Dhia K. Suker, Ahmed R. Abdo^*, Khalid Abdulkhaliq M. Alharbi

Intelligent Automation & Soft Computing, Vol.39, No.5, pp. 929-947, 2024, DOI:10.32604/iasc.2024.056341 - 31 October 2024

Abstract This paper presents a deep learning Convolutional Neural Network (CNN) for predicting grain orientations from electron backscatter diffraction (EBSD) patterns. The proposed model consists of multiple neural network layers and has been trained on a dataset of EBSD patterns obtained from stainless steel 316 (SS316). Grain orientation changes when considering the effects of temperature and strain rate on material deformation. The deep learning CNN predicts material orientation using the EBSD method to address this challenge. The accuracy of this approach is evaluated by comparing the predicted crystal orientation with the actual orientation under different conditions, More >

Lei Bao^1,2, Christophe Schuman¹, Jean-sébastien Lecomte¹, Marie-Jeanne Philippe¹, Xiang Zhao², Liang Zuo², Claude Esling¹

CMC-Computers, Materials & Continua, Vol.15, No.2, pp. 113-128, 2010, DOI:10.3970/cmc.2010.015.113

Abstract The mechanisms of small plastic deformation of titanium (T40) during cold rolling and channel die compression by means of "interrupted in situ" EBSD orientation measurements were studied. These interrupted EBSD orientation measurements allow to determine the rotation flow field which leads to the development of the crystallographic texture during the plastic deformation. Results show that during rolling, tension twins and compression twins occur and various glide systems are activated, the number of grains being larger with twins than with slip traces. In channel die compression, only tension twins are observed in some grains, whereas slip More >

Brent L. Adams, Joshua Kacher

CMC-Computers, Materials & Continua, Vol.14, No.3, pp. 185-196, 2009, DOI:10.3970/cmc.2009.014.185

Abstract Consideration is given to the resolution of dislocation density afforded by EBSD-based scanning electron microscopy. Comparison between the conventional Hough- and the emerging high-resolution cross-correlation-based approaches is made. It is illustrated that considerable care must be exercised in selecting a step size (Burger's circuit size) for experimental measurements. Important variables affecting this selection include the dislocation density and the physical size and density of dislocation dipole and multi-pole components of the structure. It is also illustrated that simulations can be useful to the interpretation of experimental recoveries. More >

Xiaohua Hu¹, Gordana A. Cingara¹, David S. Wilkinson¹, Mukesh Jain², PeidongWu², Raja K. Mishra³

CMC-Computers, Materials & Continua, Vol.14, No.2, pp. 99-124, 2009, DOI:10.3970/cmc.2009.014.099

Abstract This work aims to understand the texture distribution in the localized necking band formed during uni-axial tension of AA5754 using an edge-constrained, plane strain post-necking FE model. The model domain is a long cross section of the band. Initial grain structure is mapped into the mesh from EBSD data using a modified Voroni-cell interpolation and considering pre-straining prior to localized necking. The material points in grains are assumed to exhibit isotropic elastoplastic behavior but have a relative strength in terms of Taylor factors which are updated by a Taylor-Bishop-Hill model. The predicted textures and gradients More >