Ulrich Thombansen

Self-optimising Production Systems

One of the central success factors for production in high-wage countries is the solution of the conflict that can be described with the term “planning efficiency”. Planning efficiency describes the relationship between the expenditure of planning and the profit generated by these expenditures. From the viewpoint of a successful business management, the challenge is to dynamically find the optimum between detailed planning and the immediate arrangement of the value stream. Planning-oriented approaches try to model the production system with as many of its characteristics and parameters as possible in order to avoid uncertainties and to allow rational decisions based on these models. The success of a planning-oriented approach depends on the transparency of business and production processes and on the quality of the applied models. Even though planning-oriented approaches are supported by a multitude of systems in industrial practice, an effective realisation is very intricate, so these models with their inherent structures tend to be matched to a current stationary condition of an enterprise. Every change within this enterprise, whether inherently structural or driven by altered input parameters, thus requires continuous updating and adjustment. This process is very cost-intensive and time-consuming; a direct transfer onto other enterprises or even other processes within the same enterprise is often impossible. This is also a result of the fact that planning usually occurs a priori and not in real-time. Therefore it is hard for completely planning-oriented systems to react to spontaneous deviations because the knowledge about those naturally only comes a posteriori.

Process observation in fiber laser–based selective laser melting

Abstract. The process observation in selective laser melting (SLM) focuses on observing the interaction point where the powder is processed. To provide process relevant information, signals have to be acquired that are resolved in both time and space. Especially in high-power SLM, where more than 1 kW of laser power is used, processing speeds of several meters per second are required for a high-quality processing results. Therefore, an implementation of a suitable process observation system has to acquire a large amount of spatially resolved data at low sampling speeds or it has to restrict the acquisition to a predefined area at a high sampling speed. In any case, it is vitally important to synchronously record the laser beam position and the acquired signal. This is a prerequisite that allows the recorded data become information. Today, most SLM systems employ f-theta lenses to focus the processing laser beam onto the powder bed. This report describes the drawbacks that result for process observation and suggests a variable retro-focus system which solves these issues. The beam quality of fiber lasers delivers the processing laser beam to the powder bed at relevant focus diameters, which is a key prerequisite for this solution to be viable. The optical train we present here couples the processing laser beam and the process observation coaxially, ensuring consistent alignment of interaction zone and observed area. With respect to signal processing, we have developed a solution that synchronously acquires signals from a pyrometer and the position of the laser beam by sampling the data with a field programmable gate array. The relevance of the acquired signals has been validated by the scanning of a sample filament. Experiments with grooved samples show a correlation between different powder thicknesses and the acquired signals at relevant processing parameters. This basic work takes a first step toward self-optimization of the manufacturing process in SLM. It enables the addition of cognitive functions to the manufacturing system to the extent that the system could track its own process. The results are based on analyzing and redesigning the optical train, in combination with a real-time signal acquisition system which provides a solution to certain technological barriers.

Meta-modeling for manufacturing processes

Meta-modeling for manufacturing processes describes a procedure to create reduced numeric surrogates that describe cause-effect relationships between setting parameters as input and product quality variables as output for manufacturing processes. Within this method, expert knowledge, empiric data and physical process models are transformed such that machine readable, reduced models describe the behavior of the process with sufficient precision. Three phases comprising definition, generation of data and creation of the model are suggested and used iteratively to improve the model until a required model quality is reached. In manufacturing systems, such models allow the generation of starting values for setting parameters based on the manufacturing task and the requested product quality. In-process, such reduced models can be used to determine the operating point and to search for alternative setting parameters in order to optimize the objectives of the manufacturing process, the product quality. This opens up the path to self-optimization of manufacturing processes. The method is explained exemplarily at the gas metal arc welding process.

Brightness and average power as driver for advancements in diode lasers and their applications

Spatial and spectral emission characteristics and efficiency of high-power diode laser (HPDL) based pump sources enable and define the performance of the fundamental solid state laser concepts like disk, fiber and slab lasers. HPDL are also established as a versatile tool for direct materials processing substituting other laser types like CO2 lasers and lamp pumped solid state lasers and are starting to substitute even some of the diode pumped solid state lasers. Both, pumping and direct applications will benefit from the further improvement of the brightness and control of the output spectrum of HPDL. While edge emitting diodes are already established, a new generation of vertical emitting diode lasers (VCSELs) made significant progress and provides easy scalable output power in the kW range. Beneficial properties are simplified beam shaping, flexible control of the temporal and spatial emission, compact design and low current operation. Other characteristics like efficiency and brightness of VCSELs are still lagging behind the edge emitter performance. Examples of direct applications like surface treatment, soldering, welding, additive manufacturing, cutting and their requirements on the HPDL performance are presented. Furthermore, an overview on process requirements and available as well as perspective performance of laser sources is derived.

