Additive Manufacturing using Metal Pilot Line

Rezultaty

Project print media, brochure, leaflets available

Design and print brochures and leaflets to inform about MANUELA.

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.

Professional video of the pilot line novelty and offering

Production of a professional video of the pilot line novelty

Material properties databases utilizing tailored

45 Material properties databases utilizing tailored 35T45 Establishment of the material and process database CHALM FAU EOS HAB CU POLITO Based on the materials and processes established in T41 43 material testing will be performed in the based on certified standards andor in the certified laboratories and standard materialsprocess databases will be created for the materials of interest Developed database will be provided with the open access to the users in the validation of their use cases per chosen technology LPBF or EBM The database is expanded owing to expansion of material portfolio within WP4

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.

Post processing and surface finishing qualification protocol

The task addresses the work to prepare process performance qualification protocols for post processing and surface finishing The protocol is vital in maintaining the production quality by recording and reviewing essential conditions testing and expected manufacturing outcome of the manufacturing process The protocol will includeManufacturing conditions as operating parameters equipment limits component inputsData to be collected and analysedTests to be performed and acceptance criteria for each processing stepA sampling planStatus of validation of analytical methodsReview and approvalThe solution will be electronically and connected to a server where the quality protocol can be reached anywhere and at any time by authorized persons using a unique part ID information

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.

Consortium exploitations and operational plans

The aim of this task is to evaluate the collective impact potential of the consortium by evaluating the market potential and to determine product opportunities in relation to the customerproduct requirements throughout the course of the project The input will be from all the task consortium participants with special attention to the industrial and economic impact in the EU following the different innovation opportunities along the value chain The market intelligence collected will be used to evaluate different business models and create a business plan for a sustainable offering of the services This business plan will take into account eg the number of potential customers as identified the successrate of the services offered and relevant information from the launching customer cases and feasibility studies

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.

Information management and process interoperability

T54 Information management and process interoperability CU IVF MSC OSAIThis task addresses the communication of information in the AM supply chain as well as the storage of generated diagnostic and quality assurance data during the post processing The data will be stored as to be readily available for data analytics and process feedback in T61 A wide range of data will be collected throughout the pilot line both online and offline eg machining parameters on the machine process monitoring localized temperature profiles acoustic information metrology station digital images This task aims at identifying and characterizing the range of data types standard inputs eg manufacturing process and parts quality data and standard outputs eg diagnostics results that will be produced at applying data preprocessing techniques that will facilitate the knowledge extraction processes T33 and data analytics T61 and at providing data storage solutions andstandard interfaces for both input and output data to enable efficient feedback at different stages of the pilot linesIn those cases when different features are identified to exist within the specimens using realtime insitu techniques highaccuracy NDT will be performed on reference samples using an offline approach While a specific database will be implemented for the pilot lines developed in the project the aim will be todesign a standard structure that would be transferable to other similar pilot lines and which would ultimately allow future information sharing between various pilot lines in Cloudbased systems

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

Pilot line validated (2)

T63 Validation of the pilot lines FAU CHALM POLITO EOS OSAI IVF CSEM Validation of the pilot lines in case of the both processes EBM and LPBF will be performed utilizing standard builds to assess microstructure and mechanical properties of the material of interest for the use cases Optimised material process parameters and online monitoring system settings developed in WP4 will be utilized for manufacturing of the standard test bars Evaluation of the microstructure will be performed in order to assure manufacturing of the defectfree test bars Standard mechanical testing tensile and impact tests hardness fatigue etc will be performed based on the requirements to the material and usecase component established in WP1 Mechanical testing will be performed using established standard andor in standardised laboratories and will be coordinated with T45

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

Establish technology driven standard qualification protocol

Based on T91 to T95 by taking in consideration the relation between the measured parameters ex powder flowability particle size beam scan rates etc and quality of fabricated parts two mains qualification standards will be prepared a the qualification standard b process protocol In the first one the criteria for qualification will be established and in the second one the detailed process for fabrication of qualified components will be prepared The protocols will contain the full chain process qualification control methods and fabrication

Assembly of pilot lines (2)

