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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.
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.
Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M) faces challenges in reproducibility and quality assurance, even for widely applied alloys like AlSi10Mg. This work introduces a digital provenance framework for PBF-LB/M, showcased through the EOS M 300–4 multi-laser machine. An Extract, Transform, Load (ETL) pipeline autonomously captures machine data, including scan vectors as well as process signals, and organizes them into a Digital Shadow (DS). The DS is further extended by external data sources, such as Melt Pool Monitoring (MPM), to enable comprehensive analysis and root cause identification. This approach ensures continuous data representation and facilitates the development of new quality metrics. Moreover, the framework enhances quality assurance and traceability, supports compliance with industry standards, and improves productivity. It also enables more precise cost calculations and predictive maintenance. By addressing these challenges, the framework is essential for advancing PBF-LB/M in industrial applications, achieving greater consistency and scalability in production.
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.
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.
In the field of additive manufacturing, the ability to uniquely identify and authenticate parts is crucial for certification, logistics, and anti-counterfeiting efforts. This study introduces a novel methodology that leverages the intrinsic microstructural features of additively manufactured components for their identification, authentication, and traceability. Unlike traditional tagging methods, such as embedding QR codes on the surface or within the volume of parts, this approach requires no alteration to the printing process, as it utilizes naturally occurring microstructural characteristics. The proposed workflow involves the analysis of 3D micro-computed tomography data to identify specific voids that meet predefined identification criteria. This method is demonstrated on a batch of 24 parts manufactured with identical process parameters, proving capable of achieving unambiguous identification and authentication. By establishing a tamper-proof link between the physical part and its digital counterpart, this methodology effectively bridges the physical and digital realms. This not only enhances the traceability of additively manufactured parts but also provides a robust tool for integrating digital materials, parts databases, and product passports with their physical counterparts.
Data-driven quality assurance and reproducibility are critical for advancing the industrial maturity of Powder Bed Fusion with Laser Beam of Metals (PBF-LB/M). This study addresses the extraneous variance in scan vectors that arises even for identical cross sections of nominally identical components but located at different positions on the build platform. This inherent variance, common across most PBF-LB/M machines, is caused by scan vector computation relative to the machine coordinate origin and subsequent projection of the resulting pattern onto individual component cross sections. In this work, scan vectors are computed still following conventional slicing, but relative to the workpiece origin of each component, using an EOS M 300-4. This digital preprocessing approach homogenizes fabrication conditions, ensuring that anomalies, such as scan vector overlaps, occur consistently across identical components. The impact during fabrication is assessed through powder bed imaging, melt pool monitoring, and operational data from the multilaser PBF-LB/M machine. Components are manufactured from AlSi10Mg for each scan vector computation origin and subsequently qualified using x-ray computed tomography, optical coordinate measurement, and optical surface measurement. A comprehensive evaluation is conducted, comparing the results in terms of component density, geometric accuracy, and surface roughness to those obtained using conventional preprocessing. Based on these findings, practical recommendations are provided, focusing on achieved quality criteria to identify potential drawbacks, while also considering the life cycle analysis of fabrication. Finally, the study emphasizes the significance of consistent scan vector provisioning for identical components placed at different build platform positions, assuming no roll or pitch during nesting.
In Additive Manufacturing (AM), precise rigid three-dimensional (3D) image registration between X-ray Computed Tomography (XCT) scans and Computer-Aided Design (CAD) models is an important step for the quantification of distortions in produced parts. Given the absence of standardized benchmarks for image registration in AM, we introduce a gold standard for 3D image registration, using a reference base plate beneath the build structure. This gold standard is used to quantify the accuracy of rigid registration, with a proof of concept demonstrated in PBF-LB/M. In this study, we conduct a comparative analysis of various rigid 3D registration methods useful for quality assurance of PBF-LB/M parts including feature-based, intensity-based, and point cloud-based approaches. The performance of each registration method is evaluated using measures of alignment accuracy based on the gold standard and computational efficiency. Our results indicate significant differences in the efficacy of these methods, with point cloud based Coherent Point Drift (CPD) showing superior performance in both alignment and computational efficiency. The rigidly registered 3D volumes are used to estimate the deformation field of the printed parts relative to the nominal CAD design using Digital Volume Correlation (DVC). The quality of the estimated deformation field is assessed using the Dice score metric. This study provides insights into methods for enhancing the precision and reliability of AM process.
The layerwise geometry build-up of additive manufacturing (AM) enables the possibility of in-situ process monitoring. The objective is the detection of irregularities during the build cycle, ensuring component quality and process stability. Focus of this work is the visual in-situ monitoring of the process of powder bed fusion with laser beam of metals (PBF-LB/M). Current state of the art visual monitoring systems for PBF-LB/M are limited by low resolution, allowing the detection of gross flaws. In this work a 65 Mpixel high-resolution monochrome camera is integrated into a commercial PBF-LB/M machine enabling a spatial resolution of approx. 17.2 µm/Pixel. The observed inhomogeneities are clustered into directly detectable irregularities, and indirectly detectable irregularities that can be inferred from the surface. In parallel, two different illumination techniques are realized in the process chamber and compared. The impact of the distinct illumination technique, direct light and dark field, on the identification of irregularities is evaluated.
Parameter studies are a common step in selecting process parameters for laser powder bed fusion of metals (PBF-LB/M). Density cubes are commonly used for this purpose. Density cubes manufactured with varied process parameters can exhibit distinguishable surface structures visible to the human eye. The layer-wise process enables such surface structures to be detected during manufacturing. However, industrial visual in situ monitoring systems for PBF-LB/M currently have limited resolution and are incapable of reliably capturing small differences in the surface structures. In this work, a 65 MPixel high-resolution monochrome camera was integrated into an industrial PBF-LB/M machine together with a high-intensity LED (light-emitting diode) bar. Post-exposure images were taken to analyse differences in light reflection of fused areas. It is revealed that the grey-level co-occurrence matrix can be used to quantify the visual surface structure of nickel-based superalloy Inconel®939 density cubes per layer. The properties of the grey-level co-occurrence matrix correlate to the energy input and the resulting porosity of density cubes. Low-energy samples containing lack of fusion flaws show an increased contrast in the grey-level co-occurrence matrix compared to specimens with optimal energy input. The potential of high-resolution images for quality assurance via in situ process monitoring in PBF-LB/M is further discussed.
