté (France), with the most honorable mention. He has published more than 150 papers in journals, books and conference proceedings, including more than 30 articles in indexed journals.

He is the founder of the ASMDO association “Association for Simulation and Multidisciplinary design Optimization” and the Int. journal IJSMDO that is published by EDP Sciences. He serves as member of various expert committees in many international organizations and highly estimated scientific societies.

He’s actual scientific research activities are related to Simulation and Modelling of Composite materials either in Macro, Micro or Nano scales, Optimization strategies using gradients, heuristic methods and/or Artificial Neural Networks to solve industrials applications.

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