Analysis of aircraft load spectrum by means of flight simulator

Damage tolerance analysis is a quite new methodology based on prescribed inspections. The load spectra used to derive results of these analysis strongly influence the final defined inspections programs that for this reason must be as much as possible representative of load acting on the considered structural component and at the same time, obtained reducing both cost and time. The principal purpose of our work is in improving the actual condition developing a complete numerical Damage Tolerance analysis, able to prescribe inspection programs on typical aircraft critical components, respecting DT regulations, starting from much more specific load spectrum then those actually used today. In particular, these more specific load spectrum to design against fatigue have been obtained through an appositively derived flight simulator developed in a Matlab/Simulink environment. This dynamic model has been designed so that it can be used to simulate typical missions performing manually (joystick inputs) or completely automatic (reference trajectory need to be provided) flights. Once these flights have been simulated, model’s outputs are used to generate load spectrum that are then processed to get information (peaks, valleys) to perform statistical and/or comparison consideration with other load spectrum. However, also much more useful information (loads amplitude) have been extracted from these generated load spectrum to perform the previously mentioned predictions (Rainflow counting method is applied for this purpose). The entire developed methodology works in a complete automatic way, so that, once some specified input parameters have been introduced and different typical flights have been simulated both, manually or automatically, it is able to relate the effects of these simulated flights with the reduction of residual strength of the considered component.

Proposal of a Continuous Read-out Implementation in the ALICE-TOF detector

The main purpose of ultrarelativistic heavy-ion collisions is the investigation of the QGP. The ALICE experiment situated at the CERN has been specifically designed to study heavy-ion collisions for centre-of-mass energies up to 5.5 per nucleon pair. Extended particle identification capability is one of the main characteristics of the ALICE experiment. In the intermediate momentum region (up to 2.5 GeV/c for pi/K and 4 GeV/c for K/p), charged particles are identified in the ALICE experiment by the Time of Flight (TOF) detector. The ALICE-TOF system is a large-area detector based on the use of Multi-gap Resistive Plate Chamber (MRPC) built with high efficiency, fast response and intrinsic time resolution better than 40 ps. This thesis work, developed with the ALICE-TOF Bologna group, is part of the efforts carried out to adapt the read-out of the detector to the new requirements after the LHC Long Shutdown 2. Tests on the feasibility of a new read-out scheme for the TOF detector have been performed. In fact, the achievement of a continuous read-out also for the TOF detector would not be affordable if one considers the replacement of the TRM cards both for hardware and budget reasons. Actually, the read-out of the TOF is limited at 250 kHz i.e. it would be able to collect up to just a fourth of the maximum collision rate potentially achievable for pp interactions. In this Master’s degree thesis work, I discuss a different read-out system for the ALICE-TOF detector that allows to register all the hits at the interaction rate of 1 MHz foreseen for pp interactions after the 2020, by using the electronics currently available. Such solution would allow the ALICE-TOF detector to collect all the hits generated by pp collisions at 1 MHz interaction rate, which corresponds to an amount four times larger than that initially expected at such frequencies with the triggered read-out system operated at 250 kHz for LHC Run 3. The obtained results confirm that the proposed read-out scheme is a viable option for the ALICE TOF detector. The results also highlighted that it will be advantageous if the ALICE-TOF group also implement an online monitoring system of noisy channels to allow their deactivation in real time.

Deep Learning Architectures for time of arrival detection in Acoustic Emissions Monitoring

Acoustic Emission (AE) monitoring can be used to detect the presence of damage as well as determine its location in Structural Health Monitoring (SHM) applications. Information on the time difference of the signal generated by the damage event arriving at different sensors is essential in performing localization. This makes the time of arrival (ToA) an important piece of information to retrieve from the AE signal. Generally, this is determined using statistical methods such as the Akaike Information Criterion (AIC) which is particularly prone to errors in the presence of noise. And given that the structures of interest are surrounded with harsh environments, a way to accurately estimate the arrival time in such noisy scenarios is of particular interest. In this work, two new methods are presented to estimate the arrival times of AE signals which are based on Machine Learning. Inspired by great results in the field, two models are presented which are Deep Learning models - a subset of machine learning. They are based on Convolutional Neural Network (CNN) and Capsule Neural Network (CapsNet). The primary advantage of such models is that they do not require the user to pre-define selected features but only require raw data to be given and the models establish non-linear relationships between the inputs and outputs. The performance of the models is evaluated using AE signals generated by a custom ray-tracing algorithm by propagating them on an aluminium plate and compared to AIC. It was found that the relative error in estimation on the test set was < 5% for the models compared to around 45% of AIC. The testing process was further continued by preparing an experimental setup and acquiring real AE signals to test on. Similar performances were observed where the two models not only outperform AIC by more than a magnitude in their average errors but also they were shown to be a lot more robust as compared to AIC which fails in the presence of noise.

Factors affecting the mission time of an autonomous mobile robot: a theoretical and experimental investigation

I vantaggi dell’Industria 4.0 hanno stravolto il manufacturing. Ma cosa vuol dire "Industria 4.0"? Essa è la nuova frontiera del manufacturing, basata su princìpi che seguono i passi avanti dei sistemi IT e della tecnologia. Dunque, i suoi pilastri sono: integrazione, verticale e orizzontale, digitalizzazione e automazione. L’Industria 4.0 coinvolge molte aree della supply chain, dai flussi informativi alla logistica. In essa e nell’intralogistica, la priorità è sviluppare dei sistemi di material handling flessibili, automatizzati e con alta prontezza di risposta. Il modello ideale è autonomo, in cui i veicoli fanno parte di una flotta le cui decisioni sono rese decentralizzate grazie all'alta connettività e alla loro abilità di collezionare dati e scambiarli rapidamente nel cloud aziendale.Tutto ciò non sarebbe raggiungibile se ci si affidasse a un comune sistema di trasporto AGV, troppo rigido e centralizzato. La tesi si focalizza su un tipo di material handlers più flessibile e intelligente: gli Autonomous Mobile Robots. Grazie alla loro intelligenza artificiale e alla digitalizzazione degli scambi di informazioni, interagiscono con l’ambiente per evitare ostacoli e calcolare il percorso ottimale. Gli scenari dell’ambiente lavorativo determinano perdite di tempo nel tragitto dei robot e sono queste che dovremo studiare. Nella tesi, i vantaggi apportati dagli AMR, come la loro decentralizzazione delle decisioni, saranno introdotti mediante una literature review e poi l’attenzione verterà sull’analisi di ogni scenario di lavoro. Fondamentali sono state le esperienze nel Logistics 4.0 Lab di NTNU, per ricreare fisicamente alcuni scenari. Inoltre, il software AnyLogic sarà usato per riprodurre e simulare tutti gli scenari rilevanti. I risultati delle simulazioni verranno infine usati per creare un modello che associ ad ogni scenario rilevante una perdita di tempo, attraverso una funzione. Per questo saranno usati software di data analysis come Minitab e MatLab.