基于人工神经网络和多元线性回归的α氧化铝力学性能预测模型

IF 0.6 4区材料科学 Q4 MATERIALS SCIENCE, CERAMICS

Glass and Ceramics Pub Date : 2023-11-03 DOI:10.1007/s10717-023-00612-7

H. Belghalem, B. Fissah, M. Djeddou, M. Hamidouche

{"title":"基于人工神经网络和多元线性回归的α氧化铝力学性能预测模型","authors":"H. Belghalem, B. Fissah, M. Djeddou, M. Hamidouche","doi":"10.1007/s10717-023-00612-7","DOIUrl":null,"url":null,"abstract":"<div><div><p>In the present study, we built predictive models of the mechanical properties (Young’s modulus, fracture strength and toughness) of α-Al<sub>2</sub>O<sub>3</sub>. Experiments carried out on samples produced by spark plasma sintering (SPS). The experimental results were the basis for the evaluation of mathematical models and predictions by both the radial basis function neural network (RBFNN) and multiple linear regression (MLR) models. The results of the comparison of MLR and RBFNN models showed good agreement between the experimental data and the RBFNN model predictions whereas the MLR model reveals modest agreement with the studied mechanical properties.</p></div></div>","PeriodicalId":579,"journal":{"name":"Glass and Ceramics","volume":"80 7-8","pages":"347 - 354"},"PeriodicalIF":0.6000,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predictive Modeling of the Mechanical Properties of Alpha Alumina Using Artificial Neural Networks and Multiple Linear Regression\",\"authors\":\"H. Belghalem, B. Fissah, M. Djeddou, M. Hamidouche\",\"doi\":\"10.1007/s10717-023-00612-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div><p>In the present study, we built predictive models of the mechanical properties (Young’s modulus, fracture strength and toughness) of α-Al<sub>2</sub>O<sub>3</sub>. Experiments carried out on samples produced by spark plasma sintering (SPS). The experimental results were the basis for the evaluation of mathematical models and predictions by both the radial basis function neural network (RBFNN) and multiple linear regression (MLR) models. The results of the comparison of MLR and RBFNN models showed good agreement between the experimental data and the RBFNN model predictions whereas the MLR model reveals modest agreement with the studied mechanical properties.</p></div></div>\",\"PeriodicalId\":579,\"journal\":{\"name\":\"Glass and Ceramics\",\"volume\":\"80 7-8\",\"pages\":\"347 - 354\"},\"PeriodicalIF\":0.6000,\"publicationDate\":\"2023-11-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Glass and Ceramics\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://link.springer.com/article/10.1007/s10717-023-00612-7\",\"RegionNum\":4,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, CERAMICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Glass and Ceramics","FirstCategoryId":"88","ListUrlMain":"https://link.springer.com/article/10.1007/s10717-023-00612-7","RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, CERAMICS","Score":null,"Total":0}

引用次数: 0

摘要

在本研究中，我们建立了α-Al2O3力学性能(杨氏模量、断裂强度和韧性)的预测模型。对放电等离子烧结(SPS)试样进行了实验研究。实验结果为径向基函数神经网络(RBFNN)和多元线性回归(MLR)模型的数学模型评价和预测提供了基础。MLR和RBFNN模型的比较结果表明，实验数据与RBFNN模型的预测结果吻合较好，而MLR模型与研究的力学性能吻合较好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Predictive Modeling of the Mechanical Properties of Alpha Alumina Using Artificial Neural Networks and Multiple Linear Regression

查看原文本刊更多论文

Predictive Modeling of the Mechanical Properties of Alpha Alumina Using Artificial Neural Networks and Multiple Linear Regression

In the present study, we built predictive models of the mechanical properties (Young’s modulus, fracture strength and toughness) of α-Al₂O₃. Experiments carried out on samples produced by spark plasma sintering (SPS). The experimental results were the basis for the evaluation of mathematical models and predictions by both the radial basis function neural network (RBFNN) and multiple linear regression (MLR) models. The results of the comparison of MLR and RBFNN models showed good agreement between the experimental data and the RBFNN model predictions whereas the MLR model reveals modest agreement with the studied mechanical properties.

求助全文

通过发布文献求助，成功后即可免费获取论文全文。去求助

来源期刊

Glass and Ceramics 工程技术-材料科学：硅酸盐

CiteScore

1.00

自引率

16.70%

发文量

审稿时长

6-12 weeks

期刊介绍： Glass and Ceramics reports on advances in basic and applied research and plant production techniques in glass and ceramics. The journal''s broad coverage includes developments in the areas of silicate chemistry, mineralogy and metallurgy, crystal chemistry, solid state reactions, raw materials, phase equilibria, reaction kinetics, physicochemical analysis, physics of dielectrics, and refractories, among others.