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Additive Manufacturing using Metal Pilot Line

Deliverables

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.

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.

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

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Publications

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, 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, 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, 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, 870, 2021, Page(s) 159329, ISSN 0925-8388
Publisher: Elsevier BV
DOI: 10.1016/j.jallcom.2021.159329

In-situ electron optical measurement of thermal expansion in electron beam powder bed fusion

Author(s): Christopher Arnold, Carolin Körner
Published in: Additive Manufacturing, 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, 64/5, 2021, Page(s) 425-433, ISSN 0032-5899
Publisher: Maney Publishing
DOI: 10.1080/00325899.2021.1928997

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