Calibration point distribution study of a four-wheel alignment optimization device based on a blanket technology algorithm.

ABSTRACT

Optimal wheel alignment improves fuel efficiency, safety, and driver comfort. An explicit formula for determining kingpin parameters, namely, caster and kingpin inclination angle (KIA), in four-wheel alignment is lacking. Currently, caster and KIA values are estimated by repetitive large-scale computing with a mathematical model aimed at obtaining values infinitely close to real solutions. In this study, a four-wheel aligner calibration device was used to collect large amounts of data for a variety of four-wheel aligner measurements with a short data interval. The data were subjected to the local fractal dimension analysis with fractional dimension-based blanket technology (BT) to optimize the number of measurement points. Dramatic changes in data were attributable to local areas with a large fractional number dimension. Appropriate increase in the number of calibration measuring points in areas with a relatively low fractional number dimension can reduce the overall quantity of measuring points. The results provide a scientific basis for the development of alignment calibration standards and demonstrate that these parameters can be assessed based on a small number of measurements. Our BT-based methodology can facilitate factory inspection and performance testing of four-wheel aligners and may improve the accuracy of wheel positioning parameter assessments.