Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network

In this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4...

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Main Authors: Varol Temel, Canakci Aykut, Ozsahin Sukru
Format: Article
Language:English
Published: De Gruyter 2014-06-01
Series:Science and Engineering of Composite Materials
Subjects:
Online Access:https://doi.org/10.1515/secm-2013-0148
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author Varol Temel
Canakci Aykut
Ozsahin Sukru
author_facet Varol Temel
Canakci Aykut
Ozsahin Sukru
author_sort Varol Temel
collection DOAJ
description In this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 μm), and milling times (0–10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.
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spelling doaj.art-177a611759b04a528d84ea99322f76ef2022-12-21T21:46:07ZengDe GruyterScience and Engineering of Composite Materials0792-12332191-03592014-06-0121341142010.1515/secm-2013-0148Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural networkVarol Temel0Canakci Aykut1Ozsahin Sukru2Engineering Faculty, Department of Metallurgical and Materials Engineering, Karadeniz Technical University, Trabzon 61000, TurkeyEngineering Faculty, Department of Metallurgical and Materials Engineering, Karadeniz Technical University, Trabzon 61000, TurkeyTechnology Faculty, Department of Woodworking Industry Engineering, Karadeniz Technical University, Trabzon 61000, TurkeyIn this study, an artificial neural network approach was employed to predict the effect of B4C size, B4C content, and milling time on the particle size and particle hardness of Al2024-B4C composite powders. Al2024-B4C powder mixtures with various reinforcement weight percentages (5%, 10%, and 20% B4C), reinforcement size (49 and 5 μm), and milling times (0–10 h) were prepared by mechanical alloying process. The properties of the composite powders were analyzed using a laser particle size analyzer for the particle size and a microhardness tester for the powder microhardness. The three input parameters in the proposed artificial neural network (ANN) were the reinforcement size, reinforcement ratio, and milling time. Particle size and particle hardness of the composite powders were the outputs obtained from the proposed ANN. The mean absolute percentage error for the predicted values did not exceed 4.289% for the best prediction model. This model can be used for predicting properties of Al2024-B4C composite powders produced with different reinforcement size, reinforcement ratio, and milling times.https://doi.org/10.1515/secm-2013-0148artificial neural networksmechanical alloyingmetal-matrix composites (mmcs)powder processing
spellingShingle Varol Temel
Canakci Aykut
Ozsahin Sukru
Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network
Science and Engineering of Composite Materials
artificial neural networks
mechanical alloying
metal-matrix composites (mmcs)
powder processing
title Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network
title_full Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network
title_fullStr Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network
title_full_unstemmed Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network
title_short Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network
title_sort prediction of the influence of processing parameters on synthesis of al2024 b4c composite powders in a planetary mill using an artificial neural network
topic artificial neural networks
mechanical alloying
metal-matrix composites (mmcs)
powder processing
url https://doi.org/10.1515/secm-2013-0148
work_keys_str_mv AT varoltemel predictionoftheinfluenceofprocessingparametersonsynthesisofal2024b4ccompositepowdersinaplanetarymillusinganartificialneuralnetwork
AT canakciaykut predictionoftheinfluenceofprocessingparametersonsynthesisofal2024b4ccompositepowdersinaplanetarymillusinganartificialneuralnetwork
AT ozsahinsukru predictionoftheinfluenceofprocessingparametersonsynthesisofal2024b4ccompositepowdersinaplanetarymillusinganartificialneuralnetwork