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The successful shift to NDE 4.0 will not only require developing and embracing new technologies associated with the fourth industrial revolution or becoming an integral part of the overall Industry 4.0, but also developing and adopting new ways of working. It is undoubtful that people will remain in charge of the inspections. However, it is arguable if the current “procedure-following” “level I-III” paradigm can withstand the changes that come along NDE 4.0. With the increased autonomy and interconnectedness expected with NDT 4.0, the majority of traditional NDE tasks will no longer be needed. Instead, different skills, such as that of programming and adapting systems, as well as problem solving, will become vital for the inspections. Therefore, we suggest that a new paradigm is needed—one in which inspector roles and, thus, also the requirements will have to be reinvented. We expect the inspectors to be relieved from the tedious and error prone aspects of the current system and to take responsibility for increasingly complex automated systems and work in closer collaboration with other experts. Thus, we propose that the traditional inspector roles will be transformed into that of the system developer, caretaker and problem solver, each requiring a specific set of skills and assuming different responsibilities. In this talk, we will present the new roles and discuss the challenges that may arise with them.
Typically, the near-eutectic Al-Si alloys consist of highly interconnected three-dimensional network of the eutectic Silicon (Si) and intermetallics embedded into Aluminium (Al) matrix. For further improvement of the mechanical properties of such alloys, often, one single ceramic reinforcement phase, e.g. silicon carbide (SiC) or aluminium oxide (Al2O3) in the form of fibres or particles is added. However, hybrid reinforcements (fibres and particles) can further improve wear resistance and fracture toughness, and additionally, reduce anisotropy of the material. The engineering of metal matrix composites (MMC) for specific application requirements benefits from a comprehensive knowledge of the failure behaviour. Therefore, damage evolution under compression was investigated on:
- pure near-eutectic AlSi12CuMgNi matrix alloy
- type I: matrix reinforced with random-planar oriented Al2O3 short fibres (15 vol.%)
- type II: matrix reinforced with random-planar oriented Al2O3 short fibres (7 vol.%) and additional SiC particles (15 vol.%)
The analysis of damage mechanism was carried out in two rather independent but complementary studies. First, selected sister samples of every material were exposed to quasi-static compression (traverse control). The compression tests were interrupted at different strain levels. Miniature cylinders with a diameter of 1mm were extracted from the pre-strained samples and investigated by synchrotron computed tomography (SX-µCT) with a spatial resolution of about 0.7 µm. For the pure matrix alloy, microcracks are confined to the intermetallic particles and to the eutectic Si, hence no damage was observed in the Aluminium. The composite type II revealed a more effective strain accumulation (less damage) than type I at low plastic strain (up to 5 %), but a more catastrophic damage development due to cracking of the SiC clusters at higher strain levels.
The second approach to study the damage initiation and accumulation in the materials subjected to compressive load was Acoustic Emission (AE) analysis. In this case the in-situ monitoring of the acoustic emission signal was performed during compression tests on specimens with dimension of several mm. For all three material types, AE activity set at 2% strain. Differences in AE behaviour of the three materials was proven based on AE hitrate, signal peak amplitudes as well as weighted peak frequencies (WPF). Future work focuses on combination of AE and SX-µCT aiming for more detailed knowledge on damage mechanism of metal matrix composites.
