Comparison of the Technological Time Prediction Models

被引:0
|
作者
Simunovic, Goran [1 ]
Balic, Joze [2 ]
Saric, Tomislav [1 ]
Simunovic, Katica [1 ]
Lujic, Roberto [1 ]
Svalina, Ilija [1 ]
机构
[1] JJ Strossmayer Univ Osijek, Mech Engn Fac Slavonski Brod, Strojarski Fak Slavonskom Brodu, HR-35000 Slavonski Brod, Croatia
[2] Univ Maribor, Fac Mech Engn, Fak Strojnistvo, SLO-2000 Maribor, Slovenia
来源
STROJARSTVO | 2010年 / 52卷 / 02期
关键词
Artificial intelligence; Neural networks; Process planning; Regression model; OPTIMAL CUTTING CONDITIONS; NEURAL-NETWORKS; SURFACE-ROUGHNESS; OPTIMIZATION; SYSTEM; TOOL; PARAMETERS; SELECTION;
D O I
暂无
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
The paper sets out to describe the results obtained by investigating the prediction of technological parameters and, indirectly, of technological time needed for seam tube polishing. The results of experimental investigations were used to define, analyse and compare two models. One is a mathematical i.e. statistical model obtained by the application of the least squares method and the least absolute deviation method. The other is a model based on the application of neural networks. To define the model based on the application of neural networks various structures of the back-propagation neural network with one hidden layer were analysed and the optimal one with the minimum RMS error was selected. The more precise predictions of technological time provided by the models supplement the previously defined manufacturing operations, replace the predictions based on the technologists' experience and form the basis on which to plan production and control delivery times. The work of technologists is thus made easier and the production preparation technological time shorter.
引用
收藏
页码:137 / 145
页数:9
相关论文
共 50 条
  • [1] Mixture Cure Models in Prediction of Time to Default: Comparison with Logit and Cox Models
    Wycinka, Ewa
    Jurkiewicz, Tomasz
    CONTEMPORARY TRENDS AND CHALLENGES IN FINANCE, 2017, : 221 - 231
  • [2] Comparison of Time Series Forecast Models for Rainfall and Drought Prediction
    Ponnamperuma, Narmada
    Rajapakse, Lalith
    MORATUWA ENGINEERING RESEARCH CONFERENCE (MERCON 2021) / 7TH INTERNATIONAL MULTIDISCIPLINARY ENGINEERING RESEARCH CONFERENCE, 2021, : 626 - 631
  • [3] Comparison of models for the prediction of travel time for public transport prioritization
    Xie, Wanqin
    Hepner, Eduard
    Duensing, Josua
    Hoyer, Robert
    2024 FORUM FOR INNOVATIVE SUSTAINABLE TRANSPORTATION SYSTEMS, FISTS, 2024,
  • [4] A comparison of three liquid chromatography (LC) retention time prediction models
    McEachran, Andrew D.
    Mansouri, Kamel
    Newton, Seth R.
    Beverly, Brandiese E. J.
    Sobus, Jon R.
    Williams, Antony J.
    TALANTA, 2018, 182 : 371 - 379
  • [5] INTEGRATING TIME AND SPACE IN TECHNOLOGICAL SUBSTITUTION MODELS
    MAHAJAN, V
    PETERSON, RA
    TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE, 1979, 14 (03) : 231 - 241
  • [6] Performance Comparison of Bus Travel Time Prediction Models across Indian Cities
    Jairam, R.
    Kumar, B. Anil
    Arkatkar, Shriniwas S.
    Vanajakshi, Lelitha
    TRANSPORTATION RESEARCH RECORD, 2018, 2672 (31) : 87 - 98
  • [7] Comparison Prediction Models Using Time Series in COVID-19 Infection in Mexico
    Keila Vasthi Cortés-Martínez
    Hugo Estrada-Esquivel
    Alicia Martínez-Rebollar
    Programming and Computer Software, 2024, 50 (8) : 648 - 661
  • [8] Profit Prediction Using ARIMA, SARIMA and LSTM Models in Time Series Forecasting: A Comparison
    Sirisha, Uppala Meena
    Belavagi, Manjula C.
    Attigeri, Girija
    IEEE ACCESS, 2022, 10 : 124715 - 124727
  • [9] Comparison of Rail Deterioration Prediction Models
    Rajendir, Rajendran Bharath
    Dziedzic, Rebecca
    PROCEEDINGS OF THE CANADIAN SOCIETY FOR CIVIL ENGINEERING ANNUAL CONFERENCE 2023, VOL 2, CSCE 2023, 2024, 496 : 209 - 219
  • [10] Comparison of In Silico Models for Prediction of Mutagenicity
    Bakhtyari, Nazanin G.
    Raitano, Giuseppa
    Benfenati, Emilio
    Martin, Todd
    Young, Douglas
    JOURNAL OF ENVIRONMENTAL SCIENCE AND HEALTH PART C-ENVIRONMENTAL CARCINOGENESIS & ECOTOXICOLOGY REVIEWS, 2013, 31 (01): : 45 - 66