Deliverables Websites, patent fillings, videos etc. (2) Project print media, brochure, leaflets available Design and print brochures and leaflets to inform about MANUELA. Project handbook Preparation of handbook of metal AM components with prototype specifications and indicating main values comparing AM and other manufacturing technologies (if at all available). This handbook will serve as the promotional material for the metal AM in general and pilot line service capacities in particular. The handbook will be prepared in printed form as well as electronic form which will be integrated in the MANUELA website. Documents, reports (9) Analytics toolbox specifications T1.4 will deliver the specifications for the dashboard enabling the pilot line to process data generated by WP3 in respect of virtual representation of the AM sequence. It will cover • Big data, Data mining and Machine learning • Multi-scale and Multi-physics simulation tools • Real-time and continuous data feedback By providing the backbone for data analysis and comparison, along the coupling of virtual information and physical feedback, it will heavily support the collaboration of T3.3 and T5.4. it will comprise It will therefore sustain generation of the data knowledge, required by T6.1 to produce artificial intelligence feeding the pilot line. This task will focus on the definition of an agnostic data management platform that will enable the acquisition of information independently of its virtual or physical source/format. CSEM will assure that in the analytics toolbox the needs for the use cases are addressed and will also be in charge of the specifications related and interfaces to the Manuela Dashboard. For validating the dashboard toolbox, three reference sample parts will be specified. Online monitoring systems calbrated and tested CHALM will adopt five on-line process monitoring systems, allowing to perform on-line monitoring of number of key aspect during the whole build process: • continuous monitoring of the key process properties as laser power and scanner, temperature (build platform and process chamber), cooling systems, electronic, gas circulation systems, etc.; • powder bed monitoring – monitoring of the powder recoating with the integrated camera; • process atmosphere control based on continuous analysis of the oxygen concentration close to the powder bed with sensitivity around 10 ppm and automatic initiation of the additional purging with the process gas to assure process gas purity; • monitoring of the energy application in the melt pool– also called optical tomography –based on utilization of the sCMOS camera that allows to detect overall fusion and cooling behaviour • real-time melt pool monitoring, based on measurement of the light emissions from the melt pool. Possibility to display data in 2D and 3D makes it possible to detect any abnormalities and hence draw conclusion regarding the quality of the final component. Systems will need to be developed for the specific materials and in some cases design of interest and will be validated utilizing extensive materials characterisation. Post AM specifications This task will establish the specifications on the automated post-process configuration to be set up as part of the MANUELA pilot line. Necessary post-processing steps and functionality will be determined based on the use case studies. IVF will lead the work to determine the specifications of an automated supply chain and analyze the potential environment, health and safety risks associated with the post-processing. CHALM, EOS, POLITO and FAU will support the task by establishing the demands on automation at the 3D printer level including for example removal of build platform and machine cleaning. MSC and CU will establish the requirements on communication between the post-processing supply chain and the rest of the pilot line as well as the need for data storage. Within the post AM specifications CSEM will represent the interest of all the use cases assuring that their requirements from T1.1 can be met. Pilot line specifications Specifications of the pilot line will be established based on the use case requirements description provided in T1.1. Selection of the specific additive manufacturing technology will be performed based on an available database containing general properties of the materials, process and process parameters as well as on-line monitoring systems specifications and requirements. Based on the technology selection performed and initial design of AM components, necessary