Deep Convolutional Generative Modeling for Artificial Microstructure Development of Aluminum-Silicon Alloy 
Akshansh Mishra1, Tarushi Pathak2
1Akshansh Mishra, Centre for Artificial Intelligent Manufacturing Systems, Stir Research Technologies, India.
2Tarushi Pathak, Department of Computer Science and Engineering, SRM Institute of Science and Technology, Kattangulathur, India.
Manuscript received on 25 March 2021 | Revised Manuscript received on 15 April 2021 | Manuscript Accepted on 15 May 2021 | Manuscript published on 30 May 2021 | PP: 1-6 | Volume-1 Issue-1, May 2021 | Retrieval Number: 100.1/ijdm.A1603051121 | DOI: 10.54105/ijdm.A1603.051121
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Abstract: 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.
Keywords: Machine Learning; Deep Generative Modeling; Artificial Microstructure; Generative Adversarial Network
Scope: Visualization