T62 Assembly of the pilot lines CHALM FAU POLITO EOS OSAI IVFDeveloped in the WP4 tailored AM processes EBM and LPBF T41 and online monitoring systems will be assembled As described above some online monitoring systems require significant development of the process parameters that are determined by material and component design Full assembly of the both EBM and LPBF processes with the intended online monitoring systems will be performed Further in case of LPBF set of the online monitoring tools will be selected based on the material and complexity of the usercase component Manufacturing lines will be adapted to the most desirable material between different use cases at the beginning of the pilot line testing Further change in material of interest will be performed in accordance to the plan established in WP2

Customer relations and service

Customers are the heartbeat of all businesses therefore developing and sustaining a healthy relationship with them is crucial to the success The customers can be divided into three categories partner organisations pilot customers and external customers Each category of customers will be used to assess the business model and fine tune the process and hence increase the TRL level The input from external factors markets and companiesorganisations will be used to improve the pilot line services Also the Consortium will monitor external developments such as development of new technologies competitors patents regulations and market trends to help assess the exploitation opportunities and to identify understand and mitigate barriers to market entry This will include deskbased research eg published market business and technical reports and insights gathered by partners during dissemination activities eg by both visiting the partners and external organizations potential customers SWOT and PESTEL analysis will be performed at the start of the project and updated throughout the project Work will also be performed with each partner to evolve their innovation strategy and to understand their processes clarify the business case and product requirements

Analytics toolbox and feedback (2)

D65 Analytics toolbox and feedback 2 41T61 Analytics toolbox and feedback CU MSC SIT EOS IVF The aim of this task is to develop standard feedback loops and analytics toolbox specified in WP1 It will enable optimization of the pilot lines through the analysis of the large amount data collected Depending on the type of data available T54 various types of process modelling approaches could be used to extract knowledge and features State of the art modelling data mining and machine learning tools will be reviewed eg Image processing techniques Support Vector Machine Principal Component Analysis Deep Neural Network data regression classification clustering and the most relevant will be tested with real data collected at different stages of the pilot lines Using the selected data analytics tools a wide range of information will be extracted and used to model various aspect of the AM process eg temperature profiles dynamic spatiotemporal imaging of the melting process electron optical or laser optical imaging but again not all information will be usable It is necessary to identify what standard outputs and related controllable factors could realistically be used automatically or manually at different stages of the pilot lines to enable appropriate and usable feedback eg tuning of the digital twin Manufacturing sequence adaptation or localized process parameter modification such as laser power Depending on the monitored information and on the controllable factors feedback loops will be designed using the online monitoring information in combination with data collected and models built from past machining experience These selflearning models could then be used to propose both onlineand offline changes to machining parameters and to manufacturing sequences depending on the controllable factors or to stop the building of components identified as faulty The main result of this task will be a range standard feedback loops demonstrating the reliability of the feedback process MSC will support CU analytics effort with Data Management and Data Quality monitoring in coordination with T14 T33 and T54 activity

Pilot line validated (1)

T63 Validation of the pilot lines FAU CHALM POLITO EOS OSAI IVF CSEMValidation of the pilot lines in case of the both processes EBM and LPBF will be performed utilizing standard builds to assess microstructure and mechanical properties of the material of interest for the use cases Optimised material process parameters and online monitoring system settings developed in WP4 will be utilized formanufacturing of the standard test bars Evaluation of the microstructure will be performed in order to assure manufacturing of the defectfree test bars Standard mechanical testing tensile and impact tests hardness fatigue etc will be performed based on the requirements to the material and usecase component established in WP1 Mechanical testing will be performed in standardised laboratories and will be coordinated with T45

Development and calibration of the on-line process monitoring for material of interest (2)

T432 Development and calibration of the online process monitoring for material of interest for LPBF process CHALM POLITO EOS CHALM will adopt five online process monitoring systems allowing to perform online 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 cameraprocess atmosphere control based on continuous analysis of the oxygen concentration close to the powderbed with sensitivity around 10 ppm and automatic initiation of the additional purging with the processgas to assure process gas purity monitoring of the energy application in the melt pool also called optical tomography based onutilization of the sCMOS camera that allows to detect overall fusion and cooling behaviour realtime 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 componentSystems will need to be developed for the specific materials and in some cases design of interest and will be validated utilizing extensive materials characterisation