input for the multi-physics simulation for the work in WP3 will be determined. Material selection as well as required tailoring of the powder properties and composition, depending on the user case requirements and AM technology selected, will be performed in collaboration with the material supplier. Determination of the process parameters to assure required properties of the component will be performed together with the hardware supplier. Taking into account that the pilot line will utilize next generation on-line monitoring systems requiring development of the process monitoring parameters, specifications of the such a monitoring system will be performed to assure robustness of the pilot line as well as required component properties and performance. Furthermore, necessary test bars and components will be defined. Material testing (2) T44 correlates the development of the additive manufacturing process and the online process monitoring with the resulting materials properties FAU will apply NDT and DT for material analysis NDT comprises computed tomography for fault analysis resonance frequency analysis for determination of the elastic modulus and laserflash analysis for determination of the thermal diffusivity copper based materials In addition metallography study and dedicated microscopy analysis SEMEDXEBSDFIB and TEM will be performed when required for detailed microstructure analysis Mechanical testing of the samples TS impact test hardness etc will be alsoperformed in support of the T41 T43 Material testing (1) T4.4 Material testing (FAU, CHALM, HAB, METAS, EOS, POLITO) T4.4 correlates the development of the additive manufacturing process and the on-line process monitoring with the resulting materials properties. FAU will apply NDT and DT for material analysis. NDT comprises computed tomography for fault analysis, resonance frequency analysis for determination of the elastic modulus and laser flash analysis for determination of the thermal diffusivity (copper based materials). In addition, metallography study and dedicated microscopy analysis (SEM/EDX/EBSD+FIB and TEM) will be performed when required for detailed microstructure analysis. Mechanical testing of the samples (TS, impact test, hardness, etc.) will be also performed in support of the T4.1 - T4.3. Development and calibration of the on-line process monitoring for material of interest (1) T4.3 Development and calibration of the on-line process monitoring for material of interest (FAU,CHALM, POLITO, EOS) T4.3.1 Development and calibration of the on-line process monitoring for material of interest for EBM process (FAU) FAU will employ and assess the new electron optical observation (ELO) tool for process observation and fault detection. The spatial resolution of the ELO will be determined with the help of specific calibration plates. The scanning strategy and parameters (beam current and exposure time) for taking ELO information will be adapted to and optimized for the different materials of interest. For calibration, the ELO information will be directly compared with microstructural investigations and data from computed tomography. Raw material and process qualification The use of powder as a raw material constitutes an important strategy in metal AM as the properties of the powder will strongly affect the properties of the final AM component as well as robustness of the AM process The aim of this task is to establish qualification characteristics of powders for materials of interest for the user cases in dependence on process selected requirements to the component as well as cost factor Hence powder selected based on the materials and process selections in WP2 will be ordered from the materials suppliers in the project and qualified depending on the process and component requirements Powder bulk and surface chemistry as well as physical properties of the powders powder size powder particles distribution flow rheological properties etc will be evaluated and documented for each process and component Process parameters will be optimised for the powders utilized in WP4 This task will also assure consistent powder properties during the whole chain including pilot line validation and use case component manufacturing WP6 and WP7 Database of T45 will be completed accordingly Project dissemination and communication strategy Identify suitable channels to disseminante project results and to communicate project activities and results to the relvant target audiences. Other (1) GUI for design & optimization