Steve Vanlanduit professor - vice-dean

Research Steve Vanlanduit

Research team

Expertise

Optical measurement techniques for materials characterization: scanning laser vibrometry, optical fiber sensors, hyperspectral imaging. Dimensional metrology. Applications of optical measurement techniques in different field: corrosion and coating inspection, oil spill detection, 3D printing, smart farming.

Data-Driven Smart Shipping (DDSHIP). 01/05/2024 - 31/10/2026

Abstract

In the worldwide R&D on computer-assisted and autonomous navigation the DDSHIP project will contribute by setting a new process flow methodology and test platform for validation and certification through investigations on: • more accurate and robust perception and situational awareness of the waterborne world around the ship in dense traffic and harsh weather conditions; • the accurate representation of the real behaviour of the ship in complex waterways with low under keel clearances and nearby banks and infrastructure; • the safe and smooth control of the ship through model predictive AI-trained controllers providing necessary collision avoidance. As accidents on waterways are mainly attributed to human actions in combination with failures of technical hard- and software or environmental circumstances, the support of captains, pilots or skippers on board the manned ship or the operator from a remote operation centre on an unmanned ship, this research should prove the capabilities of existing technologies (camera, sensors, manoeuvring model prediction, path-planning and steering) leading to smarter - more accurate and higher reliability – control.

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  • Research Project

In vivo patient-specific real-time dosimetry for adaptive radiotherapy (VERIFIED). 01/04/2024 - 31/03/2028

Abstract

Errors in radiotherapy can have significant consequences for patients and generate concerns in public opinion due to misconceptions surrounding ionizing radiation. To enhance its safety, the implementation of in vivo dosimetry is crucial. The VERIFIED project aims to advance individualized therapeutic procedures by utilizing patient-specific information, real-time dose, and deep learning techniques in adaptive radiotherapy (ART). The primary objective of the project is to develop dynamic end-to-end methods that closely simulate real patient treatments. Our project encompasses several key objectives. First, it involves the development and characterization of appropriate phantoms featuring movable and deformable inserts, specifically targeting lung and brain tumors for ART. Additionally, we focus on investigating individualized patient-specific real-time dosimetry in cases of non-small-cell lung cancer using Volumetric Modulated Arc Therapy (ART-VMAT). This approach enables accurate and timely monitoring of radiation doses. development of a realtime dose prediction protocol for non-small-cell lung and bladder tumors ART-VMAT. This protocol combines data obtained from the developed dynamic phantoms and the patient-specific real-time dosimetry system. Deep learning algorithms are employed to enhance the accuracy of dose prediction. Furthermore, an image-based system is being implemented to monitor the patient's head surface during in adaptive hypofractionated Gamma Knife radiosurgery (hfGKRS) for brain tumours, ensuring precise treatment delivery. Additionally, we will analyze the data obtained from the patient's head surface monitoring system, incorporating deep learning-based algorithms to generate a protocol for patient selection in hfGKRS.The proposed protocols integrate state-of-the-art deep learning methods with patient-specific real-time dosimetry in ART-VMAT and real-time position imaging in hfGKRS, effectively addressing several unmet needs in adaptive radiotherapy. These protocols encompass adaptability assessment, dosimetric verification, imaging validation, plan evaluation metrics, and treatment efficiency. By leveraging the power of real-time dosimetry, imaging, and deep learning, treatment efficacy can be enhanced while minimizing toxicity and radiation-induced side effects, ultimately resulting in improved patient outcomes in radiotherapy.

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  • Research Project

Hybrid AI for Predictive Road Maintenance (HAIRoad). 01/10/2023 - 30/09/2025

Abstract

The current approach to monitoring road quality is based on manual inspections and is labor intensive and relatively expensive. Hybrid AI for Predictive Road Maintenance (HAIRoad) aims to use (hybrid) AI to map the condition of the road network and make recommendations for road maintenance. An efficient and robust data pipeline will be developed using MLOps tools, which allow easy switching between model development and implementation/production. Three demonstrators will illustrate the feasibility of the approach: one with the Port of Antwerp Bruges and two at the municipal level. The demonstrators will allow to validate both the more technical aspects and the market potential. HAIRoad will deliver several innovations such as automated detection of the road conditions, new indicators for road management, sensor fusion by combining information from multiple sensors, and the application of hybrid-AI where we will incorporate physical models of road degradation into data-driven machine learning models.

