Application of Artificial Neural Network (ANN) for Predicting the Wear Behaviour of Al 2219-SiC&lt;sub&gt;p&lt;/sub&gt; Composite

Radhakrishnan, Ganesh; Chandrasekaran, K.; Ramesh, Vignesh; Anandan, T

doi:10.4028/www.scientific.net/amm.852.397

Cited by 3 publications

(1 citation statement)

References 7 publications

(7 reference statements)

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“…Like polymers, the application of AI techniques on the metals and their composites can also be categorized into predictive modeling, design and optimization, and descriptive modeling. Unlike polymers, the mechanical properties of metals and their composites are attracting more attention [91][92][93][94][95][96][97][98][99][100][101][102] and a large number of literature focusing on the predictive modeling of mechanical properties of metals are given in Table 4.…”

Section: Applications Of Ai Techniques For Modeling Of Metals and Their Compositesmentioning

confidence: 99%

Artificial Intelligence in Materials Modeling and Design

Huang

Liew

Ademiloye

et al. 2020

Arch Computat Methods Eng

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In recent decades, the use of artificial intelligence (AI) techniques in the field of materials modeling has received significant attention owing to their excellent ability to analyze a vast amount of data and reveal correlations between several complex interrelated phenomena. In this review paper, we summarize recent advances in the applications of AI techniques for numerical modeling of different types of materials. AI techniques such as machine learning and deep learning show great advantages and potential for predicting important mechanical properties of materials and reveal how changes in certain principal parameters affect the overall behavior of engineering materials. Furthermore, in this review, we show that the application of AI techniques can significantly help to improve the design and optimize the properties of future advanced engineering materials. Finally, a perspective on the challenges and prospects of the applications of AI techniques for material modeling is presented. Highlights We present an up to date review of the application of artificial intelligence in materials modeling and design.  We comprehensively discuss past and recent applications in modeling and design of polymers, metals, ceramics and other materials. We identify current research focal points, challenges, and opportunities for the application of artificial intelligence in materials modeling and design.

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Section: Applications Of Ai Techniques For Modeling Of Metals and Their Compositesmentioning

confidence: 99%

Artificial Intelligence in Materials Modeling and Design

Huang

Liew

Ademiloye

et al. 2020

Arch Computat Methods Eng

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Triboinformatics: machine learning algorithms and data topology methods for tribology

Hasan

Nosonovsky

2022

Surface Innovations

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Friction and wear are very common phenomena found virtually everywhere. However, it is very difficult to predict the tribological (i.e., related to friction and wear) structure-properties relationships from the fundamental physical principles. Consequently, tribology remains a data-driven, mostly empirical discipline. With the advent of new Machine Learning (ML) and Artificial Intelligence (AI) methods, it becomes possible to establish new correlations in tribological data to better predict and control the tribological behavior of novel materials. Hence the new area of triboinformatics has emerged combining tribology with Data Science. We review ML algorithms used to establish correlations between the structure of metallic alloys and composite materials, tribological test conditions, friction, and wear. We also discuss novel methods of surface roughness analysis involving the concept of data topology in multi-dimensional data space, as applied to the macro- and nanoscale roughness. Other triboinformatic approaches are considered as well.

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Prediction of abrasive wear and surface hardness of printed parts by SLA technology

Muñoz-Valverde,

Villena-López,

Mayorga-Ases

et al. 2024

ings

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In the present study, a prediction of hardness deterioration and abrasive wear was performed through a neural network using artificial intelligence on a material printed in SLA. This article aims to predict the mechanical properties, wear resistance and surface hardness of parts manufactured by SLA stereolithography printing. A full factorial DOE was used to associate the peculiar parameters (print orientation, cure time, layer height) to perform experiments. The mechanical properties were evaluated according to ASTM regulations, with the objective of obtaining feeding data and validation of the predictions of the Taber Wear Index and hardness using an artificial neural network. The experimental results are in good agreement with the measured data with satisfactory prediction errors with a mean square error (MSE) of 0.01 corresponding to abrasive wear using the clear resin and a mean absolute error (MSE) of 0.09 with an R2 of 0.756, the prediction with the neural network with a mean square error (MSE) of 2.47 corresponding to abrasive wear using the tough resin and a mean absolute error (MSE) of 14.3 with an R2 of 0.97. It was shown that the accuracy of the prediction is reasonable, and the network has the potential to be improved if the experimental database for training the network could be expanded. Therefore, wear and hardness mechanical properties can be predicted appropriately with an ANN.

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Application of Artificial Neural Network (ANN) for Predicting the Wear Behaviour of Al 2219-SiC<sub>p</sub> Composite

Cited by 3 publications

References 7 publications

Artificial Intelligence in Materials Modeling and Design

Artificial Intelligence in Materials Modeling and Design

Triboinformatics: machine learning algorithms and data topology methods for tribology

Prediction of abrasive wear and surface hardness of printed parts by SLA technology

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