Edge-detection-of-Friction-Stir-Welded-Joints-by-using-Laplace-and-Fourier-Transformation

Visual inspection has played a vital role in the beginning era of science. Nowadays, image processing is finding application for defects analysis of the manufactured parts in many industrial processes. We have implemented two machine learning-based image processing techniques in recent work, i.e., Fourier Transformation operator and Laplacian operator for the surface defects detection in Friction Stir Welded joints. The quality of the weld surface in the Friction Stir Welding process depends on the input parameters such as Tool Rotational Speed (rpm), Tool Traverse Speed (mm/min), and an Axial Force (kN).

Machine-Learning-Approach-to-Determine-Corrosion-Potential-of-Friction-Stir-Welded-Joints

Deep-Convolutional-Generative-Modeling-for-Artificial-Microstructure-Development-of-Aluminum-Silicon

: Machine learning which is a sub-domain of an Artificial Intelligence which is finding various applications in manufacturing and material science sectors. In the present study, Deep Generative Modeling which a type of unsupervised machine learning technique has been adapted for the constructing the artificial microstructure of Aluminium-Silicon alloy. Deep Generative Adversarial Networks has been used for developing the artificial microstructure of the given microstructure image dataset. The results obtained showed that the developed models had learnt to replicate the lining near the certain images of the microstructures