0000-0003-4939-359X About
Akshansh Mishra is currently enrolled as an MS in Materials Engineering and Nanotechnology at Politecnico Di Milano. He generally works on the implementation of Artificial Intelligence tools in the domain of manufacturing. His ongoing research projects are synthetic microstructure development of Aluminum-Silicon alloy by using Deep Convolutional Generative Modelling and mechanical properties optimization of Friction Stir Welded joints as well as metal matrix composites.
Employment
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Neural Net Computing Scientist
Education
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Politecnico di Milano Masters2021 - Present
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SRM University B. tech2013 - 2017
Projects & Funding
Projects & funding information is unavailable.
Publications (86)
- Generative AI-Enhanced Finite Element Analysis of Aluminum Cold Spray Deposition for Stress Prediction Save
- Evolutionary computing-based image segmentation method to detect defects and features in additive friction stir deposition process Save
- Atomistic Modeling of Thermomechanical and Microstructural Evolution in Additive Friction Stir Deposition Save
- Assessing the Process-Property Relationship in Laser Powder Bed Fusion of AlSi10Mg Using Kalman Filter-Based Machine Learning Algorithms Save
- LatticeML: a data-driven application for predicting the effective Young Modulus of high temperature graph based architected materials Save
- Supervised Machine Learning and Physics Machine Learning approach for prediction of peak temperature distribution in Additive Friction Stir Deposition of Aluminium Alloy Save
- Novel neurosymbolic artificial intelligence (NSAI) based algorithm to predict specific energy absorption in CoCrMo based architected materials Save
- Modeling the Structural Dynamics of Carbon Fiber Composites for Robotic Systems Under Sinusoidal Load Save
- Advanced Displacement Magnitude Prediction in Multi-Material Architected Lattice Structure Beams Using Physics Informed Neural Network Architecture Save
- Novel Coupled Genetic Algorithm–Machine Learning Approach for Predicting Surface Roughness in Fused Deposition Modeling of Polylactic Acid Specimens Save