Prediction of etching rate of alumino-silicate glass by RSM and ANN

被引：0
|
作者：
Ting, H. T. [1 ]
Abou-El-Hossein, K. A. [2 ]
Chua, H. B. [1 ]
机构：
[1] Curtin Univ Technol, Dept Mech Engn, Miri, Sarawak 68006, Australia
[2] Nelson Mandela Metropolitan Univ, Dept Mechatron, ZA-6031 Port Elizabeth, South Africa
来源：
JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH | 2009年 / 68卷 / 11期
关键词：
Alumino-silicate glass; ANN; Etching rate; RSM; SURFACE METHODOLOGY RSM; NEURAL-NETWORK; OPTIMIZATION; WAFER;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
In this study, response surface methodology (RSM) and artificial neural network (ANN) were applied to predict material removal rate in chemical etching process of alumino-silicate glass (SiO2 57/Al2O3 36/CaO/MgO/BaO). 2(k) Factorial design was performed to evaluate linearity condition among process parameters. Analysis of variance (ANOVA) was performed and quadratic model was found most significant for data values of process parameters. New models were able to predict etching rate of alumino-silicate glass, with a great confidence. Input parameters analyzed were temperature, etching period and type of setup with and without-condensation.
引用
收藏
页码：920 / 924
页数：5
相关论文
共 50 条
  • [41] Modeling and Precise Prediction of Thermophysical Attributes of Water/EG Blend-Based CNT Nanofluids by NSGA-II Using ANN and RSM
    Hemmat Esfe, Mohammad
    Alidoust, Soheyl
    ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING, 2021, 46 (07) : 6423 - 6437
  • [42] Development of binary models for prediction and optimization of nutritional values of enriched kokoro: a case of response surface methodology (RSM) and artificial neural network (ANN)
    Adeoye, Babatunde Kazeem
    Ajala, Olajide Olukayode
    Oke, Emmanuel Olusola
    CHEMICAL PRODUCT AND PROCESS MODELING, 2023, 18 (02): : 313 - 324
  • [43] Modeling and Precise Prediction of Thermophysical Attributes of Water/EG Blend-Based CNT Nanofluids by NSGA-II Using ANN and RSM
    Mohammad Hemmat Esfe
    Soheyl Alidoust
    Arabian Journal for Science and Engineering, 2021, 46 : 6423 - 6437
  • [44] Analysis and Prediction of End Milling Characteristics of Al-SiCp Metal Matrix Composite Using RSM and ANN
    Jayakumar, K.
    Mathew, Jose
    Joseph, M. A.
    JOURNAL FOR MANUFACTURING SCIENCE AND PRODUCTION, 2012, 12 (02) : 105 - 110
  • [45] Prediction of machining performance using RSM and ANN models in hard turning of martensitic stainless steel AISI 420
    Zerti, Abderrahmen
    Yallese, Mohamed Athmane
    Zerti, Oussama
    Nouioua, Mourad
    Khettabi, Riad
    PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE, 2019, 233 (13) : 4439 - 4462
  • [46] Comparative study of flexural properties prediction of Washingtonia filifera rachis biochar bio-mortar by ANN and RSM models
    Boumaaza, Messaouda
    Belaadi, Ahmed
    Bourchak, Mostefa
    Jawaid, Mohammad
    Hamid, Satha
    CONSTRUCTION AND BUILDING MATERIALS, 2022, 318
  • [47] Comparison of artificial neural network (ANN) and response surface methodology (RSM) prediction in compressive strength of recycled concrete aggregates
    Hammoudi, Abdelkader
    Moussaceb, Karim
    Belebchouche, Cherif
    Dahmoune, Farid
    CONSTRUCTION AND BUILDING MATERIALS, 2019, 209 : 425 - 436
  • [48] Proportional impact prediction model of animal waste fat-derived biodiesel by ANN and RSM technique for diesel engine
    Simsek, Suleyman
    Uslu, Samet
    Simsek, Hatice
    ENERGY, 2022, 239
  • [49] Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX
    Manoj, I., V
    Soni, Hargovind
    Narendranath, S.
    Mashinini, P. M.
    Kara, Fuat
    ADVANCES IN MATERIALS SCIENCE AND ENGINEERING, 2022, 2022
  • [50] RSM, SVM and ANN modeling of the properties of self-compacting concrete with natural mordenite-rich tuff and recycled glass
    M. A. Bouzidi
    A. Bouziane
    N. Bouzidi
    Asian Journal of Civil Engineering, 2025, 26 (1) : 89 - 106
12345