Evaluation of sheet curvature error by particle swarm optimization algorithm based on simulated annealing

Sheet rolling finishing is the most commonly used machining method for single curvature plate. Conventional manual sample testing method cannot realize the automation and digitization of forming processing. The discrete point data of sheet metal forming is obtained by laser detection device. However, the evaluation method of sheet metal curvature error based on discrete point directly affects the accuracy and efficiency of sheet metal forming correction process. The evaluation process of sheet curvature error occurs after the reconstruction of the surface, and it needs to be compared with the target surface for maximum matching to reduce the error with the target curvature. In this paper, the plate is coarse matched according to a suitable feature point and feature vector, and then the particle swarm optimization algorithm based on simulated annealing is used for fine matching to minimize the error, and finally the evaluation result of rolling bending is displayed, which provides data support for the secondary automatic processing. The particle swarm optimization algorithm based on simulated annealing can reduce the matching error, shorten the processing time of workers, improve the working efficiency of shipyards, and has a good application prospect in steel plate processing.