视觉是手工焊接最重要的信息获取手段,为了模拟这一过程,建立了一套脉冲熔化极气体保护焊(P-CMAW)的双面实时视觉传感系统.利用被动光源视觉传感方法,研究了P-GMAW正反面熔池成像的滤光方法、成像时间、成像机理.结果表明,利用中心波长为665 nm的窄带滤光片在脉冲基值结束期间取像,可以获取清晰的P-GMAW正面熔池图像;利用中性减光技术,可以获取清晰的反面熔池图像.分析表明,正反面成像的主要光源均来自于熔池的自身辐射,因此,成像质量可靠.针对不同的熔池图像特征,提出了相应的图像处理算法,实现了正反面熔池边界的提取.
参考文献
[1] | BENTLEY A E;MARBERGER S J .Arc welding penetration control using quantitative feedback theory[J].Welding Journal,1992,71(11):397s-405s. |
[2] | NAKATA S;HUANG J;TSURUHA Y et al.Fundamental investigation on detecting the information on weld line and molten pool by combination of laser and interference filter with narrow half width-study on visual sensing system for in-process control of arc welding process (1 st report)[J].Quarterly Journal of Japan Welding Society,1988,6(01):123-127. |
[3] | R. Kovacevic;Y. M. Zhang .Real-Time Image Processing for Monitoring of Free Weld Pool Surface[J].Journal of manufacturing science and engineering,1997(2):161-169. |
[4] | RICHARDSON R W;GUTOW D A .Coaxial arc weld pool viewing for process monitoring and control[J].Welding Journal,1984,63(03):43-50. |
[5] | S. B. Chen;Y. J. Lou;L. Wu;D. B. Zhao .Intelligent methodology for sensing, modeling and control of pulsed TAW: Part I -- bead-on-plate welding: computer vision for sensing, neural networks for modeling, fuzzy logic and neuron self-learning for control of pulsed TAW are described[J].Welding Journal,2000(6):151s-163s. |
[6] | CHEN S B;WU L;WANG Q L et al.Self-learning fuzzy neural network and computer vision for control of pulsed GTAW[J].Welding Journal,1997,76(05):201s-209s. |
[7] | 陈彦宾,李俐群,陈凤东,陈杰.图像处理在自动焊接中的应用和展望[J].材料科学与工艺,2003(01):106-112. |
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