Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Myśliwiec, Piotr; Kubit, Andrzej; Szawara, Paulina

doi:10.3390/ma17071452

Cited by 3 publications

(1 citation statement)

References 44 publications

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“…52 To prevent overfitting and ensure model generalizability, techniques such as early stopping, regularization, and cross-validation were employed during the training process. 53 Preprocessing steps were taken to normalize the input variables. Specifically, min-max normalization was applied to rescale the feature values to a common range, between 0 and 1.…”

Section: Methodsmentioning

confidence: 99%

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

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Dementia diagnosis often relies on expensive and invasive neuroimaging techniques that limit access to early screening. This study proposes an innovative approach for facilitating early dementia screening by estimating diffusion tensor imaging (DTI) measures using accessible lifestyle and brain imaging factors. Conventional DTI analysis, though effective, is often hindered by high costs and limited accessibility. To address this challenge, fuzzy subtractive clustering identified 14 influential variables from the Lifestyle for Brain Health and Brain Atrophy and Lesion Index frameworks, encompassing demographics, medical conditions, lifestyle factors, and structural brain markers. A multilayer perceptron (MLP) neural network was developed using these selected variables to predict fractional anisotropy (FA), a DTI metric reflecting white matter integrity and cognitive function. The MLP model achieved promising results, with a mean squared error of 0.000 878 on the test set for FA prediction, demonstrating its potential for accurate DTI estimation without costly neuroimaging techniques. The FA values in the dataset ranged from 0 to 1, with higher values indicating greater white matter integrity. Thus, a mean squared error of 0.000 878 suggests that the model’s predictions were highly accurate compared to the observed FA values. This multifactorial approach aligns with the current understanding of dementia’s complex etiology influenced by various biological, environmental, and lifestyle factors. By integrating readily available data into a predictive model, this method enables widespread, cost-effective screening for early dementia risk assessment. The proposed accessible screening tool could facilitate timely interventions, preventive strategies, and efficient resource allocation in public health programs, ultimately improving patient outcomes and caregiver burden.

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Section: Methodsmentioning

confidence: 99%

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Akbarifar,

Maghsoudpour,

Mohammadian

et al. 2024

AIP Advances

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Wear parametric Optimization of FSW parameters on Al Alloy Using MLP technique

Cheepurupalli,

Selvaraj,

Reddy Muthadi

et al. 2024

E3S Web Conf.

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The purpose of this study was to identify the suitable Friction Stir Welding (FSW) parameters that would be used for welding scrapped Al alloy plates. On the experimental side, the study used four Factor Three-Level Full Factorial Design of Experiments (DoE) approach. Some of the input parameters incorporated in the analysis were the applied load, the sliding speed, displacement and weight percentage of Al2O3 reinforcement, and some of the output parameters were the specific wear rate and the coefficient of friction. The above said optimum parameters were established using the Minitab software while the above said experimental results was estimated using multilayer perceptron of the feed forward 4–10–1 network. For the actual test data set in the given experiment, the overall performance of the MLP predictions resulted to an R2. This results to a coefficient of determination (R R2 of 0.98474 and a mean squared error (MSE) of 0. 025075. Therefore the high R values, which are near to 1, show that the actual values mean and the predicted values mean are closely matched.

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Increasing Exploitation Durability of Two-Layer Cast Mill Rolls and Assessment of the Applicability of the XGBoost Machine Learning Method to Manage Their Quality

Vlasenko,

Glowacki,

Vlasovets

et al. 2024

Materials

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The increase in exploitation durability of two-layer cast rolls with the working layer made of high chromium cast iron allows one to significantly improve the quality of rolled metal as well as to increase the economic efficiency of the manufacturing process. However, it is severely hindered due to the massiveness of castings, the impossibility of both evaluating mechanical properties along the depth of the working layer, and providing the structural uniformity of the working surface and the decrease in stresses. In our research, aiming to enhance the exploitation durability of sheet rolls, it is recommended to achieve structural uniformity by CuMg alloying, which increases the concentration of copper up to 2.78 wt.% in certain zones and, owing to the accelerated austenite decomposition at a high temperature during the cool-down of the castings, led to the reduction in excessive strength and the level of heat stresses in the castings. We propose the regimes of cyclic heat treatments which, due to the decomposition of retained austenite and the fragmentation of structure, control the level of hardness to reduce and uniformize the level of stresses along the length of a barrel. A further improvement in the predictions of exploitation durability using XGboost method, which was performed based on the chemical composition of the working layer of high-chromium cast iron and heat treatment parameters, requires taking into account the factors characterizing exploitation conditions of specific rolling mills and the transformations of structural-phase state of the surface obtained by a non-destructive control method. As the controlled parameter, the hardness measured on the roll’s surface is recommended, while the gradient change in mechanical properties along the working layer depth can be feasibly analyzed by a magnetic method of coercive force measuring.

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Optimization of 2024-T3 Aluminum Alloy Friction Stir Welding Using Random Forest, XGBoost, and MLP Machine Learning Techniques

Cited by 3 publications

References 44 publications

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Multimodal dementia identification using lifestyle and brain lesions, a machine learning approach

Wear parametric Optimization of FSW parameters on Al Alloy Using MLP technique

Increasing Exploitation Durability of Two-Layer Cast Mill Rolls and Assessment of the Applicability of the XGBoost Machine Learning Method to Manage Their Quality

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