Machine learning reinforced microstructure-sensitive prediction of material property closures

Hasan, Masud; Acar, Pınar

doi:10.1016/j.commatsci.2021.110930

Cited by 14 publications

(2 citation statements)

References 35 publications

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“…Machine Learning Task Chan et al (2022) [4] Material Prediction & Design Kadulkar et al (2022) [10] Prediction of Material properties Huang et al (2023) [9] Material Property Prediction Qin et al (2023) [13] Prediction of lattice conductivity Zhao et al (2023) [19] Classification & Segmentation of material Hasan and Acar (2022) [8] Material Property Prediction Bagherzadeh & Shafighfard (2022) [2] Evaluating material characterization Bello et al (2023) [3] Material screening…”

Section: Table 1 Applications Of Machine Learning In Materials Orient...mentioning

confidence: 99%

Integration of Blockchain & Machine Learning Approach in Sustainable Material Selection

2024

jmc

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Sustainable materials with special focus on environment are the need of the hour. The alarming signs of global warming demands the emerge of sustainable industries with the goal of producing products using sustainable materials. The process of drawing optimal decisions on sustainable materials is highly intricated and henceforth this research work proposes a comprehensive and integrated approach of decision-making. Block chain together with Random Forest algorithm is leveraged in determining the sustainable material selection. The combined approach resolves the challenges of managing data integrity in obtaining precise results. On comparing the results using performance metrics, it is evident that the proposed approach facilitates optimum decision making of sustainable materials to a greater extent. This integrated approach shall be applied to several decision-making scenarios customizing the parameters to the need of the industrial problems.

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Section: Table 1 Applications Of Machine Learning In Materials Orient...mentioning

confidence: 99%

Integration of Blockchain & Machine Learning Approach in Sustainable Material Selection

2024

jmc

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“…A significant problem when using this type of solution is the amount of data. The use of artificial intelligence or machine learning as well as data analysis to develop predictive models to determine the mechanical Materials 2022, 15, 8254 2 of 16 properties of products is more and more commonly described in the literature [1][2][3][4][5][6]. For example, a multi-task learning algorithm with augmentation data preprocessing dealt with the small imbalanced data and multi-target predictions.…”

Section: Introductionmentioning

confidence: 99%

Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods

et al. 2022

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This paper presents an assessment of the possibility of using digital image classifiers for tomographic images concerning ductile iron castings. The results of this work can help the development of an efficient system suggestion allowing for decision making regarding the qualitative assessment of the casting process parameters. Special attention should be focused on the fact that automatic classification in the case of ductile iron castings is difficult to perform. The biggest problem in this aspect is the high similarity of the void image, which may be a sign of a defect, and the nodular graphite image. Depending on the parameters, the tests on different photos may look similar. Presented in this article are test scenarios of the module analyzing two-dimensional tomographic images focused on the comprehensive assessment by convolutional neural network models, which are designed to classify the provided image. For the purposes of the tests, three such models were created, different from each other in terms of architecture and the number of hyperparameters and trainable parameters. The described study is a part of the decision-making system, supporting the process of qualitative analysis of the obtained cast iron castings.

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Advances in Modeling and Control of Magnetorheological Elastomers for Engineering Applications

Dhiman,

Salodkar,

Gagandeep

et al. 2023

Arch Computat Methods Eng

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Machine learning reinforced microstructure-sensitive prediction of material property closures

Cited by 14 publications

References 35 publications

Integration of Blockchain & Machine Learning Approach in Sustainable Material Selection

Integration of Blockchain & Machine Learning Approach in Sustainable Material Selection

Development of a CT Image Analysis Model for Cast Iron Products Based on Artificial Intelligence Methods

Advances in Modeling and Control of Magnetorheological Elastomers for Engineering Applications

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