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  • Research Project

Inspection and measurement of complex 3D printed objects. 01/06/2023 - 31/05/2025

Abstract

3D printing, and additive manufacturing in particular, has in the last couple of years transformed from a prototyping to a more mature manufacturing technology. The driving force for this transformation was the need for custom built, lightweight parts and a more efficient use of raw materials in order to reduce waste. The current trend is towards increasingly complex parts, consisting of multiple materials to enhance their physical and/or mechanical properties. The complexity of the parts brings with it a need for specialized inspection and quality control methods. In this project, the complementarity between different imaging technologies such as 3D x-ray imaging, thermal imaging and terahertz imaging will be explored within the application field of 3D inspection and metrology of 3D printed parts.

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Simultaneous characterization and treatment of cancerous tissue using plasma 01/01/2023 - 31/12/2026

Abstract

In this project we propose a radically new plasma based-methodology to both characterize as well as treat cancerous tissue (with a focus on melanoma). We will use plasma excitation (in combination with laser vibration measurements) for in-situ characterization of the visco-elastic mechanical properties of biomedical tissue. These mechanical properties will allow us to detect and monitor cancerous tissue. Furthermore, we will develop a novel controlled plasma cancer treatment method which integrates the in-situ material identification method in order to tune the plasma therapy.

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  • Research Project

BITuminous Mortars: an Accelerated testing APproach (BITMA²P). 01/10/2022 - 30/09/2025

Abstract

Investigating the viscoelastic properties of bituminous mortars can lead to a better understanding of the mechanical behavior of asphalt mixtures and therefore, the design and construction of cost-effective and sustainable road structures. Even though bituminous mortar is considered as the missing link between binder and asphalt mixtures and is gaining increased worldwide attention, there are still no effective tests to quantify its viscoelastic behavior. The state-of-the-art methods to determine the properties of these materials are cyclic-loading tests, which are time-consuming and use classical measurement instruments that only provide a global view of the mechanical performance of the whole sample. In this research, novel accelerated testing procedures are proposed that use the full-field vibration response of the samples to estimate the complex modulus and fatigue properties of bituminous mortars. Different optical measurement techniques are used and combined to design and validate these novel methods. These methods will be a big step forward in the road engineering community since the testing time is reduced from hours/days to a few minutes. This offers the possibility to conduct research on more samples and improve the mixture designs. Furthermore, the full-field measurements with the combined optical systems can shed light on some of the highly investigated aspects of asphalt mixtures, such as blending efficiency, self-heating, and the location of microcracks.

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  • Research Project

Femtosecond pulsed laser micromachining for engineering, materials, and catalysis research. 01/05/2022 - 30/04/2026

Abstract

Through femtosecond pulsed laser micromachining a wide variety of materials such as ceramics (e.g. glass), hard metals (e.g. Hastelloy), and polymers can be processed with microscale resolution, offering innovation and beyond state-of-the-art research opportunities. To name a few, the planned research infrastructure would allow to tune the catalytic properties of surfaces, to enhance flow distribution, heat transfer and mass transfer in chemical reactors, to increase detection limit of photoelectrochemical sensors, to facilitate flow chemistry, to tailor-make EPR and TEM measurement cells, and to allow machine learning for hybrid additive manufacturing. Currently, the University of Antwerp lacks the necessary research infrastructure capable of processing such materials and surfaces with microscale precision. Access to femtosecond pulsed laser micromachining would yield enormous impact on ongoing and planned research both for the thirteen involved professors and ten research groups as for industry, essential to conduct research at the highest international level.

