Abstract-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.Note to Practitioners-Today's manufacturing enterprises have to face a number of challenges in a turbulent environment that originates amongst other from saturated markets, unprecedented and abrupt changes in market demands, an ever increasing number of product variants and smaller lot sizes. A recent research trend in Germany is the so called Cognitive Factory, where artificial cognitive capabilities are introduced to the control of production systems. The applications range from production planning and control, human-robot-cooperation, automatic robot programming to intuitive worker guidance systems.The concept of fully automated production systems is no longer a viable vision, as it has been shown, that the conventional automa- tion is not able to deal with the ever-rising complexity of modern production systems. Especially, a high reactivity, agility and adaptivity that is required by modern production systems, can only be reached by human operators with their immense cognitive capabilities, which enable them to react to unpredictable situations, to plan their further actions, to learn and to gain experience and to communicate with others. Thus, new concepts are required, that apply these cognitive principles to the planning processes and control systems of pr...
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.With the IDM (In-Process Depth Meter), Precitec developed a sensor system that is able to measure the depth of the keyhole in-process. On the basis of low-coherence interferometry, with a high robustness of the measured values against process emissions, the system is perfectly qualified to provide the measurement that the industry has been asking about for decades.As a leading manufacturer of processing heads, Precitec is able to provide a solution that is easy to integrate into existing optics. As a leading manufacturer of non-contact measurement systems, the company is also able to reduce the hardware to the most compact size. Both have proven their industrial suitability in hundreds of applications.A substantial reason for the increasing use of the laser in numerous fields of industrial production is the increased efficiency in comparison with competing techniques. Another reason would be the unique features of the laser beam as a tool itself. To really gain a profit by the use of this tool, a highly automated quality control of the production process is needed.The laser welding process offers several possibilities for process monitoring systems or process control but the complexity of the laser 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 neighbouring areas.One of the most significant pieces of information that needs to be measured in order to qualify the strength of the weld with respect to mechanical load and stress is the depth of the keyhole. There have been numerous approaches to find a sensor technology to be best placed to discover a correlation between the keyhole depth and the measured signal. These attempts have been discussed extensively in R&D and some have found their way to industrial applications. The common feature of these solutions is that they need basic understanding of the beam-materialinteraction to correlate the signal with the quality criteria. These systems pro-vide an estimate and not the actual keyhole depth.The IDM system is able to really measure the depth of the keyhole. The technology and the comparison between existing sensor systems and application results will be content of the following article.
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 closedloop control.
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