Metal AM

NiTi is a versatile material with inherent properties such as biocompatibility, shape recovery and superelasticity. However, the application of NiTi components in engineering systems is limited by the availability of suitable geometries. Most commercial off-the-shelf geometries are simple and limited in freedom of shape such as wires, rods, and springs. With powder bed fusion for metals (PBF-LB/M) a manufacturing technique is present of producing complex shaped and highly customized geometries. The capabilities of PBF-LB/M combined with the unique properties of NiTi can lead to novel developments in fields of biomedical, aerospace or robotics.

The research conducted aims to understand the impact of processing NiTi with PBF-LB/M on functional and mechanical properties over a wide range of processing parameters in comparison to conventional manufactured NiTi. The feedstock powder is crucial and affects the properties of the processed parts. However, the powder behaves significantly different compared to the same composition of conventional bulk NiTi. In addition, Ni evaporation through process parameter variation, such as laser power and scanning speed, is the governing factor when it comes to mechanical and functional characterization. In turn, the evaporation of Ni gives unique possibilities to tailor the functional properties, in particular the transformation behavior responsible for the shape recovery, to manufacture thermal graded monolithic and complex formed structures, which is not possible with conventional manufacturing. This is particular interesting for actuator applications, because with the developed technique sequential spatial actuation can be realized.

Besides, we extensively study the two-way shape memory effect (TWSME) and its usage in potential novel applications. In contrast to the one-way shape memory effect (OWSME) the recovery strain can be obtained both during heating and cooling offering lightweight actuation for aerospace or micromechanism applications.

In the arena of Industry 4.0, PBF is often recognized as a key element for the manufacturing revolution. While the technology has come a long way in recent years, the PBF process chain's reliability and quality still largely hinge on operator skill, thus leading to potential inconsistencies in product output.

Our expert team of researchers seeks to address these challenges by reducing operator-induced variability and enhancing part quality consistency. The core of our approach involves the implementation of an intelligent feedback control system, rooted in computer vision, sensor fusion, and dynamic process control.

Utilizing computer vision, our research incorporates the use of a high-resolution camera to scrutinize the PBF manufacturing process in intricate detail. We then apply sophisticated AI-driven image analysis techniques to evaluate the material deposition layer by layer, identifying any potential defects or deviations in real-time.

In conjunction with this visual data, we employ sensor fusion, the integration of data from multiple sensors such as temperature, pressure, and vibration sensors. This method furnishes us with a more thorough understanding of the manufacturing process and heightens the accuracy of our defect detection capabilities. By blending multiple sensor inputs, we can achieve a comprehensive view of the PBF process and pinpoint issues that might otherwise elude detection.

This wealth of collected data is fed into our intelligent control system, a vital component of the feedback loop. This system is designed to adapt the local scanning strategy and tweak the process parameters for optimal results, swiftly rectifying detected issues without requiring manual intervention.

Our research and expertise lie in using this technology-rich feedback control system to bring heightened reliability, efficiency, and quality to the PBF process chain. We believe that our system will not only augment the accuracy of PBF but will also play a pivotal role in propelling Industry 4.0 forward, thus leading the way towards fully autonomous and smart manufacturing processes.

At the heart of our work lies the transformational power of additive manufacturing (AM). Recognized for its ability to create intricate parts, offer exceptional design flexibility, and tailor parts to specific needs, AM simultaneously minimizes material wastage. Despite its potential, the challenge of maintaining the quality and structural stability of the fabricated parts remains. Our research focuses on integrating non-destructive testing (NDT) techniques with the AM process, creating a powerful solution to this challenge. We're specifically exploring the further development and refinement of Eddy Current Testing technology (ECT) in Powder Bed Fusion Laser Beam Melting (PBF-LB/M) machinery. Our goal is to achieve control over the PBF-LB/M process layer-by-layer to identify and rectify any defects.

Our research process initiates with a focus on defect characterization. By harnessing the power of machine learning and artificial intelligence, we aim to construct a precise and robust system for defect detection and classification. This system will lay a solid foundation for our subsequent research stages.

