1 Ocak 2012 Dergi makalesi Açık Erişim

Kilic, Ergin; Dogruer, Can Ulas; Dolen, Melik; Koku, Ahmet Bugra

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{
  "@context": "https://schema.org/", 
  "@id": 86451, 
  "@type": "ScholarlyArticle", 
  "creator": [
    {
      "@type": "Person", 
      "affiliation": "Middle E Tech Univ, Dept Mech Engn, TR-06531 Ankara, Turkey", 
      "name": "Kilic, Ergin"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Hacettepe Univ, Dept Mech Engn, TR-06800 Ankara, Turkey", 
      "name": "Dogruer, Can Ulas"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Middle E Tech Univ, Dept Mech Engn, TR-06531 Ankara, Turkey", 
      "name": "Dolen, Melik"
    }, 
    {
      "@type": "Person", 
      "affiliation": "Middle E Tech Univ, Dept Mech Engn, TR-06531 Ankara, Turkey", 
      "name": "Koku, Ahmet Bugra"
    }
  ], 
  "datePublished": "2012-01-01", 
  "description": "This paper focuses on a viable position estimation scheme for timing-belt drives using artificial neural networks. In this study, the position of a carriage (load) is calculated via a structured neural network topology accepting input from a position sensor on the actuator side of the timing belt. The paper presents a detailed discussion on the source of transmission errors. The characteristics of the error in different operation regimes are exploited to construct different network topologies. That is, a relevant neural network model is developed by the sketchy guidance of a priori knowledge on the process. The resulting structured neural network is shown to estimate the error of the carriage quite accurately whereas generic recurrent neural networks fail to capture the dynamics of the system under investigation altogether. Extensive testing demonstrates the effectiveness of proposed method when the drive system is not subjected to external loads while the operating conditions such as ambient temperature and belt tensions do not deviate from the experimental conditions. (C) 2011 Elsevier Ltd. All rights reserved.", 
  "headline": "Position estimation for timing belt drives of precision machinery using structured neural networks", 
  "identifier": 86451, 
  "image": "https://aperta.ulakbim.gov.tr/static/img/logo/aperta_logo_with_icon.svg", 
  "license": "http://www.opendefinition.org/licenses/cc-by", 
  "name": "Position estimation for timing belt drives of precision machinery using structured neural networks", 
  "url": "https://aperta.ulakbim.gov.tr/record/86451"
}