Tracking the course of the manufacturing process in selective laser melting

An innovative optical train for a selective laser melting based manufacturing system (SLM) has been designed under the objective to track the course of the SLM process. In this, the thermal emission from the melt pool and the geometric properties of the interaction zone are addressed by applying a pyrometer and a camera system respectively. The optical system is designed such that all three radiations from processing laser, thermal emission and camera image are coupled coaxially and that they propagate on the same optical axis. As standard f-theta lenses for high power applications inevitably lead to aberrations and divergent optical axes for increasing deflection angles in combination with multiple wavelengths, a pre-focus system is used to implement a focusing unit which shapes the beam prior to passing the scanner. The sensor system records synchronously the current position of the laser beam, the current emission from the melt pool and an image of the interaction zone. Acquired data of the thermal emission is being visualized after processing which allows an instant evaluation of the course of the process at any position of each layer. As such, it provides a fully detailed history of the product This basic work realizes a first step towards self-optimization of the manufacturing process by providing information about quality relevant events during manufacture. The deviation from the planned course of the manufacturing process to the actual course of the manufacturing process can be used to adapt the manufacturing strategy from one layer to the next. In the current state, the system can be used to facilitate the setup of the manufacturing system as it allows identification of false machine settings without having to analyze the work piece.

Self-optimizing Production Technologies

Customer demands have become more individual and complex, requiring a highly flexible production. In high-wage countries, efficient and robust manufacturing processes are vital to ensure global competitiveness. One approach to solve the conflict between individualized products and high automation is Model-based Self-optimization (MBSO). It uses surrogate models to combine process measures and expert knowledge, enabling the technical system to determine its current operating point and thus optimize it accordingly. The objective is an autonomous and reliable process at its productivity limit. The MBSO concept is implemented in eight demonstrators of different production technologies such as metal cutting, plastics processing, textile processing and inspection. They all have a different focus according to their specific production process, but share in common the use of models for optimization. Different approaches to generate suitable models are developed. With respect to implementation of MBSO, the challenge is the broad range of technologies, materials, scales and optimization variables. The results encourage further examination regarding industry applications.

Determination of process variables in melt-based manufacturing processes

Industrial manufacturing requires continuous production at reliable quality to be competitive. Many manufacturing processes are run close to their technological limits to increase productivity what leads to a significant threat of malfunction in the case of limited control over setting parameters or deviating boundary conditions. This paper discusses the difficulties of determining process variables during manufacturing for two melt-based manufacturing processes, laser cutting and gas metal arc welding. Both manufacturing processes show a highly dynamic and complex behaviour which today prevents a physical description of all interactions of the process variables. On the practical side, even the dominant process variables cannot be measured as they are not directly accessible. The approach that is presented here suggests a combined solution with both modelling and measuring tools that connect through surrogate criteria. It involves a simplified modelling of the manufacturing process that describes the process behaviour well enough and that can be evaluated numerically within a short time frame. The measurement evaluates a property of the process which is well accessible. This ensures robust signal processing and stable information about the surrogate criterion. In combination with the simplified model, the operating point of the process can easily be determined. For laser cutting of metal sheets and gas metal arc welding, it is demonstrated how to acquire information about the process and how to model surrogates. The research is focused on providing tools for fast machine set-up and for components which can be used for self-optimisation.

Process observation in selective laser melting (SLM)

In additive manufacturing, the quality of products can be traced by observation of process variables track by track and layer by layer. The stacking of layer wise information can be used to consolidate the entire build up history of a product thus leading to a truly three dimensional quality histogram. The first step that is necessary to achieve such a quality histogram is the acquisition of process measurands that are related to product quality. Successful acquisition of measurements for thermal radiation has been reported in several publications. The authors of such papers report the detection of changes in boundary conditions of the process by observing the thermal radiation of the process. It has been reported that for example a change in laser power has an influence on the thermal emission and that different readings are received for processing a thin powder layer on a solid work piece compared to scanning pure powder in the situation of an overhang structure. A correlation to the underlying reason for the increase in thermal radiation however is mostly related to the experimental setup rather than to in process measurements. This report demonstrates an approach of acquiring and combining synchronous measurements of different physical properties of the process. The coaxial observation system used in the experiments enables the synchronous acquisition of measurements of the thermal emission and the acquisition of images that visualize the surface of the powder bed in the vicinity of the interaction zone. The images are used to monitor the motion of powder particles as they are influenced by the melting process. This amount of particle motion is then correlated to areas of different powder thicknesses. The combination of this information with excessive readings in thermal emission classifies the event to be a situation of noncritical deviation of thermal emission. In fact, this combination of extracted features establishes a first key criterion for an unequivocal event mapping.

Mathematical modelling and linear stability analysis of laser fusion cutting

A model for laser fusion cutting is presented and investigated by linear stability analysis in order to study the tendency for dynamic behavior and subsequent ripple formation. The result is a so called stability function that describes the correlation of the setting values of the process and the process’ amount of dynamic behavior.

Methodology for assessing laser-based equipment

Abstract Methodologies for the assessment of technology’s maturity are widely used in industry and research. Probably the best known are technology readiness levels (TRLs), initially pioneered by the National Aeronautics and Space Administration (NASA). At the beginning, only descriptively defined TRLs existed, but over time, automated assessment techniques in the form of questionnaires emerged in order to determine TRLs. Originally TRLs targeted equipment for space applications, but the demands on industrial relevant equipment are partly different in terms of, for example, overall costs, product quantities, or the presence of competitors. Therefore, we present a commonly valid assessment methodology with the aim of assessing laser-based equipment for industrial use, in general. The assessment is carried out with the help of a questionnaire, which allows for a user-friendly and easy accessible way to monitor the progress from the lab-proven state to the application-ready product throughout the complete development period. The assessment result is presented in a multidimensional metric in order to reveal the current specific strengths and weaknesses of the equipment development process, which can be used to direct the remaining development process of the equipment in the right direction.