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Rai, Piyush
- Finite Element-Based Simulation and Analysis of Dragline Bucket in Static and Dynamic Loading Condition
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PDF Views:82
Authors
Affiliations
1 Indian Institute of Technology (BHU), Varanasi 221 005, IN
1 Indian Institute of Technology (BHU), Varanasi 221 005, IN
Source
Current Science, Vol 116, No 4 (2019), Pagination: 612-619Abstract
Draglines are bulky and expensive machines widely utilized in opencast mines for overburden stripping. Due to tedious working conditions, a variety of fatigue failures in dragline components are common. Bucket is one of the main components of dragline, and it is a source of external load on the machinery than its interaction through broken rock material directly. Hence, dragline buckets are the most vulnerable components of wear, tear and related failures. This article analyses the von Mises stresses using the finite element method (FEM) under the static and dynamic loading conditions. In this study, the three-dimensional solid bucket models were developed in AUTO CAD and were investigated for stress, deformation, and safety factor on the dragline bucket under static and dynamic loading conditions using the ANSYS 18 software. FEM outcomes have been highlighted from teeth, the arc of anchors and hitch elements have a maximum value of stress and a minimum value of safety factor under various loading conditions. The purpose of this study was to prognosticate the bucket failure, the strength of bucket teeth and identify the sensitive areas of the dragline bucket.Keywords
Dragline Bucket, Loading Conditions, Static and Dynamic.Full Text
References
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- Influence of Rake Angle, Bucket Width and Teeth Depth of Dragline Bucket on the Resistive Force in Different Rock Types
Abstract Views :293 |
PDF Views:83
Authors
Affiliations
1 Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, IN
1 Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, IN
Source
Current Science, Vol 118, No 1 (2020), Pagination: 26-28Abstract
Draglines are used to dispose the overburden for exposing minerals in a surface mine. Draglines may be greater than 4000 t in overall weight, with bucket capacity ranging from 24 to 120 m3. The buckets are dragged against the blasted muck to fill the blasted overburden materials1. Bucket teeth fail easily while the excavator is being operated due to the fact that they are in direct touch with the rocks. The bucket teeth have an appropriate geometrical design for longer life and cost reduction2.Full Text
References
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- Ozdogan, M., Bucket penetration mechanism and bucket penetration forces of walking draglines, 2015; doi:10.13140/RG.2.1.2304.8406.
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- Golbasi, O. and Demirel, N., J. S. Afr. Inst. Min. Metall., 2015, 115(7), 623– 628.
- Abo-Elnor, M., Hamilton, R. and Boyle, J. T., Soil Tillage Res., 2004, 75(1), 61–73.
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- Porosity Prediction from Offshore Seismic Data of F3 Block, the Netherlands using Multi-Layer Feed-Forward Neural Network
Abstract Views :230 |
PDF Views:97
Authors
Affiliations
1 Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi 221 005, IN
2 Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi 221 005, IN
1 Department of Mining Engineering, Indian Institute of Technology (BHU), Varanasi 221 005, IN
2 Department of Geophysics, Institute of Science, Banaras Hindu University, Varanasi 221 005, IN
Source
Current Science, Vol 119, No 10 (2020), Pagination: 1652-1662Abstract
In the present study, seismic and well log information is incorporated with a multi-layer feed-forward neural network (MLFN) to predict porosity in the inter-well region. The aim of this study is to estimate a relationship between porosity and impedance to characterize the reservoir, if any, in the offshore F3 block, the Netherlands. MLFN is used to generate a connection between porosity logs and a set of seismic attributes, which are further used for porosity prediction. Modelbased inversion is employed to produce an acoustic impedance volume, which is a reliable technique for quantitative estimation of reservoir characteristics and acoustic impedance. The model-based inversion results indicate that the acoustic impedance (AI) in the region varies from 2500 to 6200 m/s*g/cm3, which is comparatively low and indicates loose formation. Thereafter, AI along with other attributes estimated from seismic data, is used as an input in MLFN, and porosity is predicted. The technique is first implemented on the traces close to well locations, and the findings are correlated with well log information, and after appropriate matching, the entire seismic segment is inverted for porosity. The results indicate that the porosity varies from 0.07 to 0.40. Further, a relationship between predicted porosity and inverted impedance is derived to represent the connection between these two parameters in the region. Moreover, based on this study, it is concluded that there is no significant reservoir in the region. However, as the analyses are performed for a specific range of data, it is possible that other parts of the area may have a different stratigraphy and possibility of the primary reservoir in the area.Keywords
Acoustic Impedance, Multi-layer Feed-forward Neural Network Reservoir, Porosity, Seismic Inversion.Full Text
- Evaluation of ground vibrations induced by blasting in a limestone quarry
Abstract Views :147 |
PDF Views:80
Authors
Punit Paurush
1,
Piyush Rai
1
Affiliations
1 Department of Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, IN
1 Department of Mining Engineering, Indian Institute of Technology, Banaras Hindu University, Varanasi 221 005, IN
Source
Current Science, Vol 122, No 11 (2022), Pagination: 1279-1287Abstract
Despite being a versatile and low-cost method, rock blasting produces undesirable severe effects. The present study aims to examine the ground vibrations produced by blasting, which are of serious concern to mine operators as well as the nearby inhabitants. Forty-nine field-scale trial blasts were conducted and recorded to measure ground vibrations produced by blasting in a limestone quarry in Rajasthan, India. The multivariate linear regression (MLR) and artificial neural network (ANN) techniques were used to predict the peak particle velocity (PPV) with distance between the blasting site and measuring station, charge per delay and scaled distance as the input parameters. Subsequently, a coefficient of determination (R2) was calculated using MLR and ANN approaches. Additionally, to verify whether the recorded events exceeded the threshold levels, the values of PPV and dominant frequency propounded by the United States Bureau of Mines (USBM), German standard (DIN), and Director General of Mines Safety, India were carefully scrutinized. Results were compared based on R2 values obtained by the USBM predictor equation, MLR and ANN techniques. It was found that ANN provided a good prediction with a high degree of correlation (0.901) in comparison to MLR (0.754). Also, frequency analysis for the study field showed that the dominance of frequencies was in the range 10–40 Hz. Although the values were within safe limits, disturbances may be witnessed in nearby structures if PPV values are high at lower frequency range.Full Text
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