Nanomaterials bring various benefits and have become a part of our daily lives. However, the risks emerging from nanotechnology need to be minimized and controlled at the regulatory level and therefore, there is a need for nanorisk governance. One of the prerequisites for successful nanorisk governance is the availability of high-quality data on nanomaterials and their impact with the human body and the environment. In recent decades, a countless number of publications and studies on nanomaterials and their properties have been produced due to the fast development of nanotechnology. Despite such a vast amount of data and information, there are certain knowledge gaps hindering an efficient nanorisk governance process. Knowing the state of the available data and information is an important requirement for any decision maker in dealing with risks. In the specific case of nanotechnology, where most of the risks are complex, ambiguous, and uncertain in nature, it is essential to obtain complete data and metadata, to fill knowledge gaps, and to transform the available knowledge into functional knowledge. This can become possible using a novel approach developed within the NANORIGO project (Grant agreement No. 814530) – the Knowledge Readiness Level (KaRL). In analogy to NASA’s Technology Readiness Levels (TRLs), we define KaRLs as a categorization system of data, information, and knowledge which enables transformation of data and information into functional knowledge for nanorisk governance. Our approach goes beyond the technical curation of data and metadata and involves quality and completeness filters, regulatory compliance requirements, nanorisk-related tools, and most importantly, human input (inclusion of all stakeholder groups). With the KaRL approach we also address key issues in nanotechnology such as societal and ethical concerns, circular economies and sustainability, the Green Deal, and the traceability of data, knowledge, and decisions. The KaRL approach could be used for nanorisk governance by a nanorisk governance council (NRGC), which is currently under development by three EU projects (NANORIGO, GOV4NANO, and RISKGONE).
Nanomaterials bring various benefits and have become a part of our daily lives. However, the risks emerging from nanotechnology need to be minimized and controlled at the regulatory level and therefore, there is a need for nanorisk governance. One of the prerequisites for successful nanorisk governance is the availability of high-quality data on nanomaterials and their impact with the human body and the environment. In recent decades, a countless number of publications and studies on nanomaterials and their properties have been produced due to the fast development of nanotechnology. Despite such a vast amount of data and information, there are certain knowledge gaps hindering an efficient nanorisk governance process. Knowing the state of the available data and information is an important requirement for any decision maker in dealing with risks. In the specific case of nanotechnology, where most of the risks are complex, ambiguous, and uncertain in nature, it is essential to obtain complete data and metadata, to fill knowledge gaps, and to transform the available knowledge into functional knowledge. This can become possible using a novel approach developed within the NANORIGO project (Grant agreement No. 814530) – the Knowledge Readiness Level (KaRL). In analogy to NASA’s Technology Readiness Levels (TRLs), we define KaRLs as a categorization system of data, information, and knowledge which enables transformation of data and information into functional knowledge for nanorisk governance. Our approach goes beyond the technical curation of data and metadata and involves quality and completeness filters, regulatory compliance requirements, nanorisk-related tools, and most importantly, human input (inclusion of all stakeholder groups). With the KaRL approach we also address key issues in nanotechnology such as societal and ethical concerns, circular economies and sustainability, the Green Deal, and the traceability of data, knowledge, and decisions. The KaRL approach could be used for nanorisk governance by a nanorisk governance council (NRGC), which is currently under development by three EU projects (NANORIGO, GOV4NANO, and RISKGONE).
In the field of non-destructive testing (NDT) in civil engineering, a large number of measurement data are collected. Although they serve as a basis for scientific analyses, there is still no uniform representation of the data. An analysis of various distributed data sets across different test objects is therefore only possible with high manual effort.
We present a system architecture for an integrated data management of distributed data sets based on Semantic Web technologies. The approach is essentially based on a mathematical model - the so-called ontology - which represents the knowledge of our domain NDT. The ontology developed by us is linked to data sources and thus describes the semantic meaning of the data. Furthermore, the ontology acts as a central concept for database access. Non-domain data sources can be easily integrated by linking them to the NDT construction ontology and are directly available for generic use in the sense of digitization. Based on an extensive literature research, we outline the possibilities that this offers for NDT in civil engineering, such as computer-aided sorting, analysis, recognition and explanation of relationships (explainable AI) for several million measurement data.
The expected benefits of this approach of knowledge representation and data access for the NDT community are an expansion of knowledge through data exchange in research (interoperability), the scientific exploitation of large existing data sources with data-based methods (such as image recognition, measurement uncertainty calculations, factor analysis, material characterization) and finally a simplified exchange of NDT data with engineering models and thus with the construction industry.