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Nexor - Cyber-Physical Systems for the Industry 4.0 era 01/01/2021 - 31/12/2026

Abstract

The fourth industrial revolution (Industry 4.0 as it is commonly referred to) is driven by extreme digitalization, enabled by tremendous computing capacity, smart collaborating machines and wireless computer networks. In the last six years, Nexor — a multi-disciplinary research consortium blending expertise from four Antwerp research labs — has built up a solid track record therein. We are currently strengthening the consortium in order to establish our position in the European eco-system. This project proposal specifies our 2021 - 2026 roadmap, with the explicit aim to empower industrial partners to tackle their industry 4.0 challenges. We follow a demand driven approach, convincing industrial partners to pick up our innovative research ideas, either by means of joint research projects (TRL 5—7) or via technology licenses.

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PhairywinD project 01/03/2020 - 28/02/2025

Abstract

In this PhD research we will develop a non-destructive testing technique for the automated and quantitative inspection of bare and coated steel structures during manufacturing and operation, using a hyperspectral camera. Whereas the human eye and color cameras perceive three colors in the visible range, a hyperspectral camera is able to capture several tens of images over a wider wavelength range. This facilitates observation of phenomena that cannot be observed with traditional cameras. Although hyperspectral cameras have already proven their merit in quality control of food products, their use for non-destructive testing is still at its infancy. The main limitation of hyperspectral imaging is the limited spatial resolution. We will develop an image processing technique to artificially increase the spatial resolution of hyperspectral images. We will deploy a technique to continuously scan over the surface of the structure in order to reduce inspection times and we will apply the hyperspectral NDT technique to deblur images.

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Industrial computer vision services enabled by a generic Python camera toolbox. 01/09/2023 - 31/08/2024

Abstract

The goal of this Service Platform project is firstly to provide feasibility and data acquisition services to companies interested in utilising cutting-edge camera technologies (like high-speed, thermal and hyperspectral cameras). Our services will assist companies in making better, well-informed decisions and offer valuable perspectives on utilising and integrating these technologies into their solutions. Secondly, we will offer easy-to-use software tools needed to integrate cameras quickly and in a standardised way into their customised implementations. Our user-friendly generic camera acquisition toolbox for Python (GenPyCam) will facilitate camera deployment (also on embedded and virtualised systems). In this way, image processing companies will be able to solve problems they could not tackle before, their capabilities in terms of camera types will increase, and their camera software implementation cost will decrease significantly.

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  • Research Project

SWIR and drones for early detection of oil spills in ports (SWIPE). 01/07/2022 - 31/12/2023

Abstract

The Port of Antwerp-Bruges is seeking solutions to perform accurate and fast detection of oil incidents in the port area. The port is polluted on a regular basis by oil which has a serious environmental and economical impact. Amongst the economical impacts are the costs for clean-up of the oil, which are related to its size, volume, the type of product and the location. These costs variate year to year, but in average we speak about the budget of 700K up to one million euro per year. The port is seeking solutions for early detection of the spills and follow up the spill while cleaning. Drones are seen as the ideal platform and are part of the port's future strategy towards digitization. To make drone inspections operational in 2021 is the priority of the Port of Antwerp-Bruges Authority. For this proposal, the port has identified two partners in Belgium, VITO and the University of Antwerp which have the necessary expertise to develop an appropriate solution. The objectives of this project are (1) to develop a prototype workflow to detect oil spills from a drone and (2) demonstrate the technology in the Port of Antwerp-Bruges. Port of Antwerp-Bruges Authority will launch a tender in Q3 2021 to select a company which will provide drone inspections in the port area. Automated oil spills detection is a part of the inspection program. Therefore it is very important for the Port of Antwerp-Bruges to help to develop this technology and to bring it to a higher TRL level. The Antwerp Port authority is co-funding by opening up their infrastructure, introducing an artificial spill and providing continuous feedback on the results. The result will be a prototype workflow to detect oil spills from drone images at TRL 5-6, accompanied by a protocol for the camera settings and flight protocol. The innovation in this proposal is linked to the challenging application of the technology in a complex and harsh port environment and the combined use of SWIR and RGB imagery.

