{"title":"Comprehensive Assessment of the Performance of Removable Load-Handling Devices Using Artificial Neural Networks","authors":"R. V. Khvan","doi":"10.1134/S1052618825701523","DOIUrl":null,"url":null,"abstract":"<p>This article considers the use of artificial neural networks to analyze various defect combinations in removable load-handling devices and make decisions regarding their continued suitability for use. Statistical databases of expert assessments are used to train the artificial neural networks. An artificial neural network model has been developed that takes into account various defect combinations based on the deviation of the geometric parameters of the removable load-handling device from design values. It can be used to assess the performance of various types of removable load-handling devices, consisting of elements considered in the input layer of the neural network. The influence of the operating factors of removable load-handling devices on the confidence levels for determining their technical condition is demonstrated.</p>","PeriodicalId":642,"journal":{"name":"Journal of Machinery Manufacture and Reliability","volume":"55 1","pages":"54 - 63"},"PeriodicalIF":0.3000,"publicationDate":"2026-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Machinery Manufacture and Reliability","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1134/S1052618825701523","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0
Abstract
This article considers the use of artificial neural networks to analyze various defect combinations in removable load-handling devices and make decisions regarding their continued suitability for use. Statistical databases of expert assessments are used to train the artificial neural networks. An artificial neural network model has been developed that takes into account various defect combinations based on the deviation of the geometric parameters of the removable load-handling device from design values. It can be used to assess the performance of various types of removable load-handling devices, consisting of elements considered in the input layer of the neural network. The influence of the operating factors of removable load-handling devices on the confidence levels for determining their technical condition is demonstrated.
期刊介绍：
Journal of Machinery Manufacture and Reliability is devoted to advances in machine design; CAD/CAM; experimental mechanics of machines, machine life expectancy, and reliability studies; machine dynamics and kinematics; vibration, acoustics, and stress/strain; wear resistance engineering; real-time machine operation diagnostics; robotic systems; new materials and manufacturing processes, and other topics.
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