Novel intelligent control method for improving the fatigue life of crane boom

Roozbahani, Hamid; Mäkinen, Sasu; Pandolfo, Vinícius; Handroos, Heikki

doi:10.1109/icnsc.2016.7478982

Cited by 3 publications

(3 citation statements)

References 4 publications

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“…Malzeme elleçleme makinelerinin bomlarına benzer bomlara sahip akıllı kontrol sistemlerinin, yorulma ömrünün artıran çalışmalar ve bom boylarını arttırıcı çalışmalar gerçekleştirilmiştir [6]. Bir diğer çalışma olarak ise paletli vinçlerin birden fazla yükleme koşulundaki örgü yapıları incelenmiştir [7].…”

Section: Hurda Elleçleme Makinası Kaldırma Ve Kırma Bomunun Sonlu Elemanlar Analizi Yöntemi İle Optimizasyonu Optimization Of Scrap Handunclassified

Hurda Elleçleme Makinası Kaldırma Ve Kırma Bomunun Sonlu Elemanlar Analizi Yöntemi İle Optimizasyonu

Şan¹,

Demirsoy²

2021

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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ÖzGeri dönüşüm tesislerinde bulunan makinelerin çalıştığı sahalarda malzeme elleçleme makineleri de sıklıkla kullanılmaktadır. Malzeme elleçleme makinelerinde kapasitenin belirlenmesinde en önemli iki unsur makine bomlarının uzunluğu ve makinenin kaldırabileceği yük kapasitesidir. Bom uzunluğunun artması ve bununla birlikte kaldırma kapasitesinin artması tasarımı zorlaştıran durumdur. Bu çalışmada hurda elleçleme makinesinin bomlarının tam olarak açık durumu olan 14 metrede 3,5 ton kaldırabilecek, 2 serbestlik derecesine sahip bomların tasarlanması hedeflenmektedir. Bu kapsamda, öncelikle malzeme elleçleme makinesinin kinematik analizi yapılıp, kapasite açısından uygun hale getirilmiştir. Kapasite açısından uygun hale gelmiş makinenin bomlarının mukavemet optimizasyonu, oluşturulmuş olan akış diyagramına göre sonlu elemanlar analiz yöntemi olan ANSYS ile gerçekleştirilmiştir.

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Section: Hurda Elleçleme Makinası Kaldırma Ve Kırma Bomunun Sonlu Elemanlar Analizi Yöntemi İle Optimizasyonu Optimization Of Scrap Handunclassified

Hurda Elleçleme Makinası Kaldırma Ve Kırma Bomunun Sonlu Elemanlar Analizi Yöntemi İle Optimizasyonu

Şan¹,

Demirsoy²

2021

Deu Muhendislik Fakultesi Fen Ve Muhendislik

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“…Arsic et al conducted three-point bending experiments on an excavator boom to test fatigue crack growth [8]. Roozbahani proposed an intelligent control method to improve the fatigue life of crane arms [9]. The literature [10], [11]used Monte Carlo simulation to estimate the life and reliability of an aero-engine turbine disk.…”

Section: Introductionmentioning

confidence: 99%

Lifetime Reliability of Hydraulic Excavators’ Actuator

Liu,

Zeng,

Qin

et al. 2023

IEEE Access

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Due to the harsh working environment of hydraulic excavators, the actuators are subjected to high intensity and long duration, resulting in fatigue-induced cracks, fractures, and other failures, leading to work interruptions and delays, reducing the operational efficiency of the excavator. Therefore, fatigue life prediction is of great significance in improving its safety, reliability, and operational efficiency. The load-time history at each articulation point of the working mechanism is the basis for fatigue life prediction. Due to the complex and variable forces experienced during the excavation process, it is difficult to obtain the working load spectrum through direct measurement, which can provide a more realistic prediction of the fatigue life. In this study, the excavation process of different materials under different working conditions of the excavator is studied to obtain a more accurate load spectrum and complete the life prediction of the working mechanism based on the discrete element principle. Taking the hydraulic excavator's actuators as the research object, the load spectrum of each articulation point under extreme conditions such as eccentric load and impact is obtained for different excavation materials, and the minimum life prediction of the boom is completed. Genetic algorithm (GA) and Self-Adaptive Fast Fireworks Algorithm (SF-FWA) are used for life prediction, and function fitting is performed using historical failure data for life calculation and comparative analysis. The analysis shows that SF-FWA is more effective than GA. The advanced algorithm provides more accurate predicted values for estimating fatigue life, but less accurate for estimating MTBF.

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“…Researchers have developed theoretical approaches to coordinate control of hydraulic machines since the 90s, however, these proposed methods have not become popular, as in practice they require additional mechanical components [8][9][10][11][12][13][14]. Besides, various methodologies have been proposed to implement advanced boom tip control for hydraulic systems [15][16][17][18][19]. However, the uncertainty of control during the operating region was the main reason for not implementing these methods.…”

Section: Introductionmentioning

confidence: 99%