Periodic Reporting for period 1 - xCTing (Enabling X-ray CT based Industry 4.0 process chains by training Next Generation research experts)
Okres sprawozdawczy: 2021-03-01 do 2023-02-28
First-time-right and zero-defect manufacturing of customized lot-size-one products are essential elements of the Industry 4.0 paradigm shift to reinforce Europe’s global leadership in manufacturing. X-ray Computed Tomography (CT) metrology has a key role to play in this transition, since it is the only known technology that can certify non-destructively the quality of internal complex structures, such as those produced by additive manufacturing or found in assemblies.
However, CT largely remains an off-line technology, due to the unsolved trade-off between scan speed and scan quality, and especially the need for extensive expert user input. xCTing will therefore focus on significantly increasing autonomy, robustness and speed in CT metrology in order to support its transition towards a fully in-line quality assurance technology as required in Industry 4.0 environments. Meeting these challenges requires the integration of a broad range of interdisciplinary expertise, including physics, manufacturing, dimensional metrology, machine learning, as well as efficient and reliable big data analytics and visualization.
In order to achieve the envisaged innovation breakthrough in the European industry, Europe is in dire need of young innovators who can combine this variety of competences with entrepreneurial skills. The xCTing project is a pan-European industrial- academic initiative committed to the provision of a unique and encompassing training environment required to foster a new generation of innovation-minded research engineers, that will act as catalysts in the further transformation of Europe’s manufacturing industry towards global technological leadership.
However, CT largely remains an off-line technology, due to the unsolved trade-off between scan speed and scan quality, and especially the need for extensive expert user input. xCTing will therefore focus on significantly increasing autonomy, robustness and speed in CT metrology in order to support its transition towards a fully in-line quality assurance technology as required in Industry 4.0 environments. Meeting these challenges requires the integration of a broad range of interdisciplinary expertise, including physics, manufacturing, dimensional metrology, machine learning, as well as efficient and reliable big data analytics and visualization.
In order to achieve the envisaged innovation breakthrough in the European industry, Europe is in dire need of young innovators who can combine this variety of competences with entrepreneurial skills. The xCTing project is a pan-European industrial- academic initiative committed to the provision of a unique and encompassing training environment required to foster a new generation of innovation-minded research engineers, that will act as catalysts in the further transformation of Europe’s manufacturing industry towards global technological leadership.
In total there are 15 early stage researcher (ESR) fellows have been recruited at the 10 partner institutions in the xCTing consortium, and each of them has enrolled as a PhD research at an academic institution. The ESRs on post have already presented 3 times their research project results and progress in front of the xCTing supervisory board and once to the project officer.
There have been 7 consortium-wise training weeks successfully organized, with both in-depth lectures and workshop of technical contents in computer tomography (XCT) and transferrable soft-skills.
The xCTing consortium has also carried out outreach activities via dissemination of research results publications in journal and presentations/posters on professional/dedicated conferences (e.g. 4 oral presentations by xCTing ESRs on iCT2023 Conference Fürth, Germany). A broad community has been reached as well through project website and especially by posts on LinkedIn.
There have been 7 consortium-wise training weeks successfully organized, with both in-depth lectures and workshop of technical contents in computer tomography (XCT) and transferrable soft-skills.
The xCTing consortium has also carried out outreach activities via dissemination of research results publications in journal and presentations/posters on professional/dedicated conferences (e.g. 4 oral presentations by xCTing ESRs on iCT2023 Conference Fürth, Germany). A broad community has been reached as well through project website and especially by posts on LinkedIn.
The xCTing project expects to bring technological breakthrough and progress above the XCT SoA in the following areas:
1. Reducing CT expert user input. Current practice still requires ample expert user decisions and optimization iterations for each new specimen to be scanned throughout the entire CT pipeline. Reducing the required expert input would yield a more consistent CT output quality at reduced set-up time.