Environment, Health and Safety

This task aims to support safe and environmentally sustainable design and operation of the MANUELA pilot line Existing applicable EHS regulations will be reviewed and a gap analysis performed with the aim to provide regulatory recommendations and best practices for industrially applied metal AM Furthermore focus will be on the safety at operator level where important work environment considerations such as powder handling and ergonomics will be addressed The work will connect closely to T51 dealing with safe humanrobot interaction

Technology overview of the EU and International companies of future MANUELA clients

Whenever a new technology hit the market it is important to develop the surrounding ecosystems Hightech Industrial partners and SMEs play an important role the fast take up of new technology like design services business toolmanufactures and integration specialists This is especially the case in proximity of the pilot production line MANUELA consortium will actively seek for potential partners and technologies that could use MANUELA pilot production for improvement and development of new products MANUELA will also engage in outreach workshops especially to encourage uptake of the technology and to find more opportunities for enterprises from whole Europe and proximity of lines in particular This multiplication will source more future customers and will create economic value from the MANUELA project for significantly higher companies Educational training activities will be also addressed with the promotion of participation of final year university students for development of Practical works Undergraduate students and Master Thesis Postgraduate students with research infrastructures pilot lines and demonstration sites available in MANUELA

Pilot line process acceptance criteria

The pilot lines developed in MANUELA will consist of multiple step processes and interactions ie design for AM AM fabrication postAM treatment and quality control Acceptance criteria relying on material qualification sample parts manufacturing as well as process simulation will be established Evaluation of the process stability repeatability properties of the material microstructure and mechanical properties as well as geometry assurance will be performed on the standard builds for specific technology LPBF or EBM Optimised design material process parameters online monitoring system settings and postAM treatment methodology developed in WP3WP5 will be utilized for manufacturing of the standard test bars Mechanical testing will be performed in standardised laboratories and will be coordinated with T45

Recommendation to standardization bodies

The main goal of this task is a gap analysis of standards and specifications especially focused on pilot line for metal additive manufacturing The analysis where standardization is needed the most but does not yet exist Each gap description be supplemented by a recommendation how the identified gap can be addressed and eventually closed recommendation who can do it in Europe and how critical it is The recognized national standards bodies SNV SIS DIN will be dynamically involved through active intervention within this task

Project dissemination and communication strategy

Identify suitable channels to disseminante project results and to communicate project activities and results to the relvant target audiences.

Analytics toolbox and feedback (1)

T61 Analytics toolbox and feedback CU MSC SIT EOS IVFThe aim of this task is to develop standard feedback loops and analytics toolbox specified in WP1 It will enable optimization of the pilot lines through the analysis of the large amount data collected Depending on the type of data available T54 various types of process modelling approaches could be used to extract knowledge and features State of the art modelling data mining and machine learning tools will be reviewed eg Image processing techniques Support Vector Machine Principal Component Analysis Deep Neural Network data regression classification clustering and the most relevant will be tested with real data collected at different stages of the pilot linesUsing the selected data analytics tools a wide range of information will be extracted and used to model various aspect of the AM process eg localized acoustic information temperature profiles dynamic spatiotemporal imaging of the melting process but again not all information will be usable It is necessary to identify what standard outputs and related controllable factors could realistically be used automatically or manually at different stages of the pilot lines to enable appropriate and usable feedback eg tuning of the digital twin Manufacturing sequence adaptation or localized process parameter modification such as laser power Depending on the monitored information and on the controllable factors feedback loops will be designed using the online monitoring information in combination with data collected and models built from past machining experience These selflearning models could then be used to propose both online and offline changes to machining parameters and to manufacturing sequences depending on the controllable factors or to stop the building of components identified as faulty The main result of this task will be a range standard feedback loops demonstrating the reliability of the feedback process MSC will support CU analytics effort with Data Management and Data Quality monitoring in coordination with T14 T33 and T54 activity

Pilot line assembled and tested (1)

