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Large-language models (LLMs) such as GPT-4 caught the interest of many scientists. Recent studies suggested that these models could be useful in chemistry and materials science. To explore these possibilities, we organized a hackathon. This article chronicles the projects built as part of this hackathon. Participants employed LLMs for various applications, including predicting properties of molecules and materials, designing novel interfaces for tools, extracting knowledge from unstructured data, and developing new educational applications. The diverse topics and the fact that working prototypes could be generated in less than two days highlight that LLMs will profoundly impact the future of our fields. The rich collection of ideas and projects also indicates that the applications of LLMs are not limited to materials science and chemistry but offer potential benefits to a wide range of scientific disciplines.
The industrialization of AM is only possible by creating synergy with the tools of Industry 4.0. The system technology of Powder Bed Fusion with Laser beam of Metals (PBF-LB/M) reached a level of high performance in terms of process stability and material spectrum in the past years. However, the digital process chain, starting from CAD via CAM and plant-specific compila-tion of the manufacturing file exhibits media disruptions. The consequence is a loss of metadata. A uniform data scheme of simulation for Design for Additive Manufacturing (DfAM), the PBF-LB/M process itself and quality assurance is currently not realized within industry. There is no entity in the common data flows of the process chains, that enables the integration of these functionalities. As part of the creation of a digital quality infrastructure in the QI-Digital pro-ject, an integration of the CAD/CAM chain is being established. The outcome is a file in an advanced commercially available format which includes all simula-tions and manufacturing instructions. The information depth of this file extends to the level of the scan vectors and allows the automatic optimization and holis-tic documentation. In addition, the KPI for the economic analysis are generated by compressing information into a unique file combined with the application of a digital twin. The implementation and advantages of this solution are demon-strated in a case study on a multi-laser PBF-LB/M system. A build job contain-ing a challenging geometry is thermally simulated, optimized, and manufac-tured. To verify its suitability for an Additive Manufacturing Service Platform (AMSP), the identical production file is transferred to a PBF-LB/M system of another manufacturer. Finally, the achieved quality level of the build job is evaluated via 3D scanning. This evaluation is carried out in the identical entity of the production file to highlight the versatility of this format and to integrate quality assurance data.
Additive manufacturing (AM) technologies are becoming increasingly important, not only for the manufacture of parts, but also as repair technology that complement existing production technologies. Powder bed fusion of metals by laser beam (PBF-LB/M) combines the freedom in design with high achievable accuracy, making it ideal as a repair approach. However, there are still challenges in adapting process for repair applications. When mounting parts inside PBF-LB/M machines, their real position within the build volume is unknown. One goal of a repair process is to minimize the offset between the base component and the additively manufactured structure to reduce additional rework. For a minimum offset between component and additively manufactured structure, the actual position of the component has to be identified with high precision within the machine coordinate system (MCS). In this work a process setup is presented that allows the actual position of a gas turbine blade to be detected inside a PBF-LB/M machine. A high resolution camera with 65 megapixel is used for this purpose. The presented setup is implemented on a SLM 280 HL PBF-LB/M machine. In addition to the setup, a novel repair workflow using PBF-LB/M is presented. The developed setup and workflow consider inaccuracies in the component and camera mounting, as well as process inaccuracies. This includes keystone distortion correction by homography. The machine setup and workflow are used to repair a real gas turbine blade. Subsequently the offset between the turbine blade and the additivley manufactured structure is validated by 3D scanning the repaired part. The maximum offset is 160 µm. The presented approach can be extended to other geometries and PBF-LB/M machine manufacturers. The high-resolution camera approach is platform independent, which facilates the market penetration of PBF-LB/M repair processes.