component of MANUELA’s T34 Design and optimization interface MSC CSEMT34 will develop the GUI dealing as part of the MANUELA Dashboard with the simulation tool and manufacturing feedback The GUI will empower end users with means to optimize the process parameters of the AM pilot line based on previous manufacturing feedback of the virtual pilotline This will lead to a rightfirsttimephysical part on the real AM pilot lineUserfriendliness and accessibility to nonexperts in the domains of CAD optimization and behavioural representation of design to manufacture process are key requirements for the GUI specifications As such seamless simulation knowledge for nonexperts will be provided in order to optimize AM processes A collaboration between CSEM and MSC will be performed on the GUI design making sure that all relevant process parameters are properly addressed and that the outputs are represented in a user friendly and accessible way in the GUI Publications Peer reviewed articles (24) Increasing productivity of laser powder bed fusion manufactured Hastelloy X through modification of process parameters Author(s): Claudia Schwerz, Fiona Schulz, Elanghovan Natesan, Lars Nyborg Published in: Journal of Manufacturing Processes, Issue Volume 78, 2022, ISSN 2212-4616 Publisher: Elsevier DOI: 10.1016/j.jmapro.2022.04.013 Effect of layer thickness on spatters oxidation of Hastelloy X alloy during powder bed fusion-laser beam processing Author(s): Ahmad Raza, Claudia Schwerz, Camille Pauzon, Lars Nyborg, Eduard Hryha Published in: Powder Technology, Issue Volume 422, 15 May 2023, 118461, 2023, ISSN 0032-5910 Publisher: Elsevier BV DOI: 10.1016/j.powtec.2023.118461 A neural network for identification and classification of systematic internal flaws in laser powder bed fusion Author(s): Claudia Schwerz, Lars Nyborg Published in: CIRP Journal of Manufacturing Science and Technology, Issue Volume 37, 2022, ISSN 1878-0016 Publisher: Elsevier DOI: 10.1016/j.cirpj.2022.02.010 Optimizing the parameters of long short-term memory networks using the bees algorithm Author(s): Alamri, N. M., Packianather, M. and Bigot Published in: Applied Sciences, Issue 13(4), 2023, ISSN 2076-3417 Publisher: MDPI DOI: 10.3390/app13042536 The effect of powder reuse on the surface chemical composition of the Scalmalloy powder in Powder Bed Fusion – Laser Beam process Author(s): Alessandra Martucci, Pui Lam Tam, Alberta Aversa, Mariangela Lombardi, Lars Nyborg Published in: Surface & Interface Analysis, 2022, ISSN 1096-9918 Publisher: Wiley Analytical Science DOI: 10.1002/sia.7176 Corrosion behaviour of additively manufactured 316L and CoCrNi Author(s): Sri Bala Aditya Malladi, Pui Lam Tam, Yu Cao, Sheng Guo, Lars Nyborg Published in: Surface and Interface Analysis, Issue 30 Jan, 2023, 2023, ISSN 1096-9918 Publisher: Wiley DOI: 10.1002/sia.7200 In-situ detection of redeposited spatter and its influence on the formation of internal flaws in laser powder bed fusion Author(s): Claudia Schwerz, Ahmad Raza, Xiangyu Lei, Lars Nyborg, Eduard Hryha, Håkan Wirdelius Published in: Additive Manufacturing, Issue 47, 2021, Page(s) 102370, ISSN 2214-8604 Publisher: Elsevier BV DOI: 10.1016/j.addma.2021.102370 Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing Author(s): Chao Liu, Léopold Le Roux, Ze Ji, Pierre Kerfriden, Franck Lacan, Samuel Bigot Published in: Procedia Computer Science, Issue 176, 2020, Page(s) 2586-2595, ISSN 1877-0509 Publisher: Elsevier DOI: 10.1016/j.procs.2020.09.314 Automatised quality assessment in additive layer manufacturing using layer-by-layer surface measurements and deep learning Author(s): Léopold Le Roux, Chao Liu, Ze Ji, Pierre Kerfriden, Daniel Gage, Felix Feyer, Carolin Körner, Samuel Bigot Published in: Procedia CIRP, Issue 99, 2021, Page(s) 342-347, ISSN 2212-8271 Publisher: Elsevier DOI: 10.1016/j.procir.2021.03.050 A study on the microstructure and mechanical properties of the Ti-6Al-2Sn-4Zr-6Mo alloy produced via Laser Powder Bed Fusion Author(s): Alessandro Carrozza, Alberta Aversa, Paolo Fino, Mariangela Lombardi Published in: Journal of Alloys and Compounds, Issue 870, 2021, Page(s) 159329, ISSN 0925-8388 Publisher: Elsevier BV DOI: 10.1016/j.jallcom.2021.159329 Surface chemical analysis of spatter particles generated in laser powder bed fusion of Hastelloy X in process atmospheres with high and low oxygen content Author(s): Claudia Schwerz, Yu Cao, Lars Nyborg Published in: Surface and Interface Analysis, Issue 2 Feb, 2023, 2023, ISSN 1096-9918 Publisher: Wiley DOI: 