AKSHANSH MISHRA's picture
Real name: 
AKSHANSH MISHRA
First Name: 
AKSHANSH
Last Name: 
MISHRA
Affiliation: 
Politecnico Di Milano, Milan, Italy
Job Title: 
MS Student
Expertise: 
Machine Learning; Advanced Manufacturing
Short Bio: 
Akshansh Mishra is currently enrolled as an MS in Materials Engineering student in 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.

Datasets & Competitions

Strut based architected materials with varying strut diameter

The presented dataset contains information about struts utilized in a material system, including three key attributes: strut diameter, strut type, and sample number. The strut diameter describes the structural element's physical dimension, whereas the strut type specifies the design or placement inside the material, such as edge configurations. A sample number is assigned to each sample, identifying it uniquely. This data can be used in machine learning systems to forecast material qualities, optimize designs, and investigate the effect of strut configurations on structural performance.

Categories:
Artificial Intelligence
Machine Learning
Health
124 Views

Graph Architected Lattice Materials based Tensile Samples

Architected materials, which are designed and engineered with specific microstructures, hold great promise for healthcare applications. In particular, tensile samples based on architected materials exhibit unique properties that can address key challenges in medical devices and implants. By precisely controlling the architecture at the micro- and nano-scale, these materials can be optimized for high strength-to-weight ratio, tunable stiffness, and enhanced biocompatibility.

Categories:
Other
Health
150 Views

Artificial Intelligence Algorithms for the Analysis of Mechanical Property of Friction Stir Welded Joints by using Python Programming

In modern computational science, the interplay existing between machine learning and optimization process marks the most vital developments. Optimization plays an important role in mechanical industries because it leads to reduce in material cost, time consumption and increase in production rate. The recent work focuses on performing the optimization task on Friction Stir Welding process for obtaining the maximum Ultimate Tensile Strength (UTS) of the friction stir welded joints. Two machine learning algorithms i.e.

Categories:
Machine Learning
416 Views

Thin film thickness of Polystyrene on the glass substrates

Advent in machine learning is leaving a deep impact on various sectors including the material science domain. The present paper highlights the application of various supervised machine learning regression algorithms such as polynomial regression, decision tree regression algorithm, random forest algorithm, support vector regression algorithm and artificial neural network algorithm to determine the thin film thickness of Polystyrene on the glass substrates.

Categories:
Machine Learning
255 Views