Automated post-processing supply chain for AM

T51 Automated and safe part handling and 3D printer interaction IVF SIT CHALM FAU POLITO ABBThis task addresses the implementation of an automated post process supply chain for metal AM allowing robotized operation of multiple AM machines and part delivery to the post process steps in T52 Additionally this task considers the design of a safe humanrobot collaborative industrial workplace and minimizes the exposure of the personnel to the potential risks that is accommodated with metal powders in additivemanufacturing both from health and environment point of view but also from safety perspectiveTo ensure a flexible and scalable pilot line IVF SIT and ABB will implement a system where automated guided vehicle AGV that will be used to remove and transport build platforms from the 3D printers to a separate postprocessing station An AGV suitable for working in an environment with human operators will be selected TheAGV will be equipped with a lifting platform and a flexible gripper tool to be able extract build platforms from multiple 3D printer types Additionally the AGV will be equipped with a robotic arm which will be used to perform service operations in the 3D printers such as powder cleaning The proposed solution will minimizephysiological issues associated with the carrying of high loads and contact with metal powderA postprocessing station with all necessary equipment as determined in T13 and adapted in T52 will be set up as a closed robot cell by IVF and ABB The cell will include a pick and place station to which the AGV will deliver build platforms

Optimised microstructure for LPBF and EBM processes

T42 Tailoring of the material microstructure for process of interest POLITO CHALM FAU EOST421 Tailoring of the material microstructure for EBM process FAUT421 aims on tailoring the microstructure and thereby the properties of the materials of interest by applyingparticular process strategies FAU expects that the high power in combination with the high flexibility of the newelectron gun can be used to influence and tailor the solidification conditions over a wide range Thus FAU willinvestigate the possibility to realize different microstructures for the materials of interest This comprises the grainstructure eg fine columnar coarse columnar or equiaxed and the fineness of the microstructureT422 Tailoring of the material microstructure for LPBF process CHALM POLITOThe focus of this activity is placed on design and optimisation of the LPBF process in dependence on thematerial composition and components design and requirements Focus will be placed on optimisation of themicrostructure with focus on the specific design features as eg thin structures as rods thin walls design featureswith different angle to the build directionrecoater and their respective mechanical performance Development ofthe process parameters to assure required microstructure and hence properties of such design features will beperformed

Manufacturing of the test components by optimized processes

T64 Manufacturing of the test components by optimized processes CSEM FAU POLITO CHALMTest components demonstrators will be manufactured utilizing validated AM processes in T63 Intended demonstrators will possess important design features from the user cases and will allow to perform assessment of the optimised processes to fabricate user case components utilizing selected AM technology and developedprocess Focus will be placed on geometrical tolerance of the asbuilt demonstrator surface finish etc in addition to the mechanical and microstructure properties of the material assessed in T63

MANUELA's dashboard

T35 Implementation per technology CSEM MSC IVF USE CASEST35 will implement the previous tasks ie the chosen design optimization T31 as part of the Simulation toolT32 together with the manufacturing feedback T33 both accessible from the GUI T34 and will deploy theoverall solution on each system connected to the pilotline Offering the simulation and manufacturing GUI aspart of the MANUELA dashboard demonstrator this will enable Technology selection LPBF or EBM process parameter optimization through simulation andmanufacturing feedback Postprocessing selection IVF and optimization process parameter optimization through simulation andmanufacturing feedback starting from cutting platesupport and HIP whenever applicable to machiningor surface treatmentsCSEM supervises the implementation of the simulation tool and its GUI for both processes LPBF and EBM andwill provide the required links within the MANUELA Dashboard to interface with the GUI provided by MSCThe implementation of the process and postprocessing decision tree T31 and its outputs will be done by CSEMUnderstanding that part of the software implementation might be driven by users preferred choice and thatmanufacturing technology means has to take specific USE CASE requirement MSC will implement the GUIwithin an agnostic platformThe implementation will be validated through designing and reference samples manufacturing specified in WP1CSEM will provide the link between the use cases and the Multi Physics Simulation and design tool assisting theusers in the validation of their use cases per chosen technology LPBF or EBMMSC will support this task with handson training on the dashboard steering the digital twin in order to educateend users and capitalize upon testers feedback in order to fine tune software platform and ensure finalvalidationacceptance of the proposed solution

