ArticleHeliyon2024
Application of machine learning and neural network models based on experimental evaluation of dissimilar resistance spot-welded joints between grade 2 titanium alloy and AISI 304 stainless steel.
Article in Heliyon, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Review
- Data-Driven Modeling of Friction in Drawbead Test Through Advanced Machine Learning.Materials (Basel, Switzerland) · 2026Article
- Prediction and Optimization of Load-Bearing Capacity in Resistance Spot Welded Titanium Joints Using Neural Networks and Genetic Algorithms.Materials (Basel, Switzerland) · 2026Article
- Modeling the Friction Behavior of Low-Carbon Steel Sheets Using Various Machine Learning Algorithms Based on Strip Drawing Test Data.Materials (Basel, Switzerland) · 2026Article
- Inverse Neural Network Approach for Optimizing Chemical Composition in Shielded Metal Arc Weld Metals.Materials (Basel, Switzerland) · 2025Article
- Predicting the Effect of RSW Parameters on the Shear Force and Nugget Diameter of Similar and Dissimilar Joints Using Machine Learning Algorithms and Multilayer Perceptron.Materials (Basel, Switzerland) · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of a composite welded joint consisting of titanium and austenitic stainless steel metals is evidently a favourable selection for industrial applications employing the resistance spot welding (RSW) operation. Nevertheless, achieving a high-quality welded joint proved challenging owing to the properties of the diverse range of materials' used. To improve the quality of dissimilar welded joints, the welding parameters should be selected precisely. With that in mind, the current research endeavoured to figure out the ideal RSW parameters for a dissimilar resistance spot-welded joint between grade 2 titanium alloy (Ti) and AISI 304 austenitic stainless steel (ASS) with equal and unequal thicknesses of 0.5 and 1 mm. The RSW cases based on the selected thickness were referred to as the following: similar thickness of 1 mm for Ti and ASS as case E, dissimilar thickness of 0.5 mm for Ti and 1 mm for ASS as case F, dissimilar thickness of 0.5 mm for ASS and 1 mm for Ti as case I, and similar thickness of 0.5 mm as case J. Tensile shear force, failure mode, and micro-hardness were the metrics used to assess the dissimilar joint's soundness. The RSW variables utilized in the scope of the present investigation were "welding current, pressure, welding time, squeeze time, holding time, and pulse welding". Models from gradient boosting, CatBoost, and random forest machine learning (ML) algorithms were used to guarantee an accurate analysis, along with the artificial neural network regressions. Therefore, the ML and artificial neural network (ANN) models were trained using real data collected from 100 experimental RSW samples conducted under different RSW process parameters. Various transfer and training functions were applied with the multilayer perceptron employing the feed-forward-back propagation approach when building the ANN models. Also, for the first time in the RSW field, an estimation of the relative importance of the RSW variables regarding the predicted shear force is presented in the current study. Evaluating the experimental findings demonstrated that the highest shear force was 2.183, 2.589, 1.708, and 1.851 kN for cases E, F, I, and J. The micro-hardness data indicated that the nugget zone had a much higher hardness than the heat-affected zone (HAZ) and base metal (BM) zones, wherein case F showed the peak nugget hardness in comparison to the other cases. The best prediction model was found to be the ANN model when training the conjugate gradient with the Polak-Ribiere updates (Traincgp) training function with the hyperbolic tangent sigmoid transfer function (Tansig) with the mean squared error (MSE) and correlation coefficient (R
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.