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Mubashshar Ahmed, Satya Panigrahi Dept of Agricultural and Bioresource Engineering

Exploring the Potential of Neural Networks for Characterizing Biocomposite Material Properties: A Review. Mubashshar Ahmed, Satya Panigrahi Dept of Agricultural and Bioresource Engineering University of Saskatchewan M.M. Gupta Dept of Mechanical Engineering University of Saskatchewan

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Mubashshar Ahmed, Satya Panigrahi Dept of Agricultural and Bioresource Engineering

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  1. Exploring the Potential of Neural Networks for Characterizing Biocomposite Material Properties:A Review Mubashshar Ahmed, Satya Panigrahi Dept of Agricultural and Bioresource Engineering University of Saskatchewan M.M. Gupta Dept of Mechanical EngineeringUniversity of Saskatchewan Denise S. D. Stilling Industrial Systems Engineering University of Regina University of Saskatchewan

  2. Introduction • Materials today is going through a rapid revolution. • Using nano, micro technology and fusion of different materials new materials are being created • Materials engineering is at the fore front of rapid development

  3. Biocomposite • Biocomposite materials utilize natural fibres to reinforce matrix material. • Material mix creates properties superior to its constituents. • Varying fibre and matrix creating new material with specified properties.

  4. Biocomposite ...contd Biocomposites have an extensive history with industry use dating to the early 20th century as promulgated by Henry Ford. Fig: Henry Ford swings hammer at hemp-composite trunk lid on Ford car http://www.hemphasis.net/Building/plasticmettle.htm

  5. Biocomposite …contd Applications today range from structural applications for civil structures to semi-structural for component designs and daily household products. Biocomposite Lumber production http://uwadmnweb.uwyo.edu/sbir/Wssi/images/nwsltr_pics/HeartlandBioProto.jpg

  6. Biocomposite problems • Biocomposites face the challenge that their properties are varying, non-homogeneous and inconsistent. • Industry applications cannot tolerate this inherent variation • Inconsistency has proven to be a challenge in developing biocomposites for consumer market

  7. problems …contd Design Engineering Process. The goal is to meet the end user requirements the first time with low cost (Characterization and Failure Analysis of Plastics, 2003)

  8. problems …contd • Time required for consistent product development • Rapid prototyping not possible • Biological Materials are inherently complex to model • Huge investment in Research and Development • The result is biocomposites have not gained its fair market share.

  9. A solution! • The properties of biocomposites are multifaceted and often require complex computations to model effectively. • One tool capable of performing such parallel processing is artificial neural networks (NN). • NN have been applied to other applications requiring complex algorithms with notable successes for many different areas ranging from neuro-vision, neuro-control and others. (Gupta et. al., 2003).

  10. Neural Networks Fig: Artificial NN in operation Fig: Biological neurons synaptic operations

  11. Neural Networks …contd

  12. Neural Networks …contd

  13. Neural Networks …contd Neural Pattern Classifiers x2 (-1,1) (1,1) x1 (-1,-1) (1,-1)

  14. Neural Networks …contd • Neural Unit with Linear Synaptic Operation (LSO) OR logic operations AND logic operations

  15. Neural Networks …contd XOR logic operations

  16. NN & materials Ashida (2003), have worked on Multilayer composites plates. In their effort to optimize the design they have used neural networks. In their study their have tried to determine the thickness of each peroceramic layer by using neural networks

  17. NN & materials …contd According to Bhadeshia, (1999), in his review work on neural networks in materials science –points out that there are many problems where the quantitative treatments are “dismally” lacking.

  18. NN & materials …contd Tho et. al., 2004, used neural networks to interpret load displacement curves. They used data derived from finite element analyses to train and also validate the artificial neural network. Their research model as used in their study is the figure above

  19. NN & materials …contd A Generalized Regression Neural Network was used by Ren and Yao, 2004. Their study performed structural optimization of pneumatic tire using neural Networks.

  20. Summary • Neural network models can be wherever the complexity • of problem is overwhelming and simplification is not • acceptable • NN in Materials Science and Engineering • Use for Biological Materials

  21. Acknowledgements • Dr.Satyanarayan Panigrahi, Dr. M.M. Gupta • Bill Crerar, Jimmy Fung • NSERC, Saskatchewan Agriculture and Food (SAF) • AMUBE Group, College of Engineering • SASKBET, Biofiber Industries Ltd.

  22. Questions ?

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