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

Project Workshops

Organisation oftwo project workshops and a final conference

Publikacje

Increasing productivity of laser powder bed fusion manufactured Hastelloy X through modification of process parameters

Autorzy: Claudia Schwerz, Fiona Schulz, Elanghovan Natesan, Lars Nyborg
Opublikowane w: Journal of Manufacturing Processes, Numer Volume 78, 2022, ISSN 2212-4616
Wydawca: 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

Autorzy: Ahmad Raza, Claudia Schwerz, Camille Pauzon, Lars Nyborg, Eduard Hryha
Opublikowane w: Powder Technology, Numer Volume 422, 15 May 2023, 118461, 2023, ISSN 0032-5910
Wydawca: 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

Autorzy: Claudia Schwerz, Lars Nyborg
Opublikowane w: CIRP Journal of Manufacturing Science and Technology, Numer Volume 37, 2022, ISSN 1878-0016
Wydawca: Elsevier
DOI: 10.1016/j.cirpj.2022.02.010

Optimizing the parameters of long short-term memory networks using the bees algorithm

Autorzy: Alamri, N. M., Packianather, M. and Bigot
Opublikowane w: Applied Sciences, Numer 13(4), 2023, ISSN 2076-3417
Wydawca: 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

Autorzy: Alessandra Martucci, Pui Lam Tam, Alberta Aversa, Mariangela Lombardi, Lars Nyborg
Opublikowane w: Surface & Interface Analysis, 2022, ISSN 1096-9918
Wydawca: Wiley Analytical Science
DOI: 10.1002/sia.7176

Corrosion behaviour of additively manufactured 316L and CoCrNi

Autorzy: Sri Bala Aditya Malladi, Pui Lam Tam, Yu Cao, Sheng Guo, Lars Nyborg
Opublikowane w: Surface and Interface Analysis, Numer 30 Jan, 2023, 2023, ISSN 1096-9918
Wydawca: 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

Autorzy: Claudia Schwerz, Ahmad Raza, Xiangyu Lei, Lars Nyborg, Eduard Hryha, Håkan Wirdelius
Opublikowane w: Additive Manufacturing, Numer 47, 2021, Strona(/y) 102370, ISSN 2214-8604
Wydawca: Elsevier BV
DOI: 10.1016/j.addma.2021.102370

Machine Learning-enabled feedback loops for metal powder bed fusion additive manufacturing

Autorzy: Chao Liu, Léopold Le Roux, Ze Ji, Pierre Kerfriden, Franck Lacan, Samuel Bigot
Opublikowane w: Procedia Computer Science, Numer 176, 2020, Strona(/y) 2586-2595, ISSN 1877-0509
Wydawca: 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

Autorzy: Léopold Le Roux, Chao Liu, Ze Ji, Pierre Kerfriden, Daniel Gage, Felix Feyer, Carolin Körner, Samuel Bigot
Opublikowane w: Procedia CIRP, Numer 99, 2021, Strona(/y) 342-347, ISSN 2212-8271
Wydawca: 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

Autorzy: Alessandro Carrozza, Alberta Aversa, Paolo Fino, Mariangela Lombardi
Opublikowane w: Journal of Alloys and Compounds, Numer 870, 2021, Strona(/y) 159329, ISSN 0925-8388
Wydawca: 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

Autorzy: Claudia Schwerz, Yu Cao, Lars Nyborg
Opublikowane w: Surface and Interface Analysis, Numer 2 Feb, 2023, 2023, ISSN 1096-9918
Wydawca: Wiley
DOI: 10.1002/sia.7202

Predicting the porosity in selective laser melting parts using hybrid regression convolutional neural network

Autorzy: Alamri, N. M., Packianather, M., Bigot S.
Opublikowane w: Applied Sciences, Numer 12(24), 2022, ISSN 2076-3417
Wydawca: MDPI
DOI: 10.3390/app122412571

Electron-Optical In Situ Imaging for the Assessment of Accuracy in Electron Beam Powder Bed Fusion

Autorzy: Christopher Arnold, Christoph Breuning, Carolin Körner
Opublikowane w: Materials, Numer 14(23), 2021, ISSN 1996-1944
Wydawca: MDPI Open Access Publishing
DOI: 10.3390/ma14237240