ESR 1 project: Autonomous adaptation of CT acquisition parameters
ESR 2 project: 2D neural networks for artefact correction during CT reconstruction
ESR 3 project: 3D neural networks for artefact correction during CT reconstruction
ESR 4 project: Smart and autonomous feature detection and quantification for ensemble datasets and single ensemble members
ESR 5 project: Digital twin for CT analysis chains
2. Metrological traceability. Current best practice for assessing task-specific measurement uncertainty is not suitable for small lot sizes, since it relies on measuring calibrated workpieces with a very high similarity, which is prohibitively time-consuming. Novel methodologies need to be developed for in-line performance verification and uncertainty determination in CT dimensional metrology that are computationally realistic yet encompass all relevant influence factors.
ESR 6 project: Determination of task-specific measurement uncertainty caused by geometrical misalignments
ESR 7 project: Determination of task-specific measurement uncertainty caused by physical effects
ESR 8 project: Autonomous in-scanning characterization, verification and condition monitoring of the in-line CT equipment
3. Faster CT Acquisition without quality loss. Current practice for high-quality CT is time-prohibitive for in-line quality assurance, since it relies on the acquisition of several thousands of X-ray projections during a full 360° rotation of the workpiece.
ESR 9 project: A-priori-knowledge enhanced CT reconstruction for fast scanning strategies
ESR 10 project: A-priori-knowledge based determination of optimal scanning trajectories
ESR 11 project: Fast scanning strategies for conveyer-belt setups
ESR 12 project: Adaptive angle-selection for in-line CT
4. Integration in the manufacturing intelligence loop. In the context of decreasing lot sizes, traditional methods of quality inspection are no longer sufficient.
ESR 13 project: CT based process planning and build preparation for AM
ESR 14 project: CT based improvement of in-process monitoring capabilities
ESR 15 project: CT-based adaptive assembly chains
The overall aim of the xCTing project is therefore to train 15 young and promising researchers (ESRs) that will take the lead in conceiving the next generation of European Industry 4.0-ready CT technology.
1. Reducing CT expert user input. Current practice still requires ample expert user decisions and optimization iterations for each new specimen to be scanned throughout the entire CT pipeline. Reducing the required expert input would yield a more consistent CT output quality at reduced set-up time.
ESR 1 project: Autonomous adaptation of CT acquisition parameters
ESR 2 project: 2D neural networks for artefact correction during CT reconstruction
ESR 3 project: 3D neural networks for artefact correction during CT reconstruction
ESR 4 project: Smart and autonomous feature detection and quantification for ensemble datasets and single ensemble members
ESR 5 project: Digital twin for CT analysis chains
2. Metrological traceability. Current best practice for assessing task-specific measurement uncertainty is not suitable for small lot sizes, since it relies on measuring calibrated workpieces with a very high similarity, which is prohibitively time-consuming. Novel methodologies need to be developed for in-line performance verification and uncertainty determination in CT dimensional metrology that are computationally realistic yet encompass all relevant influence factors.
ESR 6 project: Determination of task-specific measurement uncertainty caused by geometrical misalignments
ESR 7 project: Determination of task-specific measurement uncertainty caused by physical effects
ESR 8 project: Autonomous in-scanning characterization, verification and condition monitoring of the in-line CT equipment
3. Faster CT Acquisition without quality loss. Current practice for high-quality CT is time-prohibitive for in-line quality assurance, since it relies on the acquisition of several thousands of X-ray projections during a full 360° rotation of the workpiece.
ESR 9 project: A-priori-knowledge enhanced CT reconstruction for fast scanning strategies
ESR 10 project: A-priori-knowledge based determination of optimal scanning trajectories
ESR 11 project: Fast scanning strategies for conveyer-belt setups
ESR 12 project: Adaptive angle-selection for in-line CT
4. Integration in the manufacturing intelligence loop. In the context of decreasing lot sizes, traditional methods of quality inspection are no longer sufficient.
ESR 13 project: CT based process planning and build preparation for AM
ESR 14 project: CT based improvement of in-process monitoring capabilities
ESR 15 project: CT-based adaptive assembly chains
The overall aim of the xCTing project is therefore to train 15 young and promising researchers (ESRs) that will take the lead in conceiving the next generation of European Industry 4.0-ready CT technology.