Engineering Applications of Artificial Intelligence, 2024
Support Vector Regression (SVR) models with different kernel functions (radial basis and polynomi... more 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.
Engineering Applications of Artificial Intelligence, 2024
Support Vector Regression (SVR) models with different kernel functions (radial basis and polynomi... more 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.
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Papers by Rajesh Yadav