Výsledky vyhledávání - "Jatti, Vijaykumar"

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Performance Prediction of Data-Driven Knowledge summarization of High Entropy Alloys (HEAs) literature implementing Natural Language Processing algorithms

Autor: Mishra, Akshansh, Jatti, Vijaykumar S, More, Vaishnavi, Dasgupta, Anish, Dixit, Devarrishi, Sefene, Eyob Messele

The ability to interpret spoken language is connected to natural language processing. It involves teaching the AI how words relate to one another, how they are meant to be used, and in what settings. The goal of natural language processing (NLP) is t

Externí odkaz: http://arxiv.org/abs/2311.07584

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Machine Learning-Assisted Pattern Recognition Algorithms for Estimating Ultimate Tensile Strength in Fused Deposition Modeled Polylactic Acid Specimens

Autor: Mishra, Akshansh, Jatti, Vijaykumar S

In this study, we investigate the application of supervised machine learning algorithms for estimating the Ultimate Tensile Strength (UTS) of Polylactic Acid (PLA) specimens fabricated using the Fused Deposition Modeling (FDM) process. A total of 31

Externí odkaz: http://arxiv.org/abs/2307.06970

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Employing Explainable Artificial Intelligence (XAI) Methodologies to Analyze the Correlation between Input Variables and Tensile Strength in Additively Manufactured Samples

Autor: Mishra, Akshansh, Jatti, Vijaykumar S

This research paper explores the impact of various input parameters, including Infill percentage, Layer Height, Extrusion Temperature, and Print Speed, on the resulting Tensile Strength in objects produced through additive manufacturing. The main obj

Externí odkaz: http://arxiv.org/abs/2305.18426

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Neurosymbolic Artificial Intelligence (NSAI) based Algorithm for predicting the Impact Strength of Additive Manufactured Polylactic Acid (PLA) Specimens

Autor: Mishra, Akshansh, Jatti, Vijaykumar S

In this study, we introduce application of Neurosymbolic Artificial Intelligence (NSAI) for predicting the impact strength of additive manufactured polylactic acid (PLA) components, representing the first-ever use of NSAI in the domain of additive ma

Externí odkaz: http://arxiv.org/abs/2305.05668

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Quantum Machine Learning Approach for the Prediction of Surface Roughness in Additive Manufactured Specimens

Autor: Mishra, Akshansh, Jatti, Vijaykumar S.

Surface roughness is a crucial factor influencing the performance and functionality of additive manufactured components. Accurate prediction of surface roughness is vital for optimizing manufacturing processes and ensuring the quality of the final pr

Externí odkaz: http://arxiv.org/abs/2304.13142

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