Mechanical Properties Characterization of Welded Automotive Steels

Among the various welding technologies, resistance spot welding (RSW) and laser beam welding (LBW) play a significant role as joining methods for the automobile industry. The application of RSW and LBW for the automotive body alters the microstructure in the welded areas. It is necessary to identify the mechanical properties of the welded material to be able to make a reliable statement about the material behavior and the strength of welded components. This study develops a method by which to determine the mechanical properties for the weldment of RSW and LBW for two dual phase (DP) steels, DP600 and DP1000, which are commonly used for the automotive bodies. The mechanical properties of the resistance spot weldment were obtained by performing tensile tests on the notched tensile specimen to cause an elongation of the notched and welded area in order to investigate its properties. In order to determine the mechanical properties of the laser beam weldment, indentation tests were performed on the welded material to calculate its force-penetration depth-curve. Inverse numerical simulation was used to simulate the indentation tests to determine and verify the parameters of a nonlinear isotropic material model for the weldment of LBW. Furthermore, using this method, the parameters for the material model of RSW were verified. The material parameters and microstructure of the weldment of RSW and LBW are compared and discussed. The results show that the novel method introduced in this work is a valid approach to determine the mechanical properties of welded high-strength steel structures. In addition, it can be seen that LBW and RSW lead to a reduction in ductility and an increase in the amount of yield and tensile strength of both DP600 and DP1000

Quantifying Mechanical Properties of Automotive Steels with Deep Learning Based Computer Vision Algorithms

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

Mechanical Properties Characterization of Welded Automotive Steels

Among the various welding technologies, resistance spot welding (RSW) and laser beam welding (LBW) play a significant role as joining methods for the automobile industry. The application of RSW and LBW for the automotive body alters the microstructure in the welded areas. It is necessary to identify the mechanical properties of the welded material to be able to make a reliable statement about the material behavior and the strength of welded components. This study develops a method by which to determine the mechanical properties for the weldment of RSW and LBW for two dual phase (DP) steels, DP600 and DP1000, which are commonly used for the automotive bodies. The mechanical properties of the resistance spot weldment were obtained by performing tensile tests on the notched tensile specimen to cause an elongation of the notched and welded area in order to investigate its properties. In order to determine the mechanical properties of the laser beam weldment, indentation tests were performed on the welded material to calculate its force-penetration depth-curve. Inverse numerical simulation was used to simulate the indentation tests to determine and verify the parameters of a nonlinear isotropic material model for the weldment of LBW. Furthermore, using this method, the parameters for the material model of RSW were verified. The material parameters and microstructure of the weldment of RSW and LBW are compared and discussed. The results show that the novel method introduced in this work is a valid approach to determine the mechanical properties of welded high-strength steel structures. In addition, it can be seen that LBW and RSW lead to a reduction in ductility and an increase in the amount of yield and tensile strength of both DP600 and DP1000

Quantifying Mechanical Properties of Automotive Steels with Deep Learning Based Computer Vision Algorithms

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