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A new solution for automatic microstructures analysis from images based on a backpropagation artificial neural network

机译：基于反向传播人工神经网络的图像自动微结构分析新解决方案

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This article presents a new solution to segment and quantify the microstructures from images of nodular, grey, and malleable cast irons, based on an artificial neural network. The neural network topology used is the multilayer perception, and the algorithm chosen for its training was the backpropagation. This solution was applied to 60 samples of cast iron images and results were very similar to the ones obtained by visual human tests. This was better than the information obtained from a commercial system that is very popular in this area. In fact, this solution segmented the images of microstructures materials more efficiently. Thus, we can conclude that it is a valid and adequate option for researchers, engineers, specialists, and professionals from materials science field to realise a microstructure analysis from images faster and automatically.
机译：本文提出了一种新的解决方案，可以基于人工神经网络从球墨铸铁，灰色铸铁和可锻铸铁图像中分割和量化显微组织。使用的神经网络拓扑是多层感知，而为其训练选择的算法是反向传播。该解决方案应用于60个铸铁图像样本，结果与通过视觉人体测试获得的结果非常相似。这比从该领域非常流行的商业系统获得的信息要好。实际上，该解决方案更有效地分割了微结构材料的图像。因此，我们可以得出结论，对于材料科学领域的研究人员，工程师，专家和专业人员来说，从图像中更快地自动进行微观结构分析是一种有效且适当的选择。

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