Optimising the steering clutch of a walking tractor using soft computing approaches

ABSTRACT

Walking tractors are often used for weeding gardens, small fields, vineyards, and greenhouses. The clutch fork arm (CFA), which is used on the rotary axle, is subject to high loads. Therefore, an optimal design must be carried out under all load conditions while minimising weight. The present study aims to reduce the mass and load acting on the CFA and increase its safety factor. To achieve an optimal design, this study used the dimensions of the CFA as independent variables and the mass and maximum load exerted on the CFA as dependent variables. Multi-linear regression (MLR) and Artificial Neural Network (ANN) models were then developed. MLR and ANN models were proposed to predict the mass and load acting on the CFA. The proposed models were trained and validated using a dataset containing dimensions, mass parameters and maximum stresses for eighty-one CFA specimens based on the results of the finite element method (FEM). Based on the optimised structural properties, a FEM model of the CFA was then developed in Ansys R19.2. The results showed that the maximum absolute difference in stresses from the FEM and ANN models was less than 3.7%, while for mass it was less than 2.2%. Furthermore, the maximum reduction in mass and increase in safety factor were achieved by 52.0% (from 1.3 kg to 0.62 kg) and 36.0% (from 1.28 to 1.64), respectively. Therefore, these results were a confirmation of the ANN model to use as an alternative to the FEM method.

KEYWORDS:

• Walking tractors
• cone clutch
• ANN
• FEM
• design optimisation

Acknowledgements

The authors are thankful to the reviewers for their comments and suggestions to improve the quality of the paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, [Afsaneh Soleimani], upon reasonable request.

Additional information

Notes on contributors

Afsaneh Soleimani

Afsaneh Soleimani is a researcher at the Department of Biosystems Engineering. She also graduated with a master’s degree in biosystem mechanics at Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran. Her primary research interests are Machine Learning, multi-objective optimization, and simulation and mechanical design.

Jalal Baradaran Motie

Jalal Baradaran Motie is a member of the Department of Biosystems Engineering and Head of the Community Relations Office at the Faculty of Agriculture, Ferdowsi University of Mashhad (Iran). He also holds a PhD in Mechanics of Biosystems Engineering. His main research interests are the development of high-tech agricultural instruments in the field of precision agriculture, soil-sensors and biosensors.