<jats:p>Abstract. Climate change, rare resources and industrial transformation processes lead to a rising demand of multi-complex lightweight forming parts, especially in aerospace and automotive sectors. In these industries, flow forming is often used to produce cylindrical forming parts by reducing the wall thickness of tubular semifinished parts, e.g. for the production of hydraulic cylinders or gear shafts. The complexity and functionality of flow forming workpieces could be significantly increased by locally graded microstructure and geometry structures. This enables customized complex hardness distributions at wear surfaces or magnetic QR codes for a unique, tamper-proof product identification. The production of those complex, 2D (axial and angular) graded forming parts currently depicts a great challenge for the process and requires new solutions and strategies. Hence, this paper proposes a novel control strategy that includes online measurements from an absolute encoder to determine the angular workpiece position. Workpieces of AISI 304L stainless steel with 2D-graded structures are successfully manufactured using this new strategy and analyzed regarding the possible accuracy and resolution of the gradation. At this point, a dependency of the gradations on the sensor and actuator dynamics, accuracy and geometry could be noted. It is further evaluated how the control strategy could be extended by an observer-based closed-loop property control approach to enhance the accuracy of the suggested strategy. </jats:p>

<jats:p>Abstract. Workpiece property-control permits the application-oriented and time-efficient production of components. In reverse flow forming, for example, a control of the microstructure profile is not yet part of the state of the art, in contrast to the geometry control. This is, due to several reasons, particularly challenging when forming seamless tubes made of metastable austenitic stainless AISI 304L steel. Inducing mechanical and/or thermal energy can cause a phase transformation from austenite to martensite within this steel. The resulting α’-martensite has different mechanical and micromagnetic properties, which can be advantageous depending on the application. For purposes of local property control, the resulting α’-martensite content should be measured and controlled online during the forming process. This paper presents results from the usage of a custom developed cryo-system and different application strategies to use liquid nitrogen as a coolant for local enhancement of the forming-temperature depending α’-martensite content. </jats:p>

The development of autonomous vehicles and their introduction in urban traffic offer many opportunities for traffic improvements. In this paper, an approach for a future traffic control system for mixed autonomy traffic environments is presented. Furthermore, a simulation framework based on the city of Paderborn is introduced to enable the development and examination of such a system. This encompasses multiple elements including the road network itself, traffic lights, sensors as well as methods to analyse the topology of the network. Furthermore, a procedure for traffic demand generation and routing is presented based on statistical data of the city and traffic data obtained by measurements. The resulting model can receive and apply the generated control inputs and in turn generates simulated sensor data for the control system based on the current system state.

High-strength wire materials are usually available as strip material which is further processed in a forming process (e.g. punch-bending). For storage and transport of the semi-finished wire to the customer, the material is wound onto coils. The manufacturing and coiling process introduces plastic deformations into the wire, which lead to undesirable residual stresses and wire curvature of the semi-finished product. These residual stresses and curvatures cause variations in the material properties of the semi-finished product, which have a negative impact on the subsequent product quality. Straightening machines are used to compensate the residual stresses and the curvature in the wire. At the beginning of the straightening process, the straightening machines must be set up in such a way that residual stresses and curvatures are optimally compensated. This setup process is usually a manual and iterative process, where a lot of material is wasted until the optimal settings for the straightening machine are found.In order to reduce the amount of material waste, the operator must be supported in the setup process. In this context, a new and innovative setup assistance system was developed to support the operator during the setup process. The setup assistant system automatically detects the wire curvature by means of an optical measuring system. Based on the optically detected measuring points, the wire curvature is determined by a robust calculation algorithm. Based on a database built up through the carried out experimental and numerical research work, the optimum setting parameters for the straightening machine are suggested to the operator without lengthy trial and error. After confirmation by the operator, the roller settings are automatically adjusted by the mechatronic straightening machine. With the presented method, the conventional iterative setup procedure can be made more resource-efficient and a high straightening quality can be reproducibly achieved.

Due to increasing globalization and rising quality requirements, the steel and metal processing industry is facing growing cost and innovation pressure. Not least because of their high lightweight potential, high-strength steel materials are meeting the growing material requirements of steel and metal processing in areas such as aerospace and medical technology. In particular, the tight tolerance limits of applicable shape and dimensional accuracies pose a challenge in the processing of high-strength steel strip materials. Improving the processability of high-strength steel materials through the use of straighteners with set-up assistance systems significantly increases the potential for competing with other materials such as aluminum or magnesium alloys.