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  • Research Project

Quantum Cascade Laser (QCL) voor in-situ spectroscopische beeldvorming vanop afstand. 01/06/2022 - 31/05/2024

Abstract

In many fields of science and technology, the Midwave Infrared range (MWIR) (4000-400 cm-1) is a highly relevant part of the electromagnetic spectrum, because in this region many compounds and materials feature a unique pattern of absorption bands, directly related to their molecular structure. Current MWIR spectroscopic techniques (like FT-IR) are often used, but these are relatively slow and can only be used in laboratory conditions (on small samples). In this project we will use a Quantum Cascade Laser (QCL) to enable in-situ remote spectroscopy. Reflection (solids) or transmission (liquids and gasses) spectra can be measured with a detector by (fast) scanning of the QCL wavelength (up to 10.000 cm-1 per seconds). We will use the QCL laser in combination with three detectors available at the UAntwerp-InViLab and AXIS research groups: 1) a deuterated triglycine sulfate, or DTGS detector, 2) a scanning laser Doppler vibrometer to perform remote scanning photoacoustic spectroscopy, 3) a thermal camera for mid-wave hyperspectral imaging. The three proposed QCL-based systems are complementary to each other: the DTGS enables a very high wavelength resolution, the LDV can be used for photoacoustic spectroscopy to perform measurements at large stand-off distances (up to 100m), and the thermal camera-based setup delivers a very high spatial resolution, but with a lower wavelength resolution. The QCL based system will be used for the research at InViLab and AXIS in different applications: artwork, corrosion, biomedical and textile inspection. Furthermore, we have identified several other potential applications that we will look into in the future together with other UAntwerp research teams: plasma chemistry, histopathology, road materials, metal oxide powders, meso-porous materials, drug detection, recycling of polymer materials, wastewater.

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Drone based infrared imaging for oil spill detection (DIOS). 01/01/2022 - 31/12/2023

Abstract

Oil spills in ports pose a direct risk to the environment and marine life, along with operational risks (sailing through the discharges, fouling of ships, fire risk) and high costs for the port itself. This project aims at an automatic detection, determination of severity and centralisation of communication based on advanced LWIR (longwave infrared) and multispectral SWIR (shortwave infrared) imaging via drones. Automation saves time, costs and creates a safer and cleaner port environment. and cleaner port environment.

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  • Research Project

Advanced Measurement techniques for data-driven Additive Manufacturing (AM2). 01/12/2021 - 30/11/2023

Abstract

In the last decades, metal additive manufacturing (AM) gradually evolved from a rapid prototyping technology to a promising transformative manufacturing technology. In the SBO-FWO DREAM project the UAntwerp research groups Visionlab and InViLab will tackle two important challenges that can transform AM into a reliable manufacturing solution for structurally loaded components: 1) the development of innovative online process monitoring solutions for increased part quality and 2) the development of advanced time-resolved computer tomography measurement techniques for part validation. The activities of the AM2 SEP research project will be integrated into the SBO-FWO DREAM project (if the re-submission of October 2021 is granted). A post-doctoral researcher will be hired to explore links between the technologies used in the two challenges of the project (process monitoring and part validation). In the case that the FWO SBO DREAM project is not granted, we will link with the AM group of VUB through the ongoing FWO SBO project HiPas, which has a similar scope, but with less focus on online process monitoring compared to the DREAM project. In addition, we will use the AM2 SEP budget to lift the SBO-FWO DREAM project to a European level (we have already identified two relevant calls: "HORIZON-CL4-2022-DIGITAL-EMERGING-01-05: AI, Data and Robotics for Industry optimisation", "HORIZON-CL4-2022-DIGITAL-EMERGING-01-03: Advanced multi-sensing systems"). To do so, we might hire a consultant to support us in the submission of a Horizon Europe RIA project. The work of the consultant will include performing a partner search, communication with partners and administrative support for the proposal preparation.

