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Remote laser welding (RLW) combines the positive features of tactile laser welding with additional benefits such as increased processing speed, reduced operational cost and service, and higher process flexibility. A leading challenge preventing the full uptake of RLW technology in industry is the lack of efficient closed loop in-process (CLIP) monitoring and weld quality control solutions. This underpins the need to fuse multiple sensor technologies and data analytics with predictive engineering simulations. Although the development and integration of a variety of sensors covers the radiation spectrum from ultraviolet to far-infrared, the flawless deployment of CLIP solutions is still challenged by the need for the following: signal denoising in the case of process instability; real-time data analytics; and adaptive control engineering architecture to cope with process variations induced by manufacturing tolerances. This paper focuses on the aspect of weld penetration depth control using optical coherence tomography (OCT) as a necessary step to enable adaptive penetration depth control during RLW of aluminum components in the fillet lap joint configuration with consideration to part-to-part gap variation. The approach entails decoupling the welding process parameters in two subsets: (1) in-plane control of the heat input on the upper part to facilitate the droplet formation; and (2) out-of-plane heat management to achieve the desired level of penetration control in the keyhole mode. This paper presents the results of finding the optimal placement of the OCT beam with variable part-to-part gap conditions. Results have shown that statistical signal processing of the raw OCT signal gives insight not only into the depth of the keyhole but can infer the shape of the keyhole itself. Current limitations and next phases of research and development are highlighted based on the experimental study.

journal of laser applications

Keyhole mapping to enable closed-loop weld penetration depth control for remote laser welding of aluminum components using optical coherence tomography