Machine learning-driven optimization of TPMS architected materials using simulated annealing
Paper Link: https://link.springer.com/article/10.1007/s44379-024-00001-z
Novel neurosymbolic artificial intelligence (NSAI) based algorithm to predict specific energy absorption in CoCrMo based architected materials
Paper Link: https://link.springer.com/article/10.1007/s41870-024-02173-6
Machine learning-assisted wire arc additive manufacturing and heat input effect on mechanical and corrosion behaviour of 316 L stainless steels
Paper Link: https://www.sciencedirect.com/science/article/pii/S2352012424012785?dgcid=coauthor
LatticeML: a data-driven application for predicting the effective Young Modulus of high temperature graph based architected materials
Paper Link: https://link.springer.com/article/10.1007/s12008-024-01976-y
Performance Prediction of Data-Driven Knowledge summarization of High Entropy Alloys (HEAs) literature implementing Natural Language Processing algorithms
Paper Link: https://www.jsoftcivil.com/article_196430.html
Deposition quality optimization of additive friction stir deposited aluminium alloy using unsupervised machine learning
Paper Link: https://www.adeletters.com/no-2-2-2024/
A cutting-edge framework for surface roughness prediction using multiverse optimization-driven machine learning algorithms
Paper Link: https://link.springer.com/article/10.1007/s12008-024-01770-w
Homogenization of Inconel 625 based periodic auxetic lattice structures with varying strut thickness
Paper link: https://pubs.aip.org/aip/apm/article/12/2/021128/3267517/Homogenization-of-Inconel-625-based-periodic
Sustainable Materials: The Role of Artificial Intelligence and Machine Learning
Fracture cracks localization in machined H13 tool steel using computer vision algorithms
Paper Link: https://www.sciencetalks-journal.com/article/S2772-5693(23)00167-6/fulltext
Machine learning-assisted pattern recognition algorithms for estimating ultimate tensile strength in fused deposition modelled polylactic acid specimens
Paper Link: https://www.tandfonline.com/doi/full/10.1080/10667857.2023.2295089?src=exp-la
Prediction of Ultimate Tensile Strength of Additive Manufactured Specimens using Neurosymbolic based Machine Learning Algorithm.
Conference Paper Link: https://dl.acm.org/doi/10.1145/3647444.3652471
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
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