1,720,970 research outputs found
The intelligent recoater: a new solution for in-situ monitoring of geometric and surface defects in powder bed fusion
Get PDFPowder bed homogeneity, contaminations, and printed surface quality are crucial in powder bed-based AM processes to obtain a defect-free part, but the scale at which these defects are seen is not compatible with the resolution of current industrial image-based monitoring solutions. In this work, we explore the implementation of an optical scanner in an industrial laser powder bed fusion (L-PBF) machine to detect powder bed and part-related defects. The sensor is mounted ”parasitically” on the recoater and exploits its movement to scan across the build platform before and after powder deposition to obtain high-resolution images. The acquisition seamlessly integrates with the process, without delaying the production as the acquisition occurs in parallel with the new layer deposition. The system was used to monitor test builds as well as longer builds (1000+ layers) to prove its robustness to the challenging L-PBF chamber environment. The in-situ powder bed images of the new monitoring system were compared to the acquisitions of a standard external camera setup. The improved image quality and resolution of the new system were demonstrated on both large-scale (>1 mm) and small-scale features. The new system proved to be capable of capturing printed surface topography anomalies and powder bed contaminations (<100 μm), opening a whole new range of possibilities for detecting small-scale defects via in-situ monitoring
Towards real-time in-situ monitoring of hot-spot defects in L-PBF: a new classification-based method for fast video-imaging data analysis
Get PDFThe increasing interest towards additive manufacturing (AM) is pushing the industry to provide new solutions to improve process stability. Monitoring is a key tool for this purpose but the typical AM fast process dynamics and the high data flow required to accurately describe the process are pushing the limits of standard statistical process monitoring (SPM) techniques. The adoption of novel smart data extraction and analysis methods are fundamental to monitor the process with the required accuracy while keeping the computational effort to a reasonable level for real-time application. In this work, a new framework for the detection of defects in metal additive manufacturing processes via in-situ high-speed cameras is presented: a new data extraction method is developed to efficiently extract only the relevant information from the regions of interest identified in the high-speed imaging data stream and to reduce the dimensionality of the anomaly detection task performed by three competitor machine learning classification methods. The defect detection performance and computational speed of this approach is carefully evaluated through computer simulations and experimental studies, and directly compared with the performance and computational speed of other existing methods applied on the same reference dataset. The results show that the proposed method is capable of quickly detecting the occurrence of defects while keeping the high computational speed that would be required to implement this new process monitoring approach for real-time defect detection
Limitations of the inherent strain method in simulating powder bed fusion processes
Get PDFProcess optimization has always been a crucial step for effective usage of metal additive manufacturing (AM) processes: it consists in establishing quantitative relations between final part's characteristics and process parameters to find their optimal combination and obtain a fully functional mechanical component. Experimental investigation techniques are usually employed for this purpose but they can be extremely expensive and time-consuming, especially when the output of the process depends on a large number of parameters, like for AM. Numerical simulation could represent an alternative solution: by reproducing the real process characteristics, a simulation could provide useful insights, allowing to evaluate the performance of the process for different parameter combinations without relying exclusively on expensive experimental campaigns. In this work, a finite element AM simulation based on the inherent strain (IS) method was developed and the prediction performance in terms of part's residual deformation was evaluated by comparing the numerical results with the measurements carried out on an experimental campaign. A new model calibration approach for prediction improvement was also implemented and it allowed to discover an unexpected behaviour of the model that strongly affects the validity of this method for AM simulation
On the Use of Generative AI to Support In-Line Process Monitoring in Zero-Defect Manufacturing
Get PDFIn recent years, the integration of new Artificial Intelligence (AI) techniques and capabilities has emerged as one of most promising research fields to aid the industrial development of smart and zero-defect manufacturing solu-tions. This study explores the potential of generative AI in this field and re-views novel opportunities enabled by generative AI methods, and Generative Adversarial Networks (GANs) in particular, to aid the generation of aug-mented datasets including realistic representations of anomalous process pat-terns. The result is an effective AI framework to learn specific defect features from real data, and reproduce them in an extended way, leading to synthetic but realistic image data that could be used to enhance defect detection and classification performances. The paper reviews the benefits and open chal-lenges associated with the implementation of these techniques, including state-of-the-art examples and real case studies in Additive Manufacturing
Towards Digital Twin in Additive Manufacturing: A Multi-fidelity Approach for Enhancing LPBF Process Modeling with In-situ Data
No full textProcess modeling for additive manufacturing is essential to enable fully digital optimization workflows and reduce dependence on experimental studies to identify design flaws such as overheating, excessive thermal stress, and deformation. However, accurate track-level predictions in Laser Powder Bed Fusion (LPBF) demand significant computational resources and detailed material parameters, which are difficult to measure across the broad temperature ranges involved in the process. While post hoc calibration has been widely used to enhance model accuracy by aligning predictions with measurable outcomes, it still relies on manufacturing physical parts for validation. This study bridges the gap between physics-based simulation models and real-world processes by developing a multi-fidelity approach that incorporates in-situ data to enhance model accuracy. By employing machine learning to integrate simulations with real-time observations of temperature evolution, the multi-fidelity model improves predictive capabilities while reducing the experimental effort for first-time right production. This represents the first step towards the development of an AM digital twin for dynamically adjusting process parameters and controlling the printing process
Advancing Sustainable Additive Manufacturing in Space via In-Situ Data Mining: Challenges and Future Prospects
No full textAdditive Manufacturing (AM) technologies have introduced a ground-breaking production paradigm to address the advancing challenges of the rapidly expanding space economy. A primary challenge confronting AM is ensuring first-time-right production, a critical factor for sustainability. This challenge is magnified when dealing with large scales, new materials, metamaterials, innovative shapes and varied environmental conditions. Achieving this target not only reduces waste and resource consumption, but also addresses the space industry's drive for efficiency. Streamlining qualification and certification processes is essential for reducing lead times and costs, a necessity in the face of the sector's increasing competitiveness.
This paper provides an overview of some recent advances in the field of in-situ sensing and monitoring technologies for AM in the space sector. Presented methods rely on in-situ big data streams gathered in metal powder bed fusion (PBF) via high-resolution imaging of every layer, developed and validated within the ESA-funded IAMSPACE project, for effective in-line inspection and rapid anomaly detection. Through real-world case studies, the paper showcases the potential of these innovative approaches to promote sustainable manufacturing practices within the space industry
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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