Abstract
This paper demonstrates that the instrumented indentation test (IIT), together with a trained artificial neural network (ANN), has the capability to characterize the mechanical properties of the local parts of a welded steel structure such as a weld nugget or heat affected zone. Aside from force-indentation depth curves generated from the IIT, the profile of the indented surface deformed after the indentation test also has a strong correlation with the materials’ plastic behavior. The profile of the indented surface was used as the training dataset to design an ANN to determine the material parameters of the welded zones. The deformation of the indented surface in three dimensions shown in images were analyzed with the computer vision algorithms and the obtained data were employed to train the ANN for the characterization of the mechanical properties. Moreover, this method was applied to the images taken with a simple light microscope from the surface of a specimen. Therefore, it is possible to quantify the mechanical properties of the automotive steels with the four independent methods: (1) force-indentation depth curve; (2) profile of the indented surface; (3) analyzing of the 3D-measurement image; and (4) evaluation of the images taken by a simple light microscope. The results show that there is a very good agreement between the material parameters obtained from the trained ANN and the experimental uniaxial tensile test. The results present that the mechanical properties of an unknown steel can be determined by only analyzing the images taken from its surface after pushing a simple indenter into its surface.
Highlights
Thermal manufacturing processes of high strength steels such as welding or cutting lead to the local deterioration of the mechanical properties [1]
The instrumented indentation test (IIT) is a well-known semi-destructive method that enables the determination of the mechanical properties [3] of small areas such as welded zones [4]
Comparison between the output of the and thethe reference the trained with the features extracted from images shows the deformation of the indented the artificial neural network (ANN) trained with the features extracted from images shows the deformation of the indented surface (3D-measurement image)
Summary
Thermal manufacturing processes of high strength steels such as welding or cutting lead to the local deterioration of the mechanical properties [1]. The indentation response depends on the material mechanical properties such as the stress–strain curve. Feature extraction with CNN layers requires a handful of labeled images for the training procedure [39] Another alternative is to use image segmentation as one of the computer vision algorithms. A training dataset contains material parameters that describe a stress–strain diagram as the output and a corresponding force–indentation depth curve or indented surface profile as the input. The image contains fewer variables, but are representative of the original image This information was used as input for the training of the ANN. 2020, 10,After segmentation, the image contains fewer variables, but are representative of ofthe original image
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