Artificial Neural Networks for Producing a Low-Cost Austempered Ductile Iron

被引：0
|
作者：
Hofmam, Diogo [1 ]
Ramos, Fabiano Dornelles [2 ,3 ]
Lemos, Guilherme Vieira Braga [3 ,4 ]
de Lima Lessa, Cleber Rodrigo [1 ,2 ]
机构：
[1] Inst Fed Rio Grande do Sul IFRS, Programa Posgrad Tecnol & Engn Mat, Caxias Do Sul, RS, Brazil
[2] Inst Fed Rio Grande do Sul IFRS, Caxias Do Sul, RS, Brazil
[3] Univ Fed Rio Grande do Sul UFRGS, Programa Posgrad Engn Min Met & Mat PPGE3M, Porto Alegre, RS, Brazil
[4] Univ Fed Santa Maria UFSM, Cachoeira Do Sul, RS, Brazil
来源：
MATERIALS RESEARCH-IBERO-AMERICAN JOURNAL OF MATERIALS | 2022年 / 25卷
关键词：
Austempered ductile iron; Artificial neural network; Mechanical properties; Cost-savings; CAST-IRON; MECHANICAL-PROPERTIES; FRACTURE-TOUGHNESS; HEAT-TREATMENT; ADI; PREDICTION; AUSTENITE; STRENGTH; MICROSTRUCTURE;
D O I：
10.1590/1980-5373-MR-2022-0336
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
Two artificial neural networks (ANNs) were developed for producing an austempered ductile iron (ADI) with low-cost chemical composition and mechanical properties as per ASTMA897/897M-16-grade-1050/750/07 standard. Thus, the first ANN predicted the chemical composition range within the lowest cost and required mechanical properties. Next, in the second ANN, the resulting values from the first ANN were refined considering the target chemical composition suggested in the standard. Moreover, mechanical properties and microstructural analyses were undertaken in the ADI produced to support the ANNs' findings. Hence, ANNs can be used to make a standard-compliant ADI and achieve cost savings.
引用
收藏
页数：6
相关论文
共 50 条
  • [1] Comparative Characteristics of Ductile Iron and Austempered Ductile Iron Modeled by Neural Network
    Savkovic, Borislav
    Kovac, Pavel
    Dudic, Branislav
    Gregus, Michal
    Rodic, Dragan
    Strbac, Branko
    Ducic, Nedeljko
    [J]. MATERIALS, 2019, 12 (18)
  • [2] Artificial Neural Network-Based Modeling for Prediction of Hardness of Austempered Ductile Iron
    Savangouder, Ravindra, V
    Patra, Jagdish C.
    Bornand, Cedric
    [J]. NEURAL INFORMATION PROCESSING, ICONIP 2019, PT V, 2019, 1143 : 405 - 413
  • [3] Prediction of ductile cast iron quality by artificial neural networks
    Perzyk, M
    Kochanski, AW
    [J]. JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, 2001, 109 (03) : 305 - 307
  • [4] HARDNESS OF DUCTILE CAST IRON ESTIMATED BY ARTIFICIAL NEURAL NETWORKS
    Zmak, Irena
    Filetin, Tomislav
    Hren, Smiljan
    [J]. ANNALS OF DAAAM FOR 2009 & PROCEEDINGS OF THE 20TH INTERNATIONAL DAAAM SYMPOSIUM, 2009, 20 : 1401 - 1402
  • [5] NEURAL NETWORK ANALYSIS OF TENSILE PROPERTIES OF AUSTEMPERED DUCTILE IRON
    Lawrynowicz, Z.
    Dymski, S.
    [J]. ADVANCES IN MATERIALS SCIENCE, 2008, 8 (01): : 94 - 104
  • [6] A practical low-cost model for prediction of the groundwater quality using artificial neural networks
    Heidarzadeh, Nima
    [J]. JOURNAL OF WATER SUPPLY RESEARCH AND TECHNOLOGY-AQUA, 2017, 66 (02): : 86 - 95
  • [7] A Low-Cost Fault-Tolerant Approach for Hardware Implementation of Artificial Neural Networks
    Ahmadi, A.
    Sargolzaie, M. H.
    Fakhraie, S. M.
    Lucas, C.
    Vakili, Sh.
    [J]. 2009 INTERNATIONAL CONFERENCE ON COMPUTER ENGINEERING AND TECHNOLOGY, VOL II, PROCEEDINGS, 2009, : 93 - 97
  • [8] Forecasting low-cost housing demand in Pahang, Malaysia using Artificial Neural Networks
    Zainun, Noor Yasmin
    Rahman, Ismail Abdul
    Eftekhari, Mahroo
    [J]. INTERNATIONAL JOURNAL OF SUSTAINABLE CONSTRUCTION ENGINEERING AND TECHNOLOGY, 2011, 2 (01): : 83 - 88
  • [9] A low-cost and portable spectrometer for measuring light spectrum using artificial neural networks
    Amirazar, Armin
    Azarbayjani, Mona
    Molavi, Maziyar
    Karami, Morteza
    [J]. ENERGY AND BUILDINGS, 2021, 252
  • [10] Dry sliding wear of low alloyed austempered ductile iron
    Bosniak, B
    Verlinden, B
    Radulovic, B
    [J]. MATERIALS SCIENCE AND TECHNOLOGY, 2003, 19 (05) : 650 - 656
12345