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Hyperspectral cameras for the efficient assessment of coatings, corrosion and material surfaces (HypIRspec). 01/11/2021 - 31/10/2023

Abstract

The overall objective of the project is to demonstrate the technical feasibility and economic added value of using of hyperspectral cameras for corrosion inspection and quality control of corrosion solutions (corrosion cleaning, wet and dry coatings and chemical surface treatments). The project focuses on the entire corrosion value chain. On the one hand, these are owners of infrastructure (petrochemical, port, energy), a group of some 100 mainly large Flemish companies, and on the other hand companies offering corrosion protection services (about 150). protection services (approximately 150 SMEs). For the rollout of the results, it is important that also companies that are active in corrosion inspection and camera integrators are also actively involved. These two groups of Flemish SMEs each count about 20 companies that, compared to the previously mentioned companies, have a larger R&D capacity to be able to implement the technology.

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  • Research Project

BITuminous Mortars: an Accelerated testing APproach (BITMA²P). 01/10/2021 - 30/09/2022

Abstract

Bituminous mortar is considered as the missing link between binder and asphalt mixtures and has gained worldwide attention over the past decade. Investigating the mechanical properties of bituminous mortars can lead to a better understanding of the mechanical behavior of asphalt mixtures and therefore, the design and construction of cost-effective and sustainable road structures. The state-of-the-art tests to find the properties of these materials are cyclic-loading tests, which are time-consuming and use classical measurement instruments that only provide a global view on the mechanical properties of the whole sample. In this research, novel accelerated mechanical testing procedures are proposed that use the full-field vibration of the samples to estimate the complex modulus of elasticity, fatigue properties, and healing potential of the investigated bituminous mortars. Different optical measurement techniques are used and combined to design and validate these novel methods. These methods can be a big step forward in the road engineering community since the testing time is reduced from hours/days to a few minutes. This offers the possibility to conduct research on many more samples and improve the mixture designs. Furthermore, the full-field measurements with the combined optical systems can shed light on some of the highly investigated aspects of asphalt mixtures such as blending efficiency, self-heating, or the localized healing of microcracks.

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    • Research Project

    Asset Inspection Platform. 01/06/2021 - 30/11/2022

    Abstract

    In this project, the research group InViLab develops machine learning techniques for the inspection of infrastructure (bridges, cranes, etc.) using images recorded by a drone. We will investigate how different camera types can be combined to improve the reliability.

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    Automated Open Precision Farming Platform (Utopia) 01/03/2021 - 29/02/2024

    Abstract

    Precision-farming needs large-scale adoption to increase production at such a level that it significantly contributes to minimizing the gap between actual and required world-production of food. Increasing the measurement and actuation intervals of e.g. monitoring for pests and watering are expected to contribute to e.g. increased yields. Sensing is an important element to quantify productivity, product quality and to make decisions. Applications, such as mapping, surveillance, exploration and precision agriculture, require a reliable platform for remote sensing. In precision agriculture, the goal is to gather and analyze information about the variability of soil/water and plant conditions in order to maximize the efficiency of the farm field. This would also increase the burden on the farmer, as the measurement-time and data-processing time increases significantly. This can be mitigated with Automated (cooperative) Precision Farming with the use of autonomous driving vehicles, vessels, drones and dedicated installations mounted on regular agri-machinery. For the cooperative robotic missions, the data will be tagged with accurate position information and merged with other data in order to create a digital map. To achieve good performance for an intelligent system in autonomous navigation tasks we will also build a 3D world model which will be integrated with a digital twin at plant level in order to improve the local path such that we obtain accurate information. To integrate the data from heterogeneous sensors, a platform will be developed to determine the practicality of the available sensors for the optimization of the spatio-temporal interpolation. This project will focus on a single (standardized) platform where (robotic)paths, monitoring strategies can be set and the drones/USV's/AGV's automatically deployed when certain conditions are met. The measurement data will be available for different stakeholders in the same platform.

