Arc length stability and droplet transition consistency are key factors for the pulse metal inert gas （MIG） welding quality. A new control strategy is proposed based on pulse current waveform adjustment to stabilize the welding process. After sufficient analysis of the droplet transition process, key current waveform parameters are refined that can affect the welding quality greatly. In order to achieve the optimal nonlinear control of parameters, the fuzzy controller is designed successfully with two inputs and three outputs in field programmable gate array （ FPGA ）, which occupies fewer resources than PID controller and has higher control performance. Experimental results show that the arc length can be adjusted fast in full range of welding current, the welding process is stable, the droplet transition has good consistency, and the welding quality is perfect.
焊接数字图像因其易被篡改的特性而影响其完整性,对焊接质量的评价过程造成很大困难,从而影响评价结果。文章建立原始焊接数字图像的敏感奇异值组,基于二维混沌序列加密水印图像,将其嵌入至原始焊接数字图像中,通过对比提取出的水印完成篡改的检测和定位,并对实验进行了验证。结果表明:算法峰值信噪比为84.29,大于等于36 d B,能够视觉保真;算法的篡改检出概率不小于99.99946%;经过对100个篡改样本进行试验,水印相似度均小于1,算法可成功完成对篡改样本检出和定位。
The welding process essentially is a complicated nonlinear system with time-varying, uncertain, strong-coupling characteristics, so it is difficult to get high welding quality by traditional control approaches such as the standard proportionalintegral （ PI） algorithm. A new algorithm based on artificial neural network （ANN） is presented to achieve optimal P1 parameters and improve its adaptability. First, main parameters of artificial neural network are researched to improve the convergence rate and system stability. Then, six expert rules are proposed to constitute the expert adaptive ANN-PI algorithm. Experimental results show that the welding current control system＇has high dynamic response rate, and the welding process is stable.
