Modeling of Probe Comprehensive Pre-travel Error For On-machine Inspection System of Gear Grinder

Due to the existence of probe pre-travel error, the existing researches consider single or double influencing factors for error compensation. However, many experimental statistics show that, due to the anisotropy of touch trigger probe, factors such as signal transmission delay, inspection speed, ball radius, rod length, probe gravity and normal vector of measuring point on ball surface will affect the trigger time of inspection signal, resulting in probe comprehensive pre-travel error, so it is difficult to compensate accurately.When inputs are multiple error influencing factors, the efficient approximation algorithm of BP neural network is helpful to solve the measurement error output problem, so as to improve on-machine inspection accuracy effectively. According to the on-machine inspection principle of independently developed horizontal gear grinder L300G, taking multiple error influencing factors as the input node and probe comprehensive pre-travel error as the output node, then the prediction model of p