Identification of the Angle Errors of the LED Parallel-Light Module in PCB Exposure Device by Using Neural Network Learning Algorithms

For the smart manufacturing development of printed-circuit-board (PCB) exposure devices, LED parallel-light (LPL) module is investigated and angle errors those LPL units are identified by neural network learning algorithms. At present, in PCB manufacturing, most circuit boards use photoresist covering etching. After development, unwanted copper foil etched removed to make boards. The process its key process, equipment used this an machine. unit designed searched under principle higher irradiance uniformity. data supervised for convolutional (CNN) include a 2D distribution image constructed ray tracing simulation tool. In these data, all LPL-EM randomly added with self-specific error. By using Fast Region-based CNN, identification multi-LPL specific inclination azimuth verified. Those results preliminarily illustrate that techniques should be able help identify single multi-light devices. other words, technology serve as reference towards manufacturing.