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    Tales from the horned – Exploring the functionality and evolutionary history behind horn occurrence in vipers. 01/11/2020 - 31/10/2024

    Abstract

    Some species of snakes carry horn-like appendages either on the snout or above the eyes. Interestingly, these structures have evolved independently in multiple clades of vipers (Viperidae). Pioneer herpetologists have speculated wildly on the function of these enigmatic appendages, but nobody has studied them in detail. In this project, I will test the putative role of rostral and supra-ocular horns in concealment, water uptake, mechanosensation and thermoregulation. To that end, I will carry out a combination of behavioural observations, visual modelling, vibrometry, thermography, (electron) microscopy, histology, µ-CT scanning and 3D image reconstructions, on a selection of specimens of different species. In a final step of the project, I will combine information obtained from the functional analyses with data on the distribution, ecology and natural history of viperid species (as available in the literature and online databases) to test ideas on the ecological drivers of horn evolution.

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    D Thermal imaging of people using statistical shape models. 01/10/2020 - 30/09/2022

    Abstract

    In this project, we will develop an easy to use method to monitor the thermal condition of a person as a function of time, with potential applications entailed in physical treatment or a sports activity. The method employs amongst others thermal imaging. To that end, we create a virtual 3D model of the person of interest. The proposed technique will enable the development of a flexible and mobile measurement system, which can be used in labs, hospitals, rehabilitation centers, sports training facilities, etc.

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    Investigating fundamental plasma effects on tumor microenvironment through development of a controlled plasma treatment system for clinical cancer therapy. 01/01/2020 - 31/12/2023

    Abstract

    Non-thermal plasma technology is gaining attention as a novel cancer therapeutic. In the clinic, plasma has been applied to patients with head and neck squamous cell carcinoma, the 6th most common cancer worldwide with long-term survival below 50%. While initial studies are promising (e.g. partial remission, decreased levels of pain, no reported side-effects), a critical issue became apparent when translating plasma technology from the laboratory to the clinic: low reproducibility of treatment. Current plasma devices are handheld and require the operator (clinician) to make a judgement as to how long to treat the patient. This leads to large variability, which becomes even more pronounced when the clinician must move the plasma applicator over a large area of treatment. We aim to develop a robotic plasma treatment system that will enable us to investigate fundamental plasma effects on the tumor for clinical cancer therapy. We will use multiple sensors to detect the patient environment, artificial intelligence to 'learn and predict' patient disturbance patterns (e.g. breathing), and a robotic arm to deliver plasma. We will test our developed system in 3D and mouse cancer models and study the consequence of plasma treatment in the tumor, and to the survival of the animal. Altogether, our project will progress plasma technology for clinical translation by elucidating previously unknown biological responses to plasma and addressing issues in the clinic.

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    Automated inspection of infrastructure using drones (AutoDrone) 01/10/2019 - 31/12/2021

    Abstract

    In this project we will use drones to detect and monitor damage in infrastructure: wind turbines, bridges, buildings, solar panels, pavements, etc. Firstly, an overview of available path planning tools will be given. Secondly, we will develop machine learning tools to automatically detect damage (cracks, potholes, corrosion). The third aim of the project is the development of a methodology to allow a systematic comparison of repeated drone based camera measurements. During the project 9 case studies will be performed. The project is performed by UAntwerpen and WTCB together with a large consortium of companies active in drone based inspections or owners of infrastructure.

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    High-Precision Hybrid Laser-based Additive & Subtractive Manufacturing (Hi-PAS). 01/01/2019 - 31/12/2022

    Abstract

    The consortium aims to achieve two main goals with this SBO project. First, through a rigorous research methodology to better understand how the roughness fatigue life of additive-made metallic components can be significantly improved. We anticipate this by rolling out a multidisciplinary approach, i.e. in terms of surface and shape metrology, non-invasive quantification of the residual stress and mapping of the process parameters that have an influence on the corrosion mechanisms. Moreover, a strong asset in this project is the possibility to also investigate the interrelationships between phenomena. The second main goal is to build up fundamental knowledge on how the laser-based hybrid production process can be substantially improved: In particular to be able to make complex-shaped metallic components with high precision and this without further intensive post-processing (in particular also known as " first-time-right approach).

