PREDICTION OF HARDNESS, FLEXURAL STRENGTH, AND FRACTURE TOUGHNESS OF ZRO2 BASED CERAMICS USING ENSEMBLE LEARNING ALGORITHMS

IF 1.1 Q3 METALLURGY & METALLURGICAL ENGINEERING Acta Metallurgica Slovaca Pub Date : 2023-06-20 DOI:10.36547/ams.29.2.1819
V. Kulyk, I. Izonin, V. Vavrukh, R. Tkachenko, Z. Duriagina, B. Vasyliv, M. Kováčová
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Abstract

Flexural strength, hardness, and fracture toughness are the basic mechanical properties of ceramic materials. Manufacturers widely use this set of properties to ensure the durability of ceramic products. However, many factors, such as chemical and phase compositions, sintering temperature, average grain size, density, and others, affect these properties, making it challenging to estimate corresponding reliability parameters correctly. Experimental examination of the impact of these factors on the mechanical properties of ceramics is a rather time-consuming and resource-consuming procedure. This work aims to predict the mechanical properties of zirconia ceramics using machine learning tools. The authors have created an experimental database for predicting the hardness, flexural strength, and fracture toughness of ZrO2-based ceramics based on chemical composition, phase composition, microstructural features, and sintering temperature on the mechanical properties of zirconia ceramics. The authors compare compared the effectiveness of using different machine learning algorithms and have found a high accuracy of the predicted values of each of the three mechanical properties using boosting ensemble methods. Also they  developed a stacked ensemble of machine learning methods to improve the accuracy of determining the hardness property prediction task. We obtained the increase in accuracy of more than 10% (R2) using our approach.
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用集成学习算法预测ZRO2基陶瓷的硬度、弯曲强度和断裂韧性
弯曲强度、硬度和断裂韧性是陶瓷材料的基本力学性能。制造商广泛使用这套性能来确保陶瓷产品的耐用性。然而,许多因素,如化学成分和相组成、烧结温度、平均晶粒度、密度等,都会影响这些性能,因此正确估计相应的可靠性参数具有挑战性。实验研究这些因素对陶瓷力学性能的影响是一个相当耗时和耗费资源的过程。本工作旨在使用机器学习工具预测氧化锆陶瓷的力学性能。作者创建了一个实验数据库,用于根据化学成分、相组成、微观结构特征和烧结温度对氧化锆陶瓷力学性能的影响来预测ZrO2基陶瓷的硬度、弯曲强度和断裂韧性。作者比较了使用不同机器学习算法的有效性,并发现使用boosting集成方法对三种机械特性的预测值都有很高的准确性。他们还开发了一套堆叠的机器学习方法,以提高确定硬度特性预测任务的准确性。使用我们的方法,我们获得了超过10%(R2)的准确性提高。
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来源期刊
Acta Metallurgica Slovaca
Acta Metallurgica Slovaca METALLURGY & METALLURGICAL ENGINEERING-
CiteScore
2.00
自引率
30.00%
发文量
22
审稿时长
12 weeks
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