Development of draft force estimation model for hand tractor powered digger-cum-conveyor by rake angle and digging depth
Date
2023
Authors
Kumawat, L.
Raheman, H.
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Journal article
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Journal of Biosystems Engineering, 2023; 48(2):152-164
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Abstract
Purpose: Predicting the draft requirement for an implement is crucial from the viewpoint of proper machine design. It was hypothesized that the draft requirement for moving the digger forward depends mainly on two major parameters, i.e., rake angle of the digging blade and depth of digging, which could be used to develop a linear regression model for estimating its draft requirement.
Methods: A digging blade of a digger cum conveyor (DCC) was typically mounted at an angle (known as rake angle) to remove the onion bulbs from the soil. In this study, a newly designed digging blade was used. A provision was made at both the ends of digging blade to vary the rake angle. Structural analysis of the digging blade was also studied to check the strength in terms of the displacement of each blade component. The rake angle of the digging blade and digging depth were measured using digital protractor and measuring scale, respectively. Investigations were carried out on draft requirement of DCC at Indian Institute of Technology, Kharagpur (22°19′ N, 87°19′ E), India, to test this hypothesis. A full factorial experimental design was made with draft measured using S-type load cell (5-tonne capacity) at three levels each of digging depth, rake angle, and operating conditions (i.e., digging without conveying, digging with conveying in 1ˢᵗ gear and 2ⁿͩ gear as well) with the objective to acquire data on the draft force requirement of DCC. The experiments were carried out at a 0.82 km h⁻¹ forward speed of hand tractor to evaluate the performance of the developed DCC in sandy clay loam soil at a moisture content of 11.84% (d.b). The independent variables were found to have significant effect on the draft with digging depth having highest influence followed by rake angle and operating conditions. A linear regression model was carried out to develop a model for estimating draft requirement for DCC using SPSS statistics software.
Results: The efficiency of draft model was assessed by various performance indices such as mean error (ME), coefficient of determination (R²), root mean square error (RMSE), mean absolute percentage error (MAPE), and value account for (VAF) and their values were found to be − 0.008, 0.91 0.026, 2.51, and 97.39, respectively. A good general agreement between measured and estimated draft was found with the data obtained from the separated set of data with an average absolute variation (AAV) of 1.98%.
Conclusions: Farmers and manufacturers could therefore rely on the developed drafts estimated model to provide users accurate information about the needed drafts for DCC.
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Copyright 2023 The Author(s), under exclusive licence to The Korean Society for Agricultural Machinery