Laser-based Powder Bed Fusion of dispersion strengthened CoCrNi by ex-situ addition of TiN

Autorzy: Sri Bala Aditya Malladi; Laura Cordova; Sheng Guo; Lars Nyborg
Opublikowane w: Procedia CIRP (22128271) vol.111(2022), 2022, ISSN 0007-8506
Wydawca: 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

Autorzy: Claudia Schwerz, Benjamin A. Bircher, Alain Küng, Lars Nyborg
Opublikowane w: Additive Manufacturing, Numer Vol. 27, 25 June, 2023, 103631, 2023, ISSN 2214-8604
Wydawca: Elsevier BV
DOI: 10.1016/j.addma.2023.103631

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

Autorzy: Christopher Arnold, Carolin Körner
Opublikowane w: Additive Manufacturing, Numer 46, 2021, Strona(/y) 102213, ISSN 2214-8604
Wydawca: Elsevier BV
DOI: 10.1016/j.addma.2021.102213

Evaluation of pore re-opening after HIP in LPBF Ti–6Al–4V

Autorzy: Topi Kosonen, K. Kakko, N. Raitanen
Opublikowane w: Powder Metallurgy, Numer 64/5, 2021, Strona(/y) 425-433, ISSN 0032-5899
Wydawca: Maney Publishing
DOI: 10.1080/00325899.2021.1928997

Predicting laser powder bed fusion defects through in-process monitoring data and machine learning

Autorzy: Shuo Feng, Zhuoer Chen, Benjamin Bircher, Ze Ji, Lars Nyborg, Samuel Bigot
Opublikowane w: Materials & Design, Numer Volume 2222, 2022, ISSN 1873-4197
Wydawca: 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

Autorzy: Claudia Schwerz, Lars Nyborg
Opublikowane w: Metals, Numer 11, 2021, ISSN 2075-4701
Wydawca: MDPI
DOI: 10.3390/met11111856

Towards customized heat treatments and mechanical properties in the LPBF-processed Ti-6Al-2Sn-4Zr-6Mo alloy

Autorzy: Alessandro Carrozza, Alberta Aversa, Paolo Fino, Mariangela Lombardi
Opublikowane w: Materials & Design, Numer Volume 215, 2022, ISSN 1873-4197
Wydawca: Elsevier
DOI: 10.1016/j.matdes.2022.110512

Electron-optical in-situ metrology for electron beam powder bed fusion: calibration and validation

Autorzy: Christopher Arnold, Carolin Körner
Opublikowane w: Measurement Science and Technology, Numer Volume 33, Number 1, 2021, ISSN 1361-6501
Wydawca: 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

Autorzy: A. Martucci, B.Mehta, M.Lombardi, L.Nyborg
Opublikowane w: Metals, Numer 12(8), 2022, ISSN 2075-4701
Wydawca: MDPI
DOI: 10.3390/met12081387

Digital Twin-enabled Collaborative Data Management for Metal Additive Manufacturing Systems

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Opublikowane w: Journal of Manufacturing Systems, 2020, ISSN 0278-6125
Wydawca: Elsevier BV
DOI: 10.1016/j.jmsy.2020.05.010

Impact of powder recoating speed on built properties in PBF-LB process

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Opublikowane w: Procedia CIRP, Numer Volume 115, 2022, ISSN 2212-8271
Wydawca: Elsevier
DOI: 10.1016/j.procir.2022.10.061

Towards data-driven additive manufacturing processes

Autorzy: Vincenzo Gulisano; Marina Papatriantafilou; Zhuoer Chen; Eduard Hryha; Lars Nyborg
Opublikowane w: Proceedings of the 23rd International Middleware Conference Industrial Track, Numer 3, 2022, Strona(/y) 43-49, ISBN 978-1-4503-9340-9
Wydawca: Middleware
DOI: 10.1145/3564695.3564778

Optimization of convolutional neural network topology and training parameters using Bees Algorithm

Autorzy: Alamri, N. M., Packianather, M. and Bigot
Opublikowane w: IEEE 2nd International Symposium on Sustainable Energy, 2022, Strona(/y) 1-6
Wydawca: IEEE
DOI: 10.1109/isssc56467.2022.10051487

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