An Informatic Approach to Predict the Mechanical Properties of Aluminum Alloys using Machine Learning Techniques

Devi, M Aruna; Prakash, Chander; Chinnannavar, Rahul Pandappa; Joshi, Venugopal Prasanna; Palada, Rohit Shankar; Dixit, Ravut

doi:10.1109/icosec49089.2020.9215277

Cited by 18 publications

(5 citation statements)

References 13 publications

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“…The authors also conceded from their overall benchmarking results that the use of PLMs alone to create domain-specific knowledge graphs is still far from being practical and requires the development of better-informed PLMs for specific materials design tasks. The group of Olivetti used LLMs to generate knowledge graphs (MatKG2) for the entire domain of materials science, taking ontological information into account as opposed to using statistical co-occurrence alone [141]. Zhao et al [142] used fine-tuned bidirectional encoder representations from a transformer (BERT) model and tested it with respect to data extraction from published corpora.…”

Section: Statusmentioning

confidence: 99%

Roadmap on data-centric materials science

Bauer,

Benner,

Bereau

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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Science is and always has been based on data, but the terms ‘data-centric’ and the ‘4th paradigm of’ materials research indicate a radical change in how information is retrieved, handled and research is performed. It signifies a transformative shift towards managing vast data collections, digital repositories, and innovative data analytics methods. The integration of Artificial Intelligence (AI) and its subset Machine Learning (ML), has become pivotal in addressing all these challenges. This Roadmap on Data-Centric Materials Science explores fundamental concepts and methodologies, illustrating diverse applications in electronic-structure theory, soft matter theory, microstructure research and experimental techniques like photoemission, and electron microscopy.While the roadmap delves into specific areas within the broad interdisciplinary field of materials science, the provided examples elucidate key concepts applicable to a wider range of topics. The discussed instances offer insights into addressing the multifaceted challenges encountered in contemporary materials research.

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Section: Statusmentioning

confidence: 99%

Roadmap on data-centric materials science

Bauer,

Benner,

Bereau

et al. 2024

Modelling Simul. Mater. Sci. Eng.

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“…To generate a ML model, a number of ML algorithms may be considered, which are proprietary for a particular type of data set and type of prediction. The mechanical characteristics of several Al alloys were predicted using various ML algorithms (Devi et al, 2020). Those algorithms are used to create models by using a large data set that is separated into subsets for training, validation and testing.…”

Section: Phenomenologymentioning

confidence: 99%

Section: Phenomenologymentioning

confidence: 99%

In pursuit of a suitable machine learning algorithm for hardness prediction of aluminium alloy

Chhabri,

Hazra,

Choudhury

et al. 2023

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PurposeBecause of the mechanical properties of aluminium (Al), an accurate prediction of its properties has been challenging. Researchers are seeking reliable models for predicting the mechanical strength of Al alloys owing to the continuous emergence of new Al alloys and their applications. There has been widespread use of empirical and statistical models for the prediction of different mechanical properties of Al and Al alloy, such as linear and nonlinear regression. Nevertheless, the development of these models requires laborious experimental work, and they may not produce accurate results depending on the relationship between the Al properties, mix of other compositions and curing conditions.Design/methodology/approachNumerous machine learning (ML) models have been proposed as alternative approaches for predicting the strengths of Al and its alloys. The hardness of Al alloys has been predicted by implementing various ML algorithms, such as linear regression, ridge regression, lasso regression and artificial neural network (ANN). This investigation critically analysed and discussed the application and performance of models generated by linear regression, ridge regression, lasso regression and ANN algorithms using different mechanical properties as training parameters.FindingsConsidering the definition of the problem, linear regression has been found to be the most suitable algorithm in predicting the hardness values of AA7XXX alloys as the model generated by it best fits the data set.Originality/valueThe work presented in this paper is original and not submitted anywhere else.

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“…Devi et al [15] compared multiple ML models for predicting aluminum alloys' tensile strength and hardness, demonstrating the efficacy of algorithms like K-nearest neighbors (KNN) and artificial neural networks (ANN) in this domain. Similarly, Xu et al [16] employed ANN and support vector machine (SVM) models to predict properties in magnesium alloys, showcasing the potential for relating composition, processing, and mechanical properties.…”

Section: Introductionmentioning

confidence: 99%

“…A thorough review of the literature [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] above demonstrates that various ML algorithms such as LR, SVM/SVR, RF, ANN, KNN, and GPR are effective at making predictions. Notably, some studies have also explored ensemble techniques such as XGBoost, AdaBoost, and SL to enhance predictive accuracy.…”

Section: Introductionmentioning

confidence: 99%

Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning

Risal,

Singh,

Yao

et al. 2024

Materials

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The scarcity of high-quality data presents a major challenge to the prediction of material properties using machine learning (ML) models. Obtaining material property data from experiments is economically cost-prohibitive, if not impossible. In this work, we address this challenge by generating an extensive material property dataset comprising thousands of data points pertaining to the elastic properties of Fe-C alloys. The data were generated using molecular dynamic (MD) calculations utilizing reference-free Modified embedded atom method (RF-MEAM) interatomic potential. This potential was developed by fitting atomic structure-dependent energies, forces, and stress tensors evaluated at ground state and finite temperatures using ab-initio. Various ML algorithms were subsequently trained and deployed to predict elastic properties. In addition to individual algorithms, super learner (SL), an ensemble ML technique, was incorporated to refine predictions further. The input parameters comprised the alloy’s composition, crystal structure, interstitial sites, lattice parameters, and temperature. The target properties were the bulk modulus and shear modulus. Two distinct prediction approaches were undertaken: employing individual models for each property prediction and simultaneously predicting both properties using a single integrated model, enabling a comparative analysis. The efficiency of these models was assessed through rigorous evaluation using a range of accuracy metrics. This work showcases the synergistic power of MD simulations and ML techniques for accelerating the prediction of elastic properties in alloys.

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An Informatic Approach to Predict the Mechanical Properties of Aluminum Alloys using Machine Learning Techniques

Cited by 18 publications

References 13 publications

Roadmap on data-centric materials science

Roadmap on data-centric materials science

In pursuit of a suitable machine learning algorithm for hardness prediction of aluminium alloy

Accelerating Elastic Property Prediction in Fe-C Alloys through Coupling of Molecular Dynamics and Machine Learning

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