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The new platform WeldMaster by Precitec provides a large toolset for modern laser welding applications. Sensors and actuators for the pre-, in- and post-process monitoring and control are implemented, offering the most advanced all-in-one system-solution for laser welding tasks. The customer's benefits are especially valuable in automotive applications, where robustness, efficiency and reliability are highly demanded. The combination with an intelligent welding head allows to improve known applications and to develop trend-setting joining processes.

The essential basis for a reliable and target-aimed process control is the understanding of the interaction between the laser beam and the treated material and this was gained by thorough research on the influence of the process input parameters on the interaction sub processes and on the treatment result. The main players con-ducting this research over the decades have been research facilities and institutes and this research is still in progress. Since the moment when it was possible to achieve the necessary power density to start the process of deep penetration welding, accompanied by a keyhole, there is hope - and need - to measure e.g. the depth of this vapor channel. In the decades in which the technology of deep penetration welding has been used, various approaches have been developed that allow a measurement of the depth of the keyhole. The aim of this contribution is to show a compact overview on the different approaches to monitor and/or control micro and macro laser welding processes and especially bring out those which successfully have been transferred from laboratory to serial production in the recent past and will be in the near future. Laser materials processing in general offers several possibilities for process monitoring systems or process control but the complexity of the process itself, meaning the dependence of the processing result on several process input parameters, does not facilitate their use. As only continuous supervision of the manufacturing process can guarantee the high demands on the quality of the produced parts, process monitoring systems have become more and more standardized devices in laser applications. There is no doubt that the basis for reliable on-line process monitoring systems is the possibility to measure significant indicators, which demonstrates the instantaneous condition of the interaction zone and/or neighboring areas. This contribution to the Photonics West 2017 LASE conference on the one hand will demonstrate an approach using chromatic coded line sensors for post-weld inspection, on the other hand will show a sensor, based on interferometric principle, which is capable to in-situ measure keyhole depth during deep penetration laser welding and further potential of this sensor approach.

Talking about laser welding predominately means talking about the generation of a keyhole, the physics behind and invariably the depth of this steam capillary. Having the ability to measure this depth would undoubtedly raise the confidence in laser welding and also raise the quality of the processed part on a higher level.

The introduction of inline coherent imaging technologies as a sensor for the laser materials processing is accompanied by the integration into several applications. One of these is the measurement of the depth of the vapor capillary for laser welding applications, now allowing to keep record of the welding depth with an accuracy of micrometers and a sub millisecond temporal resolution. The broader achievement is the closed-loop control of the welding depth that was not available in industrial environments till now due to the lack of an adequate sensor. Further use includes the acquisition of 3D images around the laser process itself, allowing for coaxial integration of pre- and post-process sensors. These applications are demonstrated by using the In-Process Depth Meter (IDM).

Der Wandel von energiehungrigen Arbeitsmaschinen hin zu leistungsoptimierten Fertigungsverfahren kann in allen industriellen Produktionsprozessen beobachtet werden. Die Innovationsallianz „Green Carbody Technologies” untersucht Moglichkeiten zur Energieeinsparung im Karosseriebau. Im Rahmen des Teilprojektes 4.3.3 werden die Potentiale zur Leistungseinsparung beim Laserschweisen untersucht.

Focal spot positioning and robot path control require online measurement of the distance between a welding head and the workpiece to be processed. For autonomously working applications, such as cognitive focal shift control, distance measurements can be used to teach coaxial video images at different distances. This paper compares optical sensors based on laser triangulation with those based on optical coherence tomography, which can be embedded in either pre or post-process monitoring systems, thus allowing for accurate distance measurements. Because optical coherence tomography sensors are mainly used for medical purposes, their applicability in laser material processing was examined and verified. Using three workpieces with known geometrical properties, the lateral and depth resolution, and detectable angle dependency could be obtained. The advantages and limitations of the examined sensors are discussed here.Focal spot positioning and robot path control require online measurement of the distance between a welding head and the workpiece to be processed. For autonomously working applications, such as cognitive focal shift control, distance measurements can be used to teach coaxial video images at different distances. This paper compares optical sensors based on laser triangulation with those based on optical coherence tomography, which can be embedded in either pre or post-process monitoring systems, thus allowing for accurate distance measurements. Because optical coherence tomography sensors are mainly used for medical purposes, their applicability in laser material processing was examined and verified. Using three workpieces with known geometrical properties, the lateral and depth resolution, and detectable angle dependency could be obtained. The advantages and limitations of the examined sensors are discussed here.