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    Inspection of road pavements 01/11/2018 - 31/10/2019

    Abstract

    In this research project we want to take a further step in refining and concretising techniques for an automatic road surface quality inspection for application in the Flemish region, in order to simplify road management and save costs by having the road surface at the right times at the right times to renovate or renew places.

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    Development of a novel optical signal processing method for analyzing data of the deformations of the asphalt construction by using Fiber Bragg technology in order to design new asphalt model. 01/07/2018 - 31/12/2019

    Abstract

    This project focuses on collecting and verifying reliable deformation data of asphalt pavements by using Fiber Bragg Grating sensors. These sensors are already integrated in a bicycle path at the University of Antwerp (project CyPaTs at Campus Groenenborger). FBG is a new technology for measuring deformations in a material, e.g. by external loading. In asphalt pavement, the service life of the lower positioned asphalt layers is directly related to these deformations, loadings and rest periods between loadings. Nowadays, this service life is monitored by Falling Weight Deflection (FWD) measurements only for primary road network and each two years. These measurements are time-consuming, expensive and the road needs to be closed for a certain time. The FBG technology could give a solution to measure these deformations continuously for a lower cost. Moreover, FBG will give more insight in the deformation under all available conditions (temperature of the road, different loadings, rest periods). In order to predict service life, an asphalt response model needs to be developed, based on a monitoring program over at least 1 year. The project will allow to determine long-term ageing and healing properties of the pavement. In this project both technology domains will be used: FBG data will give the deformations in the structure in such a way that the parameters of a visco-elastic plastic asphalt model are optimized continuously. The installed FBG monitoring system of CyPats will be used in this project. Data will be gathered by means of a monitoring campaign in normal conditions (climate) and forced-conditioned on site; calibrated loadings and rest periods. These data will be used for fitting the parameters of a simple response model by Young modulus. The data can be used in future work for parametric fit in more complex models, e.g. a visco-elastic (Burgers) and a visco-elastic plastic model (Huet-Sayegh). A first step will be taken in this project. A challenge to be encountered is to distinguish the effect of ageing and healing, e.g. increase of resistance to deformation during a rest period after a loading set. In current models these are not taken into account and the expected service life has to be estimated by doing FWD tests with a lot of variance in results. Moreover, in the FBG setting, the ageing is monitored continuously. This will give insight in the ageing mechanism in time of asphalt pavements allowing to use this factor as fundamental knowledge. The ageing factor will be used in a complex response model and in a prediction model for estimated service life. Moreover in the future, with this knowledge, a new ageing method under laboratory conditions can be developed based on the measurements on site. The project work program consists of 3 workpackages. The first workpackage focuses on the signal processing of optical FBG spectra i.e. how to determine the peak shifts in order to obtain a correct strain value. workpackage 2 focuses on the identification of the Young modulus from FBG vibration measurements using the so-called inverse modelling approach to identify the mechanical material properties of the different layers of the asphalt, starting from a simple elastic Young's modulus model. Workpackage 3 deals with the monitoring of the Young modulus in time on the asphalt pavement structure of CyPaTs bicycle path during 24 months, and relating these to more complex models.

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      Sensing and simulation for smart assembly and logistics (SENSALO) 01/10/2017 - 30/09/2019

      Abstract

      In the project we use 3D vision techniques in order to make the assembly process more efficient and safer. This is done by tracking people, products and machines (like cobots) in a manufacturing environment.

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      Non-linear and time-varying data-based modeling of rotating machinery. 01/07/2016 - 31/12/2017

