Inline Weld Depth Evaluation and Control Based on OCT Keyhole Depth Measurement and Fuzzy Control

Abstract

In an industrial joining process, exemplified by deep penetration laser beam welding, ensuring a high quality of welds requires a great effort. The quality cannot be fully established by testing, but can only be produced. The fundamental requirements for a high weld seam quality in laser beam welding are therefore already laid in the process, which makes the use of control systems essential in fully automated production. With the aid of process monitoring systems that can supply data inline to a production process, the foundation is laid for the efficient and cycle-time-neutral control of welding processes. In particular, if novel, direct measurement methods, such as Optical Coherence Tomography, are used for the acquisition of direct geometric quantities, e.g., the weld penetration depth, a significant control potential can be exploited. In this work, an inline weld depth control system based on an OCT keyhole depth measurement is presented. The system is capable of automatically executing an inline control of the deep penetration welding process based only on a specified target weld depth. The performance of the control system was demonstrated on various aluminum alloys and for different penetration depths. In addition, the ability of the control to respond to unforeseen external disturbances was tested. Within the scope of this work, it was thus possible to provide an outlook on future developments in the field of laser welding technology, which could develop in the direction of an intuitive manufacturing process. This objective should be accomplished through the use of intelligent algorithms and innovative measurement technology—following the example of laser beam cutting, where the processing systems themselves have been provided with the ability to select suitable process parameters for several years now.

Controlling the weld penetration depth of laser beam micro welding by using an iterative learning approach

“…In the literature, several experiments have already been carried out in this regard, in which machine learning algorithms, keyhole mapping or fuzzy controls are used to set the laser parameters. The main focus of these tests was the analysis of aluminum, where fixed optics were used to produce a linear weld seam [9,10]. Within the scope of this work, the experiments are to be extended in order to transfer the control to oscillating welding strategies for the processing of steel.…”

Advanced Optical Coherence Tomography for Real-Time Detection of Defects in Aluminum Alloy Laser Welding

“…Researchers like Christian Stadter et al have pioneered in establishing analysis models for weld depth and surface processing quality using machine learning methods. Their work in analyzing the data processing neural network model for the welding process of galvanized steel plates using OCT technology is a testament to the potential of well-trained neural network models in predicting the surface quality of online machining processes and achieving high machining accuracy [8]. Deyuan Ma and colleagues proposed a multi-sensor signal diagnostic approach for the online detection of critical porosity defects caused by pores in the laser welding process.…”