Comparative analysis of the properties of the nodular cast iron with carbides and the austempered ductile iron with use of the machine learning and the support vector machine

被引:40
|
作者
Wilk-Kolodziejczyk, Dorota [1 ,2 ]
Regulski, Krzysztof [1 ]
Gumienny, Grzegorz [3 ]
机构
[1] AGH Univ Sci & Technol, Krakow, Poland
[2] Foundry Res Inst, Krakow, Poland
[3] Lodz Univ Technol, Lodz, Poland
关键词
Austempered ductile iron (ADI); Nodular cast iron with carbides (NCIC); Cast iron; Data mining; Machine learning; Support vector machine; FAULT-DIAGNOSIS; SYSTEM; IDENTIFICATION; OPTIMIZATION; MODELS;
D O I
10.1007/s00170-016-8510-y
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The use of modern casting materials allows the achievement of higher product quality indices. The conducted experimental studies of new materials allow obtaining alloys with high performance properties while maintaining low production costs. Studies have shown that in certain areas of applications, the expensive to manufacture austempered ductile iron (ADI) can be replaced with ausferritic ductile iron or bainitic nodular cast iron with carbides, obtained without the heat treatment of castings. The dissemination of experimental results is possible through the use of information technologies and building applications that automatically compare the properties of materials, as the machine learning tools in comparative analysis of the properties of materials, in particular ADI and nodular cast iron with carbides.
引用
收藏
页码:1077 / 1093
页数:17
相关论文
共 50 条
  • [31] Comparative analysis of different machine learning algorithms to predict mechanical properties of concrete
    Koya, Bhanu P.
    Aneja, Sakshi
    Gupta, Rishi
    Valeo, Caterina
    MECHANICS OF ADVANCED MATERIALS AND STRUCTURES, 2022, 29 (25) : 4032 - 4043
  • [32] Analysis of the Physical-Mechanical Properties of the Zinc Phosphate Layer Deposited on a Nodular Cast Iron Substrate
    Nejneru, Carmen
    Burduhos-Nergis, Diana-Petronela
    Axinte, Mihai
    Perju, Manuela Cristina
    Bejinariu, Costica
    COATINGS, 2022, 12 (10)
  • [33] Prediction of Zeta Potential of Decomposed Peat via Machine Learning: Comparative Study of Support Vector Machine and Artificial Neural Networks
    Li, Hao
    Chen, Fudi
    Cheng, Kewei
    Zhao, Zheze
    Yang, Dazuo
    INTERNATIONAL JOURNAL OF ELECTROCHEMICAL SCIENCE, 2015, 10 (08): : 6044 - 6056
  • [34] Enhancement of Properties of Concrete by Comparative Analysis of Machine Learning Models
    Mohit
    Balwinder, L.
    PROCEEDINGS OF THE 2ND INTERNATIONAL CONFERENCE ON TRENDS IN ARCHITECTURE AND CONSTRUCTION, ICTAC-2024, 2025, 527 : 1185 - 1193
  • [35] Comparative study of support vector machines and random forests machine learning algorithms on credit operation
    Teles, Germanno
    Rodrigues, Joel J. P. C.
    Rabelo, Ricardo A. L.
    Kozlov, Sergei A.
    SOFTWARE-PRACTICE & EXPERIENCE, 2021, 51 (12) : 2492 - 2500
  • [36] A Comprehensive Comparative Study of Machine Learning Methods for Chronic Kidney Disease Classification: Decision Tree, Support Vector Machine, and Naive Bayes
    Syarif, Admi
    Riana, Olivia Desti
    Shofiana, Dewi Asiah
    Junaidi, Akmal
    INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS, 2023, 14 (10) : 597 - 603
  • [37] The Application and Analysis of Stock Forecasting Methods based on Support Vector Machine and Deep Learning
    Yuan, Haoting
    2ND INTERNATIONAL CONFERENCE ON APPLIED MATHEMATICS, MODELLING, AND INTELLIGENT COMPUTING (CAMMIC 2022), 2022, 12259
  • [38] Quantum support vector machine for forecasting house energy consumption: a comparative study with deep learning models
    Kumar, Karan K.
    Nutakki, Mounica
    Koduru, Suprabhath
    Mandava, Srihari
    JOURNAL OF CLOUD COMPUTING-ADVANCES SYSTEMS AND APPLICATIONS, 2024, 13 (01):
  • [39] The Comparative Analysis of GABP Neural Network and Support Vector Machine in Real Estate Investment Risk
    Shi, Zhenwu
    Huang, Xue
    PROCEEDINGS OF 2012 INTERNATIONAL CONFERENCE ON CONSTRUCTION & REAL ESTATE MANAGEMENT, VOLS 1 AND 2, 2012, : 660 - 663
  • [40] Metabolic detection of malignant brain gliomas through plasma lipidomic analysis and support vector machine-based machine learning
    Zhou, Juntuo
    Ji, Nan
    Wang, Guangxi
    Zhang, Yang
    Song, Huajie
    Yuan, Yuyao
    Yang, Chunyuan
    Jin, Yan
    Zhang, Zhe
    Zhang, Liwei
    Yin, Yuxin
    EBIOMEDICINE, 2022, 81