The main objective of ISO/TC 261 is to standardise the processes of additive manufacturing, the process chains (data, materials, processes, hard- and software, applications), test procedures, quality parameters, supply agreements, environment, health and safety, fundamentals and vocabularies. This section provides readers with news regarding standardisation efforts of ISO/TC 261.
Multi-laser powder bed fusion (M-LPBF) systems, characterized by large building envelopes and multiple lasers, have become increasingly prevalent in industrial additive manufacturing. The increased build rate of these machines relative to single laser systems offers the potential to drastically reduce the cost per part. However, one major challenge with M-LPBF is that often multiple lasers are needed to build a single part. Therefore, the exposure area must be divided, and the segments must be assigned to different lasers. Various strategies for splitting the exposure area are available, but their influence on part quality remains yet to be determined. This study aims to investigate the impact of the fixed exposure border, variable exposure border, and segmented stripes M-LPBF exposure strategy on the part quality of samples built with two lasers compared to samples built with one laser. Therefore, the porosity, microstructure, and mechanical properties of IN718 samples were evaluated. The research shows that the fixed exposure border strategy resulted in a localized increase in porosity at the border or within the overlap area between segmented exposure areas, as well as grain coarsening and increased texture within the remelted areas. The variable exposure strategy also resulted in a higher porosity level, but the microstructure was more like the single laser reference sample. These changes resulted in a minor decrease in mechanical properties. The segmented stripes strategy exhibited a similar microstructure and mechanical properties to the single laser reference, making it the most suitable approach for M-LPBF in industrial applications.
AbstractDiscontinuous processes face common tasks when implementing modeling and optimization techniques for process optimization. While domain data may be unequal, knowledge about approaches for each step toward the solution, e.g., data gathering, model reduction, and model optimization, may be useful across different processes. A joint development of methodologies for machine learning methods, among other things, ultimately supports fast advances in cross-domain production technologies. In this work, an overview of common maturation stages of data-intensive modeling approaches for production efficiency enhancement is given. The stages are analyzed and communal challenges are elaborated. The used approaches include both physically motivated surrogate modeling as well as the advanced use of machine learning technologies. Apt research is depicted for each stage based on demonstrator work for diverse production technologies, among them high-pressure die casting, surface engineering, plastics injection molding, open-die forging, and automated tape placement. Finally, a holistic and general framework is illustrated covering the main concepts regarding the transfer of mature models into production environments on the example of laser technologies.Increasing customer requirements regarding process stability, transparency and product quality as well as desired high production efficiency in diverse manufacturing processes pose high demands on production technologies. The further development of digital support systems for manufacturing technologies can contribute to meet these demands in various production settings. Especially for discontinuous production, such as injection molding and laser cutting, the joint research for different technologies helps to identify common challenges, ranging from problem identification to knowledge perpetuation after successfully installing digital tools. Workstream CRD-B2.II “Discontinuous Production” confronts this research task by use case-based joint development of transferable methods. Based on the joint definition of a standard pipeline to solve problems with digital support, various stages of this pipeline, such as data generation and collection, model training, optimization, and the development and deployment of assistance systems are actively being researched. Regarding data generation, e.g., for the high-pressure die-casting process, data acquisition and extraction approaches for machines and production lines using OPC UA are investigated to get detailed process insights. For diverse discontinuous processes and use cases, relevant production data is not directly available in sufficient quality and needs to be preprocessed. For vision systems, ptychographic methods may improve recorded data by enhancing the picture sharpness to enable the usage of inline or low-cost equipment to detect small defects. Further down the pipeline, several research activities concern the domain-specific model training and optimization tasks. Within the realm of surface technologies, machine learning is applied to predict process behavior, e.g., by predicting the particle properties in plasma spraying process or plasma intensities in the physical vapor deposition process. The injection molding process can also be modeled by data-based approaches. The modeling efficiency based on the used amount of data can furthermore be effectively reduced by using transfer learning to transfer knowledge stored in artificial neural networks from one process to the next. Successful modeling approaches can then be transferred prototypically into production. On the examples of vision-based defect classification in the tape-laying process and a process optimization assistance system in open-die forging, the realization of prototypical support systems is demonstrated. Once mature, research results and consequent digital services must be made available for integrated usage in specific production settings using relevant architecture. By the example of a microservice-based infrastructure for laser technology, a suitable and flexible implementation of a service framework is realized. The connectivity to production assets is guaranteed by state-of-the-art communication protocols. This chapter illustrates the state of research for use-case-driven development of joint approaches.
combination of a Multi-Phase Field model and an Orientation Field is proposed to describe the microstructure evolution induced by laser–material interaction in Laser Powder Bed Fusion (LPBF). The relevant phase transformations are covered by nucleation and growth processes driven by free enthalpy. An empiric correction is applied to the phase-field approach to reduce the grid resolution required for the numerical simulation. This contribution focuses on the LPBF processing of the titanium alloy Ti–6Al–4V. Particularly, the transition between -titanium and melt is emphasized. The results are discussed and compared to measurements. A numerical correction can be applied to the MPF model to avoid a mesh introduct anisotopy in the crystal growth. The simulation shows the -phase crystal growth with the (1 0 0) direction into the melt. The model for the phase transformation from -phase to -phase agrees with the XRD measurements.
