大功率光纤激光焊熔透状态模糊聚类识别方法

熔透是评价激光焊接质量的重要因素，但在焊接过程中难以直接检测熔透状态.以大功率光纤激光焊接304不锈钢紧密对接焊缝为试验对象，研究一种基于熔池红外热像的熔透状态识别方法.采用红外摄像机摄取焊接区域熔池动态图像，提取熔池特征量，应用模糊聚类算法分析熔池特征量与熔透状态之间的关系，使用模糊C-均值（FCM）和Gustafson-Kessel（GK）两种模糊聚类法建立熔透状态识别模型.激光焊接试验结果表明，熔池表征与熔透状态之间存在密切关联，通过GK模糊聚类算法建立的模型对熔透状态能达到78%以上的识别率，为大功率光纤激光焊接过程熔透状态的识别和控制提供试验依据.

Identification of high-power fiber laser welding penetration based on fuzzy clustering algorithm

Weld penetration is an important standard which evaluates the laser welding quality. However, it is difficult to directly measure the weld penetration during laser welding. Controlling the weld penetration can only be carried out through an indirect estimation of weld penetration by other sensing signals. A novel approach of weld penetration status identification based on molten pool infrared image processing is proposed during butt joint high-power fiber laser welding of type 304 austenitic stainless steel plates with laser power of 6 kW. An infrared high speed camera was used to capture the dynamic infrared images of molten pool. Characteristic parameters of a molten pool image were extracted by image processing, and fuzzy clustering algorithm was used to explore the relation between the molten pool parameters and the weld penetration status. An identification model of weld penetration status was established by using the fuzzy C-mean (FCM) and Gustafson-Kessel (GK) algorithm, respectively. Welding experiments confirmed that the molten pool surface characteristic had a close inherent relationship with the weld penetration, and the weld penetration status could be estimated by the proposed GK fuzzy clustering identification model, which could provide an experimental basis for detection and control of weld penetration.