      Abstract

      Rotating machines appear in many application fields ranging from large scale applications (e.g. wind turbines) to smaller ones (e.g. medical fluid pumps). The availability of a mathematical model for the dynamical behavior is of crucial importance for the design, prediction and control of these rotating systems. In the scientific domain of "system identification", the mathematical model of the system under test is retrieved through experimental input-output data. Since the dynamical characterization of rotating machines is non-linear as well as time-varying, it cannot be modeled adequately using classical existing estimation or identification methods. The aim of this project is then to develop a theoretical framework to model the time-varying and non-linear dynamical behavior of rotating machinery from experimental data. The proposed methodology consists of modeling the non-linear and time-varying dynamical character of rotating devices through a collection of linear periodically time-varying models. In this project, we will focus on the identification and validation of the non-linear and time-varying dynamics of a mechanical rotor suspended on hydrodynamic plain bearings. The novel approach consists of four main steps: (i) Construction of a non-linear and time-varying virtual model of "fluid-driven" bearing—rotor systems starting from the laws of physics; (ii) Development of a parametric identification technique; (iii) realization and adjustments of the controllable rotor—bearing setup; (iv) validation of the theoretical framework on the real-life rotor—bearing setup.

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      Characterization of advanced materials using hybrid inverse modelling from full-field optical vibration measurements. 01/11/2015 - 31/10/2019

      Abstract

      Quantitative values for mechanical properties of materials are required in the simulation of the behavior of structures and systems in several engineering domains: civil engineering (buildings, bridges, roads, …), mechanical engineering (aircraft, cars, …), biomedical engineering (implants, scaffolds, etc.) and electronic engineering (semiconductor materials). In addition, the knowledge of these material properties provides a means to follow-up the health of a structure or system during operation and to estimate the remaining lifetime. The proposed novel hybrid material characterization method combines two distinct approaches to estimate mechanical material parameters, which has never been attempted before. By using laser Doppler vibrometry for the optical measurement of both resonating (at low frequencies) and propagating surface waves (at high frequencies), modal parameters and wave propagation characteristics can be derived simultaneously. By comparing these results with Finite Element and analytical models and by using an inverse modelling approach with intelligent optimization algorithms, it will be possible to identify more material parameters with an improved accuracy in a reduced measuring time. This will allow applications on more complex materials (e.g. layered poro-elastic road surface) in an in-situ environment. The proposed method will lead to several innovations, in the fields of measuring, data processing and optimization, and will be validated in three different applications: asphalt pavements (civil engineering), composite materials (mechanical engineering), and a tympanic membrane and bone material (biomedical engineering).

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        • Research Project

        3D imaging assisted vibration measurements for product testing and quality control. 01/10/2015 - 30/09/2019

        Abstract

        In this project we will develop a technique that combines information from 3D time-of-flight camera's and computer-aided-design drawings of a product in order to facilitate product testing and quality control. The proposed procedure firstly allows the test engineer to automatically determine the sensor positions on a product. Secondly, we develop a methodology to perform vibration measurements on moving components (wind turbines, wind screen wipers, etc. or products on a conveyor belt).

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        • Research Project

        Frequency domain identification of quasi time-periodic systems with applications in the mechanical and biomedical engineering. 01/10/2015 - 30/04/2017

        Abstract

        Quasi time-periodic phenomena show up in many engineering fields. One could think of wind turbines or helicopters with rotational speed fluctuations, the vibrations and acoustic noise generated in combustion engines, the electrical impedance of a living heart with heart rate variability for cardio-vascular monitoring, respiratory systems with breathing rate variability, to name a few. Those systems in engineering have the special property that their dynamic behavior changes quasi-periodically over time. The irregularity of the periodicity in the above-mentioned applications can be faithfully modeled by virtue of a periodically time-varying model with varying periodicity. This way of modeling bridges the gap between the well established identification framework for linear time-invariant systems and the more complex approaches for non-linear time-variant systems. The extraction of experimental quasi time-periodic models in the frequency domain meant for physical interpretation, analysis, prediction or control can be a useful step for the practicing engineer. Hence, the main focus of this project will be the development of a generalized identification framework for quasi time-periodic systems with applications in the mechanical and bio-medical engineering.

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        • Research Project

        Next generation of heterogeneous sensor networks (NEXOR). 01/01/2015 - 31/12/2020

        Abstract

        This project represents a research contract awarded by the University of Antwerp. The supervisor provides the Antwerp University research mentioned in the title of the project under the conditions stipulated by the university.

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        • Research Project