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RESULTS OF STUDYING THE OPERATIONAL PROCESSES OF SMALL-SIZED SKID-STEER LOADERS WITH AN ON-BOARD STEERING SYSTEM

Leonid Razarenovкандидат технічних наук (PhD in Eng. S.), Харківський національний автомобільно-дорожній університет, доцент кафедри Експлуатації, випробування, сервісу будівельних та дорожніх машин, м. ХарківVolodymyr Hurkoаспірант (postgraduate student), Харківський національний автомобільно-дорожній університет, м. Харків
ABI

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The paper reviews the operation of skid-steer loaders and presents the methodology and results of studying dynamic processes and calculating the indicators of loading, oscillations, and stability of skid-steer loaders. The modeling of dynamic processes has proved the possibility of estimating external loads on buckets using a simplified relationship. Express methods for assessing machine load indicators, their stability, and the thermal regime of hydraulic drives are proposed. The analysis of field tests and calculated data established that the most severe loading mode of the working implement and traction drive of the loader occurs during normal bucket penetration into a pile of bulk material at a constant working volume of traction pumps. Self-excitation of the loader’s oscillations is possible during the final phase of its stopping in a bulk material pile, when the bucket bottom is tilted forward at an angle and the wheels are brought to full slip mode. The stability of the loader during the working cycle was analyzed. The load and temperature regimes of the hydraulic traction drive of skid-steer loaders are determined. Methods for predicting the main parameters and load of skid-steer loaders, proposals for improving the heat exchange system of the hydraulic drive, and methods of controlling the traction drive are presented. Measures to stabilize the hydraulic drive temperature to ensure uninterrupted operation of skid-steer loaders are proposed. The possibility of using the obtained dependencies in developing a computer model of a loader and introducing intelligent systems for automatic control of the bucket filling process using machine learning methods is substantiated.

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