A machine learning-based approach for estimation of deflection and contact area characteristics of tubeless and tube-type agricultural tyres

Abstract

Support Vector Regression (SVR) models with different kernel functions (radial basis and polynomial) were developed for predicting deflection and contact area of tubeless and tube-type tractor tyres. Data were collected from experimental trials on 13.6–28 tubeless and tube-type tyres under varying loads (750–1400 kg) and inflation pressures (69–179 kPa). A displacement transducer comprising a rack-pinion arrangement and a rotary potentiometer was developed for measuring tyre deflection. A total of 168 data points were split into 70% for training and 30% for testing the SVR models. An additional 42 new data points were collected at a normal load of 900 kg for validation purpose. As compared to tube-type tyre, tubeless tyre had higher contact area at higher normal loads due to higher tyre deflection. Two SVR models were constructed by considering normal load, inflation pressure, and tyre-type as input parameters. The well-trained SVR model could predict tyre deflection with a maximum deviation of 2% from the measured values as compared to −21% in case of Wulfsohn model. Similarly, the predicted contact area had a maximum variation of 1% from the measured values as compared to 9.99% in case of Komandi model and 31% in case of Diserens model. The sensitivity analysis indicated that the proposed models ranked normal load as the highest priority followed by inflation pressure and tyre-type for estimating both tyre deflection and contact area.