Hybrid deep learning model to predict the ultimate tensile strength of friction stir welded joints

Paper Link:  https://www.sciencedirect.com/science/article/pii/S0952197625010012

Biomimetic Machine Learning approach for prediction of mechanical properties of Additive Friction Stir Deposited Aluminum alloys based walled structures 

Preprint Link: https://arxiv.org/abs/2408.05237

Machine learning-driven optimization of TPMS architected materials using simulated annealing

Paper Link: https://link.springer.com/article/10.1007/s44379-024-00001-z

Evolutionary Computing Coupled Machine Learning Algorithms to Predict the Temperature Distribution of Additive Friction Stir Deposited Aluminum Alloy

Paper Link: https://www.sciencedirect.com/science/article/pii/S1877050923021257?via%3Dihub

Exploratory analysis and evolutionary computing coupled machine learning algorithms for modelling the wear characteristics of AZ31 alloy

Paper Link: https://www.sciencedirect.com/science/article/abs/pii/S2352492823021980

Computer Vision Algorithm for Predicting the Welding Efficiency of Friction Stir Welded Copper Joints from its Microstructures

Conference Paper Link: https://www.e3s-conferences.org/articles/e3sconf/abs/2023/67/e3sconf_icmpc2023_01252/e3sconf_icmpc2023_01252.html

Performance Evaluation of ML-Based Algorithm and Taguchi Algorithm of the Hardness Value of the Friction Stir Welded AA6262 Joints at a Nugget Joint

Conference Paper Link: https://www.e3s-conferences.org/articles/e3sconf/abs/2023/67/e3sconf_icmpc2023_01249/e3sconf_icmpc2023_01249.html

Surface roughness and surface crack length prediction using supervised machine learning–based approach of electrical discharge machining of deep cryogenically treated NiTi, NiCu, and BeCu alloys

Paper Link: https://link.springer.com/article/10.1007/s00170-023-12269-1

Evolutionary AI-Based Algorithms for the Optimization of the Tensile Strength of Additively Manufactured Specimens

Book Chapter Link: https://link.springer.com/chapter/10.1007/978-3-031-37454-8_10

A multi-criterion optimization of mechanical properties and sustainability performance in friction stir welding of 6061-T6 AA

Paper Link: https://www.sciencedirect.com/science/article/pii/S2352492823015295

Neurosymbolic artificial intelligence (NSAI) based algorithm for predicting the impact strength of additive manufactured polylactic acid (PLA) specimens

Paper Link: https://iopscience.iop.org/article/10.1088/2631-8695/ace610

Synthesis and characterization of diamond-like carbon coatings for drill bits using plasma-enhanced chemical vapor deposition 

Paper Link: https://link.springer.com/article/10.1007/s00170-023-11794-3

Prediction of Wear Rate in Al/SiC Metal Matrix Composites Using a Neurosymbolic Artificial Intelligence (NSAI)-Based Algorithm

Paper Link: https://www.mdpi.com/2075-4442/11/6/261

Novel Coupled Genetic Algorithm–Machine Learning Approach for Predicting Surface Roughness in Fused Deposition Modeling of Polylactic Acid Specimens 

Paper link: https://link.springer.com/article/10.1007/s11665-023-08379-2

 Optimizing flexural strength of fused deposition modelling using supervised machine learning algorithms 

Paper Link: https://link.springer.com/article/10.1007/s41870-023-01329-0

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

Paper Link: https://arxiv.org/abs/2305.18426

Explainable Artificial Intelligence (XAI) and Supervised Machine Learning-based Algorithms for Prediction of Surface Roughness of Additively Manufactured Polylactic Acid (PLA) Specimens

Paper Link: https://www.mdpi.com/2673-3161/4/2/34

Fracture analysis of friction stir spot welded acrylonitrile butadiene styrene sheet in butt configuration

Paper link: https://iopscience.iop.org/article/10.1088/2053-1591/acd1d6

Reinforcement learning based approach for the optimization of mechanical properties of additively manufactured specimens 

Paper link: https://link.springer.com/article/10.1007/s12008-023-01257-0

Artificial intelligence algorithms for prediction of the ultimate tensile strength of the friction stir welded magnesium alloys 

Paper link: https://link.springer.com/article/10.1007/s12008-022-01180-w

Machine Learning Based Predictive Modeling of Electrical Discharge Machining of Cryo-Treated NiTi, NiCu and BeCu Alloys

Paper link: https://www.mdpi.com/2571-5577/5/6/107/htm

Supervised and Unsupervised Machine Learning Algorithms for Forecasting the Fracture Location in Dissimilar Friction-Stir-Welded Joints

Paper link: https://www.mdpi.com/2571-9394/4/4/43

Optimization of the Mechanical Property of Friction Stir Welded Heat Treatable Aluminum Alloy by using Bio-Inspired Artificial Intelligence Algorithms

Paper link: https://fracturae.com/index.php/fis/article/view/3767

Determination of the Ultimate Tensile Strength (UTS) of friction stir welded similar AA6061 joints by using supervised machine learning based algorithms

Paper link: https://www.sciencedirect.com/science/article/abs/pii/S2213846322000207

Machine learning algorithms for prediction of penetration depth and geometrical analysis of weld in friction stir spot welding process

Paper link: https://www.metallurgical-research.org/articles/metal/abs/2022/03/metal210227/metal210227.html

Data driven knowledge summarization of friction stir welded magnesium alloys literature by using natural language processing algorithms 

Paper link: https://link.springer.com/article/10.1007/s12008-022-01118-2

Process Parameter Optimization of 6061AA Friction Stir Welded Joints Using Supervised Machine Learning Regression-Based Algorithms

Paper link: https://www.jsoftcivil.com/article_147518.html