Ontologies are already the core of numerous intelligent systems such as building information modeling or research databases. This contribution gives an overview of the range of tools we are currently creating to communicate with them.
X-ray back scatter imaging is rarely applied compared to classical X-ray projection imaging. 20 years ago the company Philips developed “COMSCAN”, a first application case in the aircraft industry, which allowed even a depth resolution using back scatter imaging. The company AS&E in Boston offers back scatter imaging solutions for the security market. Their principle is to scan the object with a highly collimated X-ray needle beam from one side only and detect the backscattered radiation by a large area detector side by side with the collimation wheel. A new prototype is investigated at BAM for application and optimization in non-destructive testing. As modern industrial application field in-situ testing in additive manufacturing is targeted. The accessibility of the printed part during the production process is very limited. This prevent the application of a two sided X-ray inspection or Computed Tomography, were an rotation of the object is required to acquire projections from 360 degrees. An important advantage for the X-ray back scatter technique are also the materials used in additive manufacturing (polymers, ceramics, light metals like Aluminum or Titanium). These materials with lower density and lower Z values give better scatter signals than metals with higher densities and Z values. The back scatter intensity decreases with increasing density and Z value of the material. But the requirements on spatial resolution and contrast sensitivity are more stringent for non-destructive testing of additive manufactured parts compared to the security area. In NDT sizes of indications smaller than 1 mm have to be detected clearly. The investigation of these limits on a stateof-the-art prototype for X-ray back scattering using rotating collimated X-ray needle beams is a part of the BAM project “ProMoAM”. The contribution shows first results of the optimization for NDT and the achieved application limits for several example cases.
The detection of cracks before the failure is highly significant when it comes to safety-relevant structures. Crack detection in metallic samples at high surface temperature is one of the challenging situation in manufacturing industries. Laser thermography has already proved its detection capability of surface cracks in metallic samples at room temperature. In this work a continuous wave (CW) laser use to generate a laser, which is using to scan the metal surface with notch. The corresponding heat distribution on the surface monitored using infrared thermal (IR) camera. A simplified 3D model for laser thermography is developed and validated with experimental results. A dedicated image processing algorithm developed to improve the detectability of the cracks. To understand the dependency of surface temperature, laser power, laser scanning speed etc. in defect detection, we carried out parametric studies with our validated model. Here we report the capability of laser thermography in crack detection at elevated temperature.
qNMR provides the most universally applicable form of direct concentration or purity determination without need for reference materials of impurities or the calculation of response factors but only exhibiting suitable NMR properties.
The workshop presents basic terms of statistics and uncertainty analysis, which are the basis for qNMR spectroscopy and data analysis such as, e.g., standard deviations, linear regression, significance tests, etc. and gives typical examples of applications in qNMR spectroscopy.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control.
The talk introduces a smart online NMR sensor module provided in an explosion proof housing as example. This sensor was developed for an intensified industrial process (pharmaceutical lithiation reaction step) funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs.
The talk also generally covers current aspects of high-field and low-field online NMR spectroscopy for reaction monitoring and process control and gives also an overview on direct dissolution studies of API cocrystals.
The departure from the current automation landscape to next generation automation concepts for the process industry has already begun. Smart functions of sensors simplify their use and enable plug-and-play integration, even though they may appear to be more complex at first sight. Monitoring specific information (i.e., “chemical” such as physico-chemical properties, chemical reactions, etc.) is the key to “chemical” process control. Here we introduce our smart online NMR sensor module provided in an explosion proof housing as example, which was developed for an intensified industrial process funded by the EU’s Horizon 2020 research and innovation programme (www.consens-spire.eu). Due to NMR spectroscopy as an “absolute analytical comparison method”, independent of the matrix, it runs with extremely short set-up times in combination with “modular” spectral models. Such models can simply be built upon pure component NMR spectra within a few hours (i.e., assignment of the NMR signals to the components) instead of tedious calibrations runs.