A programming system for robot-based remote-laser-welding with conventional optics
Introduction
The maximum focal length of a focused laser beam for a given spot diameter depends on the beam parameter product (BPP) of a laser source [1]. For a long time, the high BPP of conventional high power lasers like the Nd:YAG solid-state laser, limited the welding distances between optics and workpiece to about 250 mm [2]. Recent laser developments such as the fiber laser, improved the BPPs while operating distances were increased to one or more meters [3]. Joining with long focal lengths is commonly termed remote-laser-welding (RLW).
The main advantage of RLW is an increased positioning speed of the focus spot on the work piece by small deflections of the laser beam, leading to a significant reduction of cycle time. For beam deflection, several approaches are subject of research, e.g. galvanometer driven mirrors in scanner units [4]. An alternative, which is subject of this article, is the use of an industrial robot with a simple long-range optic (Fig. 1).
A central challenge for a broad usage of RLW is programming of an optimal robot movement with respect to cycle time. The quality of welds, expressed in welding depth and width can be manipulated by adjustable process parameters as laser power or welding speed. These parameters must be aligned, e.g. to material properties or plate thickness. Other parameters as sequence of welds or inclination angles of the laser beam have no or just minimal effect on the welding quality [5], [6], but a large influence on cycle time. Within this range, an infinite number of possible robot poses for positioning and welding exist (Fig. 2). For finding a good solution for a given welding problem, a powerful programming and optimization system is needed.
Section snippets
Current programming approaches
Today's industrial relevant programming methods can be roughly classified into online and offline techniques [7]. Online approaches like Teach-In, describe robot paths by guiding the real robot manually to discrete positions. Intelligent sensor aides for programming can assure that the robot is still on the right path but cannot create optimal paths from scratch. Offline methods mostly use 3D-simulation systems with CAD-functionality for definition of the robot setup and thereby transfer the
Programming approach
A RLW task is complex and contains variable and constant attributes. In order to optimize a path within the variant attributes, the authors propose a task-level programming system, composed of two elements (Fig. 3). The first one is used to describe the welding task and the possible solution space, independent of robot, optics and laser properties. The second part undertakes the optimization of robot paths, applying the task description as well as machinery specifications.
The result of
Augmented reality visualization
There have been many approaches for providing intuitive and efficient user interfaces for human–robot-interaction in the real environment. Augmented reality, i.e. interactively overlaying the human view of the environment with virtual information, is considered a promising technology in this context by many researchers over the past decade [10]. By means of AR-visualization, trajectory information, workpiece models, collision obstacles and also kinematic models can be visualized and also
Path planning and optimization
For path planning and optimization, computer-based calculation algorithms have been developed, which use the task description as input data. Because of the complexity of the optimization, the problem is divided in several sub-problems. The two central ones are discussed in this paper.
Experimental evaluation
To determine the performance of the proposed and implemented system for programming RLW tasks, we conducted several test with 2D and 3D work pieces. The tests focused on evaluation of programming time and cycle time, both compared to manual Teach-In programming. As can be seen in Table 1, by using the shown programming approach for RLW, a cycle time reduction between 28% and 37% could be achieved. Also, the time required for programming is strongly reduced in comparison to manual Teach-In.
Conclusion
Within this paper the authors introduce a new and efficient way to program and optimize RLW processes by using a task-level programming approach. For the task description in the real robot environment, an intuitive AR-based user interface has been developed which is combined with an automated planning system for RLW. The task level-interface allows process experts, without dedicated robot expertise, to set up RLW-processes significantly faster than using conventional methods. For the path
Acknowledgements
The path planning methods for RLW have been developed within the project “RoFaLas” funded by the German Federal Ministry of Research and Education (BMBF). The research on the AR-based user interface has been funded by the German Research Foundation (DFG) under grant number ZA288/7-1 and ZA288/7-2.
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