Within this work, we investigate how data-driven numerical approximation methods of the Koopman operator can be used in practical control engineering applications. We refer to the method Extended Dynamic Mode Decomposition (EDMD), which approximates a nonlinear dynamical system as a linear model. This makes the method ideal for control engineering applications, because a linear system description is often assumed for this purpose. Using academic examples, we simulatively analyze the prediction performance of the learned EDMD models and show how relevant system properties like stability, controllability, and observability are reflected by the EDMD model, which is a critical requirement for a successful control design process. Subsequently, we present our experimental results on a mechatronic test bench and evaluate the applicability to the control engineering design process. As a result, the investigated methods are suitable as a low-effort alternative for the design steps of model building and adaptation in the classical model-based controller design method.

The design of control engineering applications usually requires a model that accurately represents the dynamics of the real system. In addition to classical physical modeling, powerful data-driven approaches are increasingly used. However, the resulting models are not necessarily in a form that is advantageous for controller design. In the control engineering domain, it is highly beneficial if the system dynamics is given in PCHD form (Port-Controlled Hamiltonian Systems with Dissipation) because globally stable control laws can be easily realized while physical interpretability is guaranteed. In this work, we exploit the advantages of both strategies and present a new framework to obtain nonlinear high accurate system models in a data-driven way that are directly in PCHD form. We demonstrate the success of our method by model-based application on an academic example, as well as experimentally on a test bed.

Ultrasonic wire bonding is a solid-state joining process used to form electrical interconnections in micro and power electronics and batteries. A high frequency oscillation causes a metallurgical bond deformation in the contact area. Due to the numerous physical influencing factors, it is very difficult to accurately capture this process in a model. Therefore, our goal is to determine a suitable feed-forward control strategy for the bonding process even without detailed model knowledge. We propose the use of batch constrained Bayesian optimization for the control design. Hence, Bayesian optimization is precisely adapted to the application of bonding: the constraint is used to check one quality feature of the process and the use of batches leads to more efficient experiments. Our approach is suitable to determine a feed-forward control for the bonding process that provides very high quality bonds without using a physical model. We also show that the quality of the Bayesian optimization based control outperforms random search as well as manual search by a user. Using a simple prior knowledge model derived from data further improves the quality of the connection. The Bayesian optimization approach offers the possibility to perform a sensitivity analysis of the control parameters, which allows to evaluate the influence of each control parameter on the bond quality. In summary, Bayesian optimization applied to the bonding process provides an excellent opportunity to develop a feedforward control without full modeling of the underlying physical processes.

This paper deals with a novel method for the online fitting of a microscopic traffic simulation model to the current state of a real world traffic area. The traffic state estimation is based on limited data of different measurement sources and guarantees general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. The research is embedded in the challenge of improving the traffic by controlling the traffic light systems (TLS) of the examined area. Therefore, the current traffic state and the predicted route choices of individual road users are the matter of interest. The concept is generally transferable to any road traffic system. To give an impression of the accuracy and potential of the approach, the validation and first application results are presented.

The online fitting of a microscopic traffic simulation model to reconstruct the current state of a real traffic area can be challenging depending on the provided data. This paper presents a novel method based on limited data from sensors positioned at specific locations and guarantees a general accordance of reality and simulation in terms of multimodal road traffic counts and vehicle speeds. In these considerations, the actual purpose of research is of particular importance. Here, the research aims at improving the traffic flow by controlling the Traffic Light Systems (TLS) of the examined area which is why the current traffic state and the route choices of individual road users are the matter of interest. An integer optimization problem is derived to fit the current simulation to the latest field measurements. The concept can be transferred to any road traffic network and results in an observation of the current multimodal traffic state matching at the given sensor position. First case studies show promosing results in terms of deviations between reality and simulation.

One of the main objectives of production engineering is to reproducibly manufacture (complex) defect-free parts. To achieve this, it is necessary to employ an appropriate process or tool design. While this will generally prove successful, it cannot, however, offset stochastic defects with local variations in material properties. Closed-loop process control represents a promising approach for a solution in this context. The state of the art involves using this approach to control geometric parameters such as a length. So far, no research or applications have been conducted with closed-loop control for microstructure and product properties. In the project on which this paper is based, the local martensite content of parts is to be adjusted in a highly precise and reproducible manner. The forming process employed is a special, property-controlled flow-forming process. A model-based controller is thus to generate corresponding correction values for the tool-path geometry and tool-path velocity on the basis of online martensite content measurements. For the controller model, it is planned to use a special process or microstructure (correlation) model. The planned paper not only describes the experimental setup but also presents results of initial experimental investigations for subsequent use in the closed-loop control of α’-martensite content during flow-forming.