The defect data gathered in this stage is consolidated with other machine-related data. This results in a comprehensive Statistical Process Control (SPC) system, capable of offline monitoring and eventually, real-time layerwise quality assurance. Our SPC system guarantees that the produced parts align with the desired standards and specifications.

In the concluding stage of our research, we aspire to push the boundaries of layerwise quality control. We aim to explore and implement strategies for healing specific defect categories like local porosity or minor sub-surface cracks.

Our research endeavors to integrate Eddy Current Technology (ECT) with Powder Bed Fusion Laser Beam Melting (PBF-LB/M) machinery. This integration promises remarkable improvements in the quality, dependability, and efficiency of additive manufacturing, significantly boosting the robustness and sustainability of the AM industry. We believe our research findings have the potential to revolutionize how AM is perceived and utilized across various high-value sectors, thus encouraging its broader adoption in modern manufacturing practices.

We specialize in additive manufacturing (AM) technologies, with a particular focus on laser-based powder bed fusion (PBF-LB) processes. Our aim is to manufacture near-net-shape components that reduce the need for additional tooling procedures and lessen material waste. This is especially vital given the high cost and environmental impact of feedstock materials utilized in LPBF of Al alloys.

We are currently exploring the potential and constraints of employing recycled high-strength Al alloys in LPBF. Our initial investigations are centered around the influence of iron, a common impurity in recycled aluminum, on the processability and crack formation of AlMgSc alloys. Our research extends to examining the Fe-tolerance of a commercial AlMgSc alloy by blending it with Fe powders at varying contents, and conducting subsequent processing and characterization. This includes microstructural analyses and mechanical tests at both room and high temperatures, as well as the evaluation of the corrosion properties of the different alloys.

The next phase of our research focuses on the role of Mg, a solid solution strengthener in the 5xxx series Al alloys, in terms of process robustness and recyclability. The propensity of Mg to evaporate during the process poses challenges in maintaining consistent properties among parts produced at different positions on the build plate and with varying degrees of powder reuses. This loss of Mg not only occurs during the process but also during powder production, impeding the optimal infinite recyclability of aluminum. To address this, we are investigating the effects of these losses on powder and part properties by atomizing some additive manufactured components produced using a Mg-rich and a Mg-free Al alloy specifically designed for AM.

In essence, our research is aimed at expanding the understanding of how impurities like iron or low-melting point elements like magnesium can impact the processability, mechanical properties, and recyclability of Al alloys in LPBF. Our goal is to contribute to the advancement of more sustainable and efficient additive manufacturing practices.

Digitalization is evolving in all kinds of fields and industries. The first step in order to make use of data and digitalization is the integration of sensors. With PBF-LB/M, the manufacturing of smart and digitalized parts is possible due to the exploitation of the layerwise manufacturing technique. During the build-up of the physical 3-dimensional part every layer can be accessed via a process interruption to place sensors into the metal part.

We conduct research on the integration of sensors during but also after the PBF-LB/M process. During integration of sensors in additive manufactured parts, there arise several challenges, which need to be addressed in order to guarantee the integrity of the integrated sensor. Excessive heat accumulation, high peak temperatures near the surface, and powder contamination are the most urgent challenges. With the design of customized cavities and encapsulation several sensors where successfully integrated. Temperature, Hall, eddy current and fibre bragg sensors where integrated in demonstrator and industrial parts. A combination of temperature and Hall sensor was integrated into a hydrogen valve from NovaSwiss to enable condition monitoring of the valves used for hydrogen fuel stations. Besides, the initial valve space and weight could be reduced drastically with design optimization and complex flow channels with are not possible to manufacture in conventional ways.

With a novel developed integration technique, we also achieved to integrate piezoelectric stacks into an aerospace camera bracket, which enables the damping of structural eigenfrequencies. The pre-stressing and structural bonding of the stack was realized with additively manufactured NiTi and usage of shape recovery to pre-stress the stack up to 600 N, which is in the specification of the supplier. Additionally, an electric pre-stress circuit enables the measurement of pre-stress.