Prediction of Deviation Range of Gear Autonomous Machining Relying on Failure Sign Algorithm of Firefly Neural Network

Abstract

In view of the problems of complex deviation sources in gear machining and low accuracy of empirical prediction, in order to improve the detection accuracy and the operation speed of gears after machining, the failure sign algorithm of the firefly neural network (FSAFNN) is applied to the prediction of the deviation range of gear autonomous machining, and a gear tooth direction detection method is designed based on this method. The failure sign algorithm of the firefly neural network is constructed. Taking the tooth profile deviation in the gear machining process as the research target, a prediction model of the gear autonomous machining deviation is constructed, which can be used to effectively obtain the different invalid modes of the gear profile machining. The firefly neural network is quantitatively analyzed to obtain the main reasons for the tooth profile machining deviation. The firefly neural network algorithm can be used to take advantage of its advantages in metal and reflective object prediction. The final research results show that the method used in this paper can meet the requirements of high-precision and nondestructive detection of gears in industrial design, realize the division of gear accuracy levels, which can be used in the field of high-precision detection of other metal reflective objects, and is practical in the prediction of the deviation range of gear autonomous machining.