| Steel and Composite Structures Volume 25, Number 6, December30 2017, pages 663-670 DOI: http://dx.doi.org/10.12989/scs.2017.25.6.663 | |
Failure estimation of the composite laminates
using machine learning techniques | |
Alexandru Şerban
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| Abstract [Full Text] | |
| The problem of layup optimization of the composite laminates involves a very complex multidimensional solution space which is usually non-exhaustively explored using different heuristic computational methods such as genetic algorithms (GA). To ensure the convergence to the global optimum of the applied heuristic during the optimization process it is necessary to evaluate a lot of layup configurations. As a consequence the analysis of an individual layup configuration should be fast enough to maintain the convergence time range to an acceptable level. On the other hand the mechanical behavior analysis of composite laminates for any geometry and boundary condition is very convoluted and is performed by computational expensive numerical tools such as finite element analysis (FEA). In this respect some studies propose very fast FEA models used in layup optimization. However, the lower bound of the execution time of FEA models is determined by the global linear system solving which in some complex applications can be unacceptable. Moreover, in some situation it may be highly preferred to decrease the optimization time with the cost of a small reduction in the analysis accuracy. In this paper we explore some machine learning techniques in order to estimate the failure of a layup configuration. The estimated response can be qualitative (the configuration fails or not) or quantitative (the value of the failure factor). The procedure consists of generating a population of random observations (configurations) spread across solution space and evaluating using a FEA model. The machine learning method is then trained using this population and the trained model is then used to estimate failure in the optimization process. The results obtained are very promising as illustrated with an example where the misclassification rate of the qualitative response is smaller than 2%. | |
| Key Words | |
| failure estimation; layup optimization; machine learning; finite element analysis; numerical analysis | |
| Address | |
| Alexandru Şerban: Faculty of Mechanical Engineering, University of Gh. Asachi, Iasi, 700050, Romania | |
