Mechatronics at the University of Twente

This paper describes some of the mechatronics activities at the University of Twente. In 1989, the founding of the Mechatronics Research Center Twente started a cooperation of the departments of Electrical Engineering, Mechanical Engineering, Applied Mathematics and Computer Science. The mechatronics activities get especially attention in projects in the Ph.D. programme and in the `mechatronic designer' program, but Msc. students participate as well. As an illustration of the philosophy behind the work at the University of Twente and of the activities carried out so far, the paper describes two projects of the institute: the MART (Mobile Autonomous Robot Twente) project and the ALASCA (Automated Laser Aided Servo Controlled Assembly) projec

Deflecting small asteroids using laser ablation : Deep space navigation and asteroid orbit control for LightTouch2 Mission

This paper presents a low-cost, low mass, mission design to successfully intercept and deflect a small and faint, 4 m in diameter asteroid. Intended to be launched after 2025, the laser-ablating mission, LightTouch2 will be used to deflect the orbit of the asteroid by at least 1 m/s. This will be achieved with a total mission lifetime of less than three years. Analysis includes the initial approach of the spacecraft, the operations of the laser at an optimal spacecraft-to-asteroid distance of 50 m and the relative orbit of the spacecraft that flies in formation with the asteroid. Analysis includes line-of-sight measurements with radiometric tracking from ground station to improve the trajectory estimate and observability of the spacecraft, collision avoidance and mapping strategies. The spacecraft will also need optimal discrete control. This is achieved by impulse-bit manoeuvres used to account for the perturbations caused by the resultant thrust on the asteroid, plume impingement, laser recoil and solar radiation pressure. The spacecraft controls its trajectory within a 1 m box from the reference trajectory to enable the laser to optimally focussing the laser beam. The proposed approach uses an unscented Kalman filter to estimate the relative spacecraft-asteroid position, velocity and perturbative acceleration

Development of an autonomous lab-on-a-chip system with ion separation and conductivity detection for river water quality monitoring

This thesis discusses the development of a lab on a chip (LOC) ion separation for river water quality monitoring using a capacitively coupled conductivity detector (C⁴D) with a novel baseline suppression technique.Our first interest was to be able to integrate such a detector in a LOC. Different designs (On-capillary design and on-chip design) have been evaluated for their feasibility and their performances. The most suitable design integrated the electrode close to the channel for an enhanced coupling while having the measurement electronics as close as possible to reduce noise. The final chip design used copper tracks from a printed circuit board (PCB) as electrodes, covered by a thin Polydimethylsiloxane (PDMS) layer to act as electrical insulation. The layer containing the channel was made using casting and bonded to the PCB using oxygen plasma. Flow experiments have been conduced to test this design as a detection cell for capacitively coupled contactless conductivity detection (C⁴D).The baseline signal from the system was reduced using a novel baseline suppression technique. Decrease in the background signal increased the dynamic range of the concentration to be measured before saturation occurs. The sensitivity of the detection system was also improved when using the baseline suppression technique. Use of high excitation voltages has proven to increase the sensitivity leading to an estimated limit of detection of 0.0715 μM for NaCl (0.0041 mg/L).The project also required the production of an autonomous system capable of operating for an extensive period of time without human intervention. Designing such a system involved the investigation of faults which can occur in autonomous system for the in-situ monitoring of water quality. Identification of possible faults (Bubble, pump failure, etc.) and detection methods have been investigated. In-depth details are given on the software and hardware architecture constituting this autonomous system and its controlling software

Vibration control of ultra-high precision magnetic leadscrew using recurrent neural network

Ultra-high precision positioning is of strategic importance to modern industrial processes such as semiconductor manufacturing. Traditional drives with mechanical transmission elements exhibit nonlinearities such as friction, backlash and hysteresis which limit the system performance significantly. The magnetic leadscrew in this work belongs to the class of contactless drives which overcome the above mentioned limitations of contact-type drives. The operation is based on leadscrew/nut coupling but unlike mechanical Ieadscrews, the threads of the nut and the leadscrew are aligned magnetically and do not come in contact. Thus, hard nonlinearities are substantially reduced resulting in high precision and high resolution. The dynamics of the system are, however, lightly damped and result in vibration of the nut upto tens of microns peak-to-peak. Due to the high frequency of the modes, typically a few hundred Hz, the dynamics are difficult to control using conventional techniques, limited actuator bandwidth being one of the reasons. Active control must therefore be employed. This work develops a passband control scheme based on the Hilbert Transform which gives the orthogonal components of the oscillating modes. The components are extracted using a neural network to enhance the robustness of the controller. Performance of the controller is evaluated under self-resonance, forced oscillation and transient response. Self-resonance is shown to be completely eliminated while for forced oscillation, the axial gain is shown to be reduced. Stabilization time of the transient response is also significantly reduced, thereby confirming the vibration suppression capabilities of the controller