The effects of laser power induced thermal lensing are difficult to monitor and may lead to weld or cutting defects. Considering the ongoing increase in power of laser sources and improvements in terms of beam quality, focal shifts require intensified attention and thus the necessity of technical countermeasures. This paper depicts the origins of thermal lensing by demonstrating the influence of the laser power density on the optical characteristics of welding heads. In the past, considerable investigations were made to monitor the positioning of the focal spot, which are introduced throughout the paper. A review of auto-focus techniques summarizes the efforts taken to control the positioning of the welding head.

When processing materials with laser beams, strong radiations and emissions occur, requiring a robust but accurate sensor system. Therefore, multiple sensors, such as an intelligently focusing camera, acoustic sensors and photodiodes, are integrated into a cognitive technical system using Artificial Neural Networks and other techniques for learning how to monitor and control the processing systems.

Laser beam welding is the method of choice for the high-quality joining of materials. However, for industrial production these systems have to be set up and calibrated manually with much effort. Our objective is to apply intelligent data processing that results in a cognitive technical system that can learn how to weld, speed up the configuring process, and reduce costs. While monitoring laser welding with cameras and optical sensors has already been demonstrated elsewhere, this paper emphasizes the benefits of monitoring with acoustic sensors and feature extraction. Using acoustic sensors, the cognitive system is more sensitive to strong optical radiation. Several combined methods such as wavelet analysis, fast Fourier transformation, and linear dimensionality reduction are evaluated with sensor data from real experiments. Finally, as machine learning, the results are classified with learned reference data to obtain reliable information for monitoring and possibly using closed-loop control.

Today's manufacturing and assembly systems have to be flexible to adapt quickly to an increasing number and variety of products, and changing market volumes. To manage these dynamics, several production concepts (e.g., flexible, reconfigurable, changeable or autonomous manufacturing and assembly systems) were proposed and partly realized in the past years. This paper presents the general principles of autonomy and the proposed concepts, methods and technologies to realize cognitive planning, cognitive control and cognitive operation of production systems. Starting with an introduction on the historical context of different paradigms of production (e.g., evolution of production and planning systems), different approaches for the design, planning, and operation of production systems are lined out and future trends towards fully autonomous components of an production system as well as autonomous parts and products are discussed. In flexible production systems with manual and automatic assembly tasks, human-robot cooperation is an opportunity for an ergonomic and economic manufacturing system especially for low lot sizes. The state-of-the-art and a cognitive approach in this area are outlined. Furthermore, introducing self-optimizing and self-learning control systems is a crucial factor for cognitive systems. This principles are demonstrated by a quality assurance and process control in laser welding that is used to perform improved quality monitoring. Finally, as the integration of human workers into the workflow of a production system is of the highest priority for an efficient production, worker guidance systems for manual assembly with environmentally and situationally dependent triggered paths on state-based graphs are described in this paper.

Even though widely used, laser beam welding faces a strong demand for improved monitoring and control capabilities. In this work the occurring focal shift which leads to faulty welding results will be explained. An approach is taken to monitor this effect caused by the heading of the optics. By means of a camera sharing the same optical components as the laser beam, the deviation of the optimal focal point is obtained through application of sharpness operators. Moreover, a certain measure of light intensity allows one to determine whether the focal plane is in front or behind the optimal focal point. By inclusion of further process parameters and an estimation of the process quality given by a cognitive monitoring system, a large manifold is built up. By application of linear and non-linear dimensionality reduction algorithms, the manifold is reduced to significant features fed to a fuzzy kNN-classifier. Its outcome suggests the required change in z-direction of the optical system with a behavior similar to the human eyes. The overall system is taught by an expert during its initialization and later saves production down times and faulty welding procedures.

Most laser welding heads offer the ability to connect a co-axial camera, using the same focusing lenses as the laser beam. Thus, the focal shift induced by heating the lenses and thus influencing the laser beam, alters the video data acquired. In the proposed approach, the sharpness of the video data is used to monitor the focal shift and distance changes relative to the workpiece surface. Distance control is achieved by means of a controllable z-axis. The addition of an Artificial Neural Network allows the correction of uncompensated errors such as shifts caused by the collimating optics. As a result, the process errors caused by thermal lensing can be reduced, improving the process quality and other sensor data acquisitions.Most laser welding heads offer the ability to connect a co-axial camera, using the same focusing lenses as the laser beam. Thus, the focal shift induced by heating the lenses and thus influencing the laser beam, alters the video data acquired. In the proposed approach, the sharpness of the video data is used to monitor the focal shift and distance changes relative to the workpiece surface. Distance control is achieved by means of a controllable z-axis. The addition of an Artificial Neural Network allows the correction of uncompensated errors such as shifts caused by the collimating optics. As a result, the process errors caused by thermal lensing can be reduced, improving the process quality and other sensor data acquisitions.