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Image Recognition for PCB Soldering Platform Controlled by Embedded Microchip Based on Hopfield Neural Network

机译:基于Hopfield神经网络的嵌入式微芯片控制PCB焊接平台的图像识别

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—In the study, embedded BASIC Stamp 2 (BS2) microchip controller is used to design with Hopfield neural network (HNN) as the foundation of sample training, which applies for a soldering platform of mechanical vision and accomplishes PCB soldering positioning technology. The proposed system design method in this paper can be divided into two parts: 1) the control rules of RC servo motor is designed by BASIC, and 2) human-machine interface is established to acquire images for pre-processing via C++ Builder. For the method of system image recognition, HNN is employed to do PCB soldering recognition positioning. The system is verified by MATLAB and Simulink to set up the simulation of PCB image soldering positioning. The experiment proves that the proposed method improves the traditional low efficiency of PCB soldering technology, and to achieve the feasibility of PCB image positioning and promote the soldering quality.
机译:- 在研究中,嵌入式基本印章2(BS2)Microchip控制器用于与Hopfield神经网络(HNN)设计为样本训练的基础,这适用于机械视觉的焊接平台并完成PCB焊接定位技术。本文提出的系统设计方法可分为两部分:1)RC伺服电机的控制规则由基本设计,2)建立人机界面以获取通过C ++构建器进行预处理的图像。对于系统图像识别的方法,采用HNN进行PCB焊接识别定位。 Matlab和Simulink验证系统以设置PCB图像焊接定位的仿真。实验证明,该方法提高了PCB焊接技术的传统低效率,并实现了PCB图像定位的可行性,促进了焊接质量。

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