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Civil-Comp Proceedings
ISSN 1759-3433
CCP: 93
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Paper 81

Modelling and Testing of the Properties of Recovered Composite Material

J. Kers1, D. Goljandin1, K. Tall1, A. Aruniit1, E. Adoberg1, M. Saarna1 and J. Majak2

1Department of Materials Engineering, 2Department of Machinery,
Tallinn University of Technology, Estonia

Full Bibliographic Reference for this paper
J. Kers, D. Goljandin, K. Tall, A. Aruniit, E. Adoberg, M. Saarna, J. Majak, "Modelling and Testing of the Properties of Recovered Composite Material", in , (Editors), "Proceedings of the Tenth International Conference on Computational Structures Technology", Civil-Comp Press, Stirlingshire, UK, Paper 81, 2010. doi:10.4203/ccp.93.81
Keywords: composite scrap, recycling, disintegrator mills, multicriteria optimization, new composite.

Mechanical reprocessing is continually the most commonly used technology for recycling thermosetting composite plastics. The reprocessing of the composite PMMA+GFP plastic scrap by using disintegrator milling will enable the production of the acrylic plastic powder with a determined granularity and technological properties (the apparent density).

Multi-functional disintegrator mills have been utilized for processing different materials. The technology of milling using the disintegrator devices is compared with the existing advanced technologies for the size reduction of the composite and compounded plastic scrap (by using horizontal shredders and granulators). The advantages of disintegrator milling can be outlined as:

  • the simultaneous separation of the components of the composite (impossible with shredders and granulators);
  • lower specific energy of treatment.
The new composite is modelled on the basis of the properties of the new material. The target characteristics of the material considered are the tensile strength and surface hardness. Another important factor is cost of the new material. A combination of fractions of the PMMA powder, the mixing ratio of fractions and the mixing ratio of resin and powder are considered as design variables. The relation between the objectives and design variables is modelled on the basis of the experimental data. The surrogate models are used to guide the search towards a global optimum. Neural networks are applied for the approximation of the tensile strength and surface hardness of the material.

The problem considered consists of three objectives: the tensile strength and surface hardness subjected to a maximization and the cost of the materials subjected to minimization. Analysing the behaviour of the objectives considered it has been concluded that the contradictonary behaviour can be perceived between the tensile strength and cost, also between the surface hardness and cost. However, the tensile strength and surface hardness behave similarly. For that reason the two objectives: tensile strength and surface hardness are combined into one objective employing a physical programming technique. The relationship between the two combined criteria and cost (third criteria) is elucidated by use of the Pareto optimality concept. A real-coded genetic algorithm in combination with a gradient method has been utilized for finding the global extreme values of the objective functions. The sensitivity analysis showed that the objective functions are most sensitive with respect to the mixing ratio of resin and powder.

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