AbstractPowder Bed Fusion with Laser Beam of Metals (PBF-LB/M) has gained more industrial relevance and already demonstrated applications at a small series scale. However, its widespread adoption in various use cases faces challenges due to the absence of interfaces to established Manufacturing Execution Systems (MES) that support customers in the predominantly data-driven quality assurance. Current state-of-the-art PBF-LB/M machines utilize communication architectures, such as OPC Unified Architecture (OPC UA), Message Queuing Telemetry Transport (MQTT) and Representational State Transfer Application Programming Interface (REST API). In the context of the Reference Architecture Model Industry 4.0 (RAMI 4.0) and the Internet of Things (IoT), the assets, particularly the physical PBF-LB/M machines, already have an integration layer implemented to communicate data such as process states or sensor values. Missing is an MES component acting as a communication and information layer. To address this gap, the proposed Extract Transform Load (ETL) pipeline aims to extract relevant data from the fabrication of each build cycle down to the level of scan vectors and additionally to register process signals. The suggested data schema for archiving each build cycle adheres to all terms defined by ISO/TC 261—Additive Manufacturing (AM). In relation to the measurement frequency, all data are reorganized into entities, such as the AM machine, build cycle, part, layer, and scan vector. These scan vectors are stored in a runtime-independent format, including all metadata, to be valid and traceable. The resulting machine log represents a comprehensive documentation of each build cycle, enabling data-driven quality assurance at process level.
Hybrid additive manufacturing is becoming increasingly important in the field of additive manufacturing. Hybrid approaches combine at least two different manufacturing processes. The focus of this work is the build-up of geometries onto conventionally manufactured parts using Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M). The hybrid build-up requires a precise position detection system inside the PBF-LB/M machines to determine the exact position of the existing component. For this purpose, high-resolution camera systems can be utilized. However, the use of a camera system is associated with several challenges. The captured images are subject to various distortions of the optical path. Due to these distortions, it is not possible to use the images for measurements and, therefore, it is not possible to calculate the positions of objects. In this study a homography matrix is calculated to correct keystone distortion in the images. Different calibration patterns have been tested for the calculation of the homography matrix. The influence of the number of calibration points on the precision of position detection of objects is determined. Furthermore, the influence of an additional camera calibration by using ChArUco boards is evaluated. The result is a camera calibration workflow with associated calibration pattern for a precise position detection of parts inside PBF-LB/M machines allowing a hybrid build-up with minimum physical offset between base component and build-up.
Hybrid additive manufacturing is becoming increasingly important in the field of additive manufacturing. Hybrid approaches combine at least two different manufacturing processes. The focus of this work is the build-up of geometries onto conventionally manufactured parts using laser-based powder bed fusion of metals (PBF-LB/M). The hybrid build-up requires a precise position detection system inside the PBF-LB/M machines to determine the exact position of the existing component. For this purpose, high-resolution camera systems can be utilized. However, the use of a camera system is associated with several challenges. The captured images are subject to various distortions of the optical path. Due to these distortions, it is not possible to use the images for measurements and, therefore, it is not possible to calculate the positions of objects. In this study a homography matrix is calculated to correct keystone distortion in the images. Different calibration patterns have been tested for the calculation of the homography matrix. The influence of the number of calibration points on the precision of position detection of objects is determined. Furthermore, the influence of an additional camera calibration by using ChArUco boards is evaluated. The result is a camera calibration workflow with associated calibration pattern for a precise position detection of parts inside PBF-LB/M machines allowing a hybrid build-up with minimum physical offset between base component and build-up.
Additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB/M) are rapidly gaining popularity in repair applications. Gas turbine components benefit from the hybrid repair process as only damaged areas are removed using conventional machining and rebuilt using an AM process. However, hybrid repair is associated with several challenges such as component fixation and precise geometry detection. This article introduces a novel fixturing system, including a sealing concept to prevent powder sag during the repair process. Furthermore, a high-resolution camera within an industrial PBF-LB/M machine is installed and used for object detection and laser recognition. Herein, process related inaccuracies such as PBF-LB/M laser drift is considered by detection of reference objects. This development is demonstrated by the repair of a representative gas turbine blade. The final offset between AM build-up and component is analysed. An approximate accuracy of 160 μm is achieved with the current setup.
Additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB/M) are rapidly gaining popularity in repair applications. Gas turbine components benefit from the hybrid repair process as only damaged areas are removed using conventional machining and rebuilt using an AM process. However, hybrid repair is associated with several challenges such as component fixation and precise geometry detection. This article introduces a novel fixturing system, including a sealing concept to prevent powder sag during the repair process. Furthermore, a high-resolution camera within an industrial PBF-LB/M machine is installed and used for object detection and laser recognition. Herein, process related inaccuracies such as PBF-LB/M laser drift is considered by detection of reference objects. This development is demonstrated by the repair of a representative gas turbine blade. The final offset between AM build-up and component is analysed. An approximate accuracy of 160 μm is achieved with the current setup.
One of the main advantages of additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB/M) is the possibility to manufacture complex near-net-shape components. Therefore, the PBF-LB/M process is becoming increasingly important for the manufacturing and repair of gas turbine blades. Despite the great freedom in design, there are also limitations to the process. Manufacturing overhangs or bridging voids are some of the main challenges. In the conventional PBF-LB/M process, overhangs with angles up to 45° can be manufactured. However, gas turbine blades feature voids for cooling, which have to be bridged when using PBF-LB/M. In this work, different concepts for bridging voids are developed for future application in gas turbine blade repair. For this purpose, a test geometry is derived from the tip area of a gas turbine blade as a reference. By changing the initial geometry of the reference body, different designs for bridging voids are developed based on the PBF-LB/M requirements. Subsequently, these distinct designs are manufactured by PBF-LB/M. The different approaches are compared with respect to their volume increase. In addition, the specimens are visually inspected for warpage, shrinkage and imperfections by overheating. Out of the seven concepts developed, three concepts can be recommended for gas turbine blade repair based on low volume increase, distortion and shrinkage.
One of the main advantages of additive manufacturing (AM) processes such as laser powder bed fusion (PBF-LB/M) is the possibility to manufacture complex near-net-shape components. Therefore, the PBF-LB/M process is becoming increasingly important for the manufacturing and repair of gas turbine blades. Despite the great freedom in design, there are also limitations to the process. Manufacturing overhangs or bridging voids are some of the main challenges. In the conventional PBF-LB/M process, overhangs with angles up to 45° can be manufactured. However, gas turbine blades feature voids for cooling, which have to be bridged when using PBF-LB/M. In this work, different concepts for bridging voids are developed for future application in gas turbine blade repair. For this purpose, a test geometry is derived from the tip area of a gas turbine blade as a reference. By changing the initial geometry of the reference body, different designs for bridging voids are developed based on the PBF-LB/M requirements. Subsequently, these distinct designs are manufactured by PBF-LB/M. The different approaches are compared with respect to their volume increase. In addition, the specimens are visually inspected for warpage, shrinkage and imperfections by overheating. Out of the seven concepts developed, three concepts can be recommended for gas turbine blade repair based on low volume increase, distortion and shrinkage.
Additive manufacturing (AM) in general and laser powder bed fusion (PBF-LB/M) in particular are becoming increasingly important in the field of production technologies. Especially the high achievable accuracies and the great freedom in design make PBF-LB/M interesting for the manufacturing and repair of gas turbine blades. Part repair involves building AM-geometries onto an existing component. To minimise the offset between component and AM-geometry, a precise knowledge of the position of the component in the PBF-LB/M machine is mandatory. However, components cannot be inserted into the PBF-LB/M machine with repeatable accuracy, so the actual position will differ for each part. For an offset-free build-up, the actual position of the component in the PBF-LB/M machine has to be determined. In this paper, a camera-based position detection system is developed considering PBF-LB/M constraints and system requirements. This includes finding an optimal camera position considering the spatial limitations of the PBF-LB/M machine and analysing the resulting process coordinate systems. In addition, a workflow is developed to align different coordinate systems and simultaneously correct the perspective distortion in the acquired camera images. Thus, position characteristics can be determined from images by image moments. For this purpose, different image segmentation algorithms are compared. The precision of the system developed is evaluated in tests with 2D objects. A precision of up to 30μm in translational direction and an angular precision of 0.021∘ is achieved. Finally, a 3D demonstrator was built using this proposed hybrid strategy. The offset between base component and AM-geometry is determined by 3D scanning and is 69μm.