3D scanning, modelling and printing of ultra-thin nacreous shells from Jericho: a case study of small finds documentation in archaeology

This paper springs out from a collaborative project jointly carried out by the FabLab Saperi&Co and the Museum of Near East, Egypt and Mediterranean of Sapienza University of Rome focused at producing replicas of ultra-thin archeological finds with a sub-millimetric precision. The main technological challenge of this project was to produce models through 3D optical scanning (photogrammetry) and to print faithful replicas with additive manufacturing. The objects chosen for the trial were five extremely fragile and ultra-thin nacreous shells retrieved in Tell es-Sultan/ancient Jericho by the Italian-Palestinian Expedition in spring 2017, temporarily on exhibit in the Museum. The experiment proved to be successful, and the scanning, modeling and printing of the shells also allowed some observations on their possible uses in research and museum activities

Computer Vision System for Non-Destructive and Contactless Evaluation of Quality Traits in Fresh Rocket Leaves (Diplotaxis Tenuifolia L.)

La tesi di dottorato è incentrata sull'analisi di tecnologie non distruttive per il controllo della qualità dei prodotti agroalimentari, lungo l'intera filiera agroalimentare. In particolare, la tesi riguarda l'applicazione del sistema di visione artificiale per valutare la qualità delle foglie di rucola fresh-cut. La tesi è strutturata in tre parti (introduzione, applicazioni sperimentali e conclusioni) e in cinque capitoli, rispettivamente il primo e il secondo incentrati sulle tecnologie non distruttive e in particolare sui sistemi di computer vision per il monitoraggio della qualità dei prodotti agroalimentari. Il terzo, quarto e quinto capitolo mirano a valutare le foglie di rucola sulla base della stima di parametri qualitativi, considerando diversi aspetti: (i) la variabilità dovuta alle diverse pratiche agricole, (ii) la senescenza dei prodotti confezionati e non, e (iii) lo sviluppo e sfruttamento dei vantaggi di nuovi modelli più semplici rispetto al machine learning utilizzato negli esperimenti precedenti. Il lavoro di ricerca di questa tesi di dottorato è stato svolto dall'Università di Foggia, dall'Istituto di Scienze delle Produzioni Alimentari (ISPA) e dall'Istituto di Tecnologie e Sistemi Industriali Intelligenti per le Manifatture Avanzate (STIIMA) del Consiglio Nazionale delle Ricerche (CNR). L’attività di ricerca è stata condotta nell'ambito del Progetto SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), finanziato dal MUR-PRIN 2017, e volto a sostenere la qualità della produzione e dell'ambiente utilizzando pratiche agricole a basso input e la valutazione non distruttiva della qualità di prodotti ortofrutticoli.The doctoral thesis focused on the analysis of non-destructive technologies available for the control quality of agri-food products, along the whole supply chain. In particular, the thesis concerns the application of computer vision system to evaluate the quality of fresh rocket leaves. The thesis is structured in three parts (introduction, experimental applications and conclusions) and in 5 chapters, the first and second focused on non-destructive technologies and in particular on computer vision systems for monitoring the quality of agri-food products, respectively. The third, quarter, and fifth chapters aim to assess the rocket leaves based on the estimation of quality aspects, considering different aspects: (i) the variability due to the different agricultural practices, (ii) the senescence of packed and unpacked products, and (iii) development and exploitation of the advantages of new models simpler than the machine learning used in the previous experiments. The research work of this doctoral thesis was carried out by the University of Foggia, the Institute of Science of Food Production (ISPA) and the Institute of Intelligent Industrial Technologies and Systems for Advanced Manufacturing (STIIMA) of National Research Council (CNR). It was conducted within the Project SUS&LOW (Sustaining Low-impact Practices in Horticulture through Non-destructive Approach to Provide More Information on Fresh Produce History & Quality), funded by MUR- PRIN 2017, and aimed at sustaining quality of production and of the environment using low input agricultural practices and non-destructive quality evaluation