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Analysis and prediction of the tensile strength of aluminum alloy composite using statistical and artificial neural network technique
Engineering Research Express Pub Date : 2021-01-02 , DOI: 10.1088/2631-8695/abd4f1
Mohammad Mohsin 1 , Mohammad Aatif Qazi 2 , Mohd. Suhaib 1 , Mohd Bilal Naim Shaikh 2 , Mohd Misbah 2
Affiliation  

In this research work, aluminum alloy (Al-20Fe-5Cr) matrix-based aluminum oxide (Al2O3) reinforced composites were developed through the powder metallurgy (P/M) process. Effect of compaction pressure (200, 250 & 300 MPa) and wt.% of Al2O3 (0, 10, 20 & 30 wt.) on tensile strength and percentage elongation has been analyzed through statistical and artificial neural network techniques (ANN). The mixture of Al-alloy powder particles and Al2O3 particles were synthesized in a centrifugal ball mill for 20 min. Compaction of synthesized powder was carried in the standard tensile die using a uniaxial hydraulic pressing machine. Sintering was performed at temperature 58020 C for one hour in an argon gas environment using an electric tubular furnace. It was found that tensile strength enhanced significantly with the addition of Al2O3up to 20 wt.% and then declined sharply for the 30 wt.% of Al2O3at all compaction pressures. The highest tensile strengths were found for each wt.% of Al2O3 at compaction pressure 300 MPa compare to other compaction pressures. Tensile strength increased from 105 to 158 MPa with the addition of 20 wt.% Al2O3 and decreased to 142 MPa for 30 wt.% at 300 MPa compaction pressure. The improvement resulted from better compaction, leading to more plastic deformation, better packing, and high effective contact area. However, the percentage of elongation decreased from 23.2% to 2.2% with an increment of wt.% of Al2O3 for compaction pressure 200 MPa, while for 300 MPa, its value drops from 25.8% to 6.5%. This depreciation can be reasoned for the reduction in ductile matrix content and dilute flowability of the Al matrix, which occurred due to brittle Al2O3. The statistical analysis using ANOVA revealed that the compaction pressure is the primary control factor influencing tensile strength by 90.3%. The feed-forward network with a back-propagating gradient-descent error minimization training approach and mean squared error (MSE) as performance function was employed to model and predict tensile strength. The developed 3-layered multilayer perceptron (MLP) with 2–10–2 network architecture established a correlation between the inputs and outputs with minimum error (MSE) below 1% and maximum correlation coefficient (R) close to 1.



中文翻译:

基于统计和人工神经网络技术的铝合金复合材料拉伸强度分析与预测

在这项研究工作中,通过粉末冶金(P / M)工艺开发了铝合金(Al-20Fe-5Cr)基基氧化铝(Al 2 O 3)增强复合材料。通过统计和人工神经网络技术(ANN)分析了压制压力(200、250和300 MPa)和wt。%的Al 2 O 3(0、10、20和30 wt。)对拉伸强度和伸长率的影响)。铝合金粉末颗粒与Al 2 O 3的混合物颗粒在离心球磨机中合成20分钟。使用单轴液压机将合成粉末压实在标准拉伸模具中。使用电管式炉在氩气环境中在温度58020℃下烧结1小时。已经发现,当Al 2 O 3的添加量达到20wt。%时,抗拉强度显着提高,然后在所有压实压力下,对于30wt。%的Al 2 O 3,抗拉强度急剧下降。发现每重量%的Al 2 O 3最高的拉伸强度在压实压力为300 MPa时与其他压实压力相比。加入20 wt。%的Al 2 O 3,拉伸强度从105 MPa增加到158 MPa,在300 MPa的压实压力下,拉伸强度从30 wt。%降低到142 MPa。改善来自更好的压实,导致更大的塑性变形,更好的包装和高有效接触面积。然而,当压制压力为200 MPa时,随着Al 2 O 3的重量%的增加,伸长率从23.2%降低至2.2%,而对于300 MPa,其值从25.8%降至6.5%。这种折旧可以归因于由于脆性的Al 2 O 3而导致的延展性基体含量降低和Al基体稀释流动性降低。。使用ANOVA进行的统计分析表明,压实压力是影响拉伸强度90.3%的主要控制因素。使用具有反向传播梯度下降误差最小化训练方法和均方误差(MSE)作为性能函数的前馈网络来建模和预测拉伸强度。已开发的具有2–10–2网络结构的3层多层感知器(MLP)在输入和输出之间建立了相关,最小误差(MSE)低于1%,最大相关系数(R)接近1。

更新日期:2021-01-02
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