10.1002/sia.7202 Predicting the porosity in selective laser melting parts using hybrid regression convolutional neural network Author(s): Alamri, N. M., Packianather, M., Bigot S. Published in: Applied Sciences, Issue 12(24), 2022, ISSN 2076-3417 Publisher: MDPI DOI: 10.3390/app122412571 Electron-Optical In Situ Imaging for the Assessment of Accuracy in Electron Beam Powder Bed Fusion Author(s): Christopher Arnold, Christoph Breuning, Carolin Körner Published in: Materials, Issue 14(23), 2021, ISSN 1996-1944 Publisher: MDPI Open Access Publishing DOI: 10.3390/ma14237240 Laser-based Powder Bed Fusion of dispersion strengthened CoCrNi by ex-situ addition of TiN Author(s): Sri Bala Aditya Malladi; Laura Cordova; Sheng Guo; Lars Nyborg Published in: Procedia CIRP (22128271) vol.111(2022), 2022, ISSN 0007-8506 Publisher: Hallwag AG DOI: 10.1016/j.procir.2022.08.168 In-situ detection of stochastic spatter-driven lack of fusion: Application of optical tomography and validation via ex-situ X-ray computed tomography Author(s): Claudia Schwerz, Benjamin A. Bircher, Alain Küng, Lars Nyborg Published in: Additive Manufacturing, Issue Vol. 27, 25 June, 2023, 103631, 2023, ISSN 2214-8604 Publisher: Elsevier BV DOI: 10.1016/j.addma.2023.103631 In-situ electron optical measurement of thermal expansion in electron beam powder bed fusion Author(s): Christopher Arnold, Carolin Körner Published in: Additive Manufacturing, Issue 46, 2021, Page(s) 102213, ISSN 2214-8604 Publisher: Elsevier BV DOI: 10.1016/j.addma.2021.102213 Evaluation of pore re-opening after HIP in LPBF Ti–6Al–4V Author(s): Topi Kosonen, K. Kakko, N. Raitanen Published in: Powder Metallurgy, Issue 64/5, 2021, Page(s) 425-433, ISSN 0032-5899 Publisher: Maney Publishing DOI: 10.1080/00325899.2021.1928997 Predicting laser powder bed fusion defects through in-process monitoring data and machine learning Author(s): Shuo Feng, Zhuoer Chen, Benjamin Bircher, Ze Ji, Lars Nyborg, Samuel Bigot Published in: Materials & Design, Issue Volume 2222, 2022, ISSN 1873-4197 Publisher: Elsevier DOI: 10.1016/j.matdes.2022.111115 Linking In Situ Melt Pool Monitoring to Melt Pool Size Distributions and Internal Flaws in Laser Powder Bed Fusion Author(s): Claudia Schwerz, Lars Nyborg Published in: Metals, Issue 11, 2021, ISSN 2075-4701 Publisher: MDPI DOI: 10.3390/met11111856 Towards customized heat treatments and mechanical properties in the LPBF-processed Ti-6Al-2Sn-4Zr-6Mo alloy Author(s): Alessandro Carrozza, Alberta Aversa, Paolo Fino, Mariangela Lombardi Published in: Materials & Design, Issue Volume 215, 2022, ISSN 1873-4197 Publisher: Elsevier DOI: 10.1016/j.matdes.2022.110512 Electron-optical in-situ metrology for electron beam powder bed fusion: calibration and validation Author(s): Christopher Arnold, Carolin Körner Published in: Measurement Science and Technology, Issue Volume 33, Number 1, 2021, ISSN 1361-6501 Publisher: IOP Publishing DOI: 10.1088/1361-6501/ac2d5c The Influence of Processing Parameters on the Al-Mn Enriched Nano-Precipitates Formation in a Novel Al-Mn-Cr-Zr Alloy Tailored for Power Bed Fusion-Laser Beam Process Author(s): A. Martucci, B.Mehta, M.Lombardi, L.Nyborg Published in: Metals, Issue 12(8), 2022, ISSN 2075-4701 Publisher: MDPI DOI: 10.3390/met12081387 Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems Author(s): Chao Liu, Léopold Le Roux, Carolin Körner, Olivier Tabaste, Franck Lacan, Samuel Bigot Published in: Journal of Manufacturing Systems, 2020, ISSN 0278-6125 Publisher: Elsevier BV DOI: 10.1016/j.jmsy.2020.05.010 Impact of powder recoating speed on built properties in PBF-LB process Author(s): Laura Cordova, Zhuoer Chena Published in: Procedia CIRP, Issue Volume 115, 2022, ISSN 2212-8271 Publisher: Elsevier DOI: 10.1016/j.procir.2022.10.061 Conference proceedings (2) Towards data-driven additive manufacturing processes Author(s): Vincenzo Gulisano; Marina Papatriantafilou; Zhuoer Chen; Eduard Hryha; Lars Nyborg Published in: Proceedings of the 23rd International Middleware Conference Industrial Track, Issue 3, 2022, Page(s) 43-49, ISBN 978-1-4503-9340-9 Publisher: Middleware DOI: 10.1145/3564695.3564778 Optimization of convolutional neural network topology and training parameters using Bees Algorithm Author(s): Alamri, N. M., Packianather, M. and Bigot Published in: IEEE 2nd International Symposium on Sustainable Energy, 2022, Page(s) 1-6 Publisher: IEEE DOI: 10.1109/isssc56467.2022.10051487 Searching for OpenAIRE data... There was an error trying to search data from OpenAIRE No results available
