Abstract
Among many factors that influence an excavator’s performance and productivity, the volume of the bucket load and duration of the excavator working cycle are crucial. In this paper, both factors were investigated, including the granulometric composition of the excavated material. The volume of material in the bucket was determined by photogrammetric analysis while the excavator cycle time was measured by analysis of video recordings captured by a digital video camera during the excavator operation. Interconnections between the angle of repose, slewing angle, particle size distribution of material, and their effects on hydraulic excavator productivity were analyzed. It was found that a larger number of fine particles in granular materials with a higher coefficient of uniformity resulted in an increase in the volume of the bucket load. Correlation analysis revealed significant interconnection between the bucket fill factor and swell factor. It was also found that calculation of the production rate according to ISO (International Organization for Standardization) standards was more accurate for materials with a higher angle of repose while the CECE (Committee for European Construction Equipment) standard was more appropriate for materials with lower angles of repose.
Highlights
Hydraulic excavators are common nowadays and are often irreplaceable equipment at the majority of mining and civil worksites [1]
This is in conformity with investigations mentioned in the introduction that the bucket fill factor for no coherent material will depend on the granulometric composition
The excavator cycle time is mainly affected by the bucket fill time and swing angle
Summary
Hydraulic excavators are common nowadays and are often irreplaceable equipment at the majority of mining and civil worksites [1]. Hydraulic excavators are primarily used for earthmoving works. In open pits or quarries, they are used for many different tasks such as auxiliary equipment or main machinery for non-cohesive mineral raw material excavation. Many authors have analyzed the productivity of earthmoving machinery as a system consisting of an excavator and truck [4,5,6,7,8,9], but calculation of the productivity of only the excavator was not conducted. Authors used miscellaneous statistical methods to model excavator productivity using different independent variables. An artificial neural network was applied in [11,12]
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