Application of artificial intelligence to determine the phase fraction of welded duplex steels using neural networks with partial use of augmented data

This paper addresses the application of artificial intelligence in metallography. The aim is to determine the phase fractions of ferrite and austenite in the weld zone of duplex stainless steel based on the evaluation of metallographic microscopic micrographs. The idea arose from the motivation to technically replicate the functionality of the biological eye–brain system, thereby combining the properties of the highly effective human vision (or its simulation) with the significantly faster technical image-processing system. Since the distribution of pixel amplitudes in the metallographic images is irregular, the use of artificial intelligence is a suitable alternative to algorithm-based or gray-value statistical methods. Butt-welded sheet metal strips, which were cut in the weld zone, were used as sample material. The cut surfaces were polished and color-etched. Microscopic images were taken of the prepared samples. In the subsequent image analysis, both simple perceptron variants and deep convolutional neural networks (CNNs) were used. These were designed using appropriate tools and (subsequently) implemented programmatically. To obtain a sufficient amount of training data, the use of synthetic data derived from original data was investigated and implemented for training the networks in addition to using polished samples from real specimens. Training and test runs were conducted with various datasets and network variants, and performance parameters were determined. The results obtained met expectations regarding both the use of synthetic data and the classification error rates of 2 %–3 %. Significant performance differences were observed in processing speed. Here, the convolutional neural networks performed considerably better than the perceptrons.