IDeS methodology applied to automotive component re-engineering focused on lightweight and high-strength by additive manufacturing

The project aims to gather an understanding of additive manufacturing and other manufacturing 4.0 techniques with an eyesight for industrialization. First the internal material anisotropy of elements created with the most economically feasible FEM technique was established. An understanding of the main drivers for variability for AM was portrayed, with the focus on achieving material internal isotropy. Subsequently, a technique for deposition parameter optimization was presented, further procedure testing was performed following other polymeric materials and composites. A replicability assessment by means of the use of technology 4.0 was proposed, and subsequent industry findings gathered the ultimate need of developing a process that demonstrate how to re-engineer designs in order to show the best results with AM processing. The latest study aims to apply the Industrial Design and Structure Method (IDES) and applying all the knowledge previously stacked into fully reengineer a product with focus of applying tools from 4.0 era, from product feasibility studies, until CAE – FEM analysis and CAM – DfAM. These results would help in making AM and FDM processes a viable option to be combined with composites technologies to achieve a reliable, cost-effective manufacturing method that could also be used for mass market, industry applications.

In-car sound linear simulation

In recent years, vehicle acoustics have gained significant importance in new car development: increasingly advanced infotainment systems for spatial audio and sound enhancement algorithms have become the norm in modern vehicles. In the past, car manufacturers had to build numerous prototypes to study the sound behaviour inside the car cabin or the effect of new algorithms under development. Nowadays, advanced simulation techniques can reduce development costs and time. In this work, after selecting the reference test vehicle, a modern luxury sedan equipped with a high-end sound system, two independent tools were developed: a simulation tool created in the Comsol Multiphysics environment and an auralization tool developed in the Cycling ‘74 MAX environment. The simulation tool can calculate the impulse response and acoustic spectrum at a specific position inside the cockpit. Its input data are the vehicle’s geometry, acoustic absorption parameters of materials, the acoustic characteristics and position of loudspeakers, and the type and position of virtual microphones (or microphone arrays). The simulation tool can also provide binaural impulse responses thanks to Head Related Transfer Functions (HRTFs) and an innovative algorithm able to compute the HRTF at any distance and angle from the head. Impulse responses from simulations or acoustic measurements inside the car cabin are processed and fed into the auralization tool, enabling real-time interaction by applying filters, changing the channels gain or displaying the acoustic spectrum. Since the acoustic simulation of a vehicle involves multiple topics, the focus of this work has not only been the development of two tools but also the study and application of new techniques for acoustic characterization of the materials that compose the cockpit and the loudspeaker simulation. Specifically, three different methods have been applied for material characterization through the use of a pressure-velocity probe, a Laser Doppler Vibrometer (LDV), and a microphone array.

High-strength steels manufactured via Laser Powder Bed Fusion: effect of post-process heat treatments and surface finishing on microstructure and mechanical properties

Laser Powder Bed Fusion (LPBF) permits the manufacturing of parts with optimized geometry, enabling lightweight design of mechanical components in aerospace and automotive and the production of tools with conformal cooling channels. In order to produce parts with high strength-to-weight ratio, high-strength steels are required. To date, the most diffused high-strength steels for LPBF are hot-work tool steels, maraging and precipitation-hardening stainless steels, featuring different composition, feasibility and properties. Moreover, LPBF parts usually require a proper heat treatment and surface finishing, to develop the desired properties and reduce the high roughness resulting from LPBF. The present PhD thesis investigates the effect of different heat treatments and surface finishing on the microstructure and mechanical properties of a hot-work tool steel and a precipitation-hardening stainless steel manufactured via LPBF. The bibliographic section focuses on the main aspects of LPBF, hot-work tool steels and precipitation-hardening stainless steels. The experimental section is divided in two parts. Part A addresses the effect of different heat treatments and surface finishing on the microstructure, hardness, tensile and fatigue behaviour of a LPBF manufactured hot-work tool steel, to evaluate its feasibility for automotive and racing components. Results indicated the possibility to achieve high hardness and strength, comparable to the conventionally produced steel, but a great sensitivity of fatigue strength on defects and surface roughness resulting from LPBF. Part B investigates the effect of different heat treatments on the microstructure, hardness, tensile and notch-impact behaviour of a LPBF produced precipitation-hardening stainless steel, to assess its feasibility for tooling applications. Results indicated the possibility to achieve high hardness and strength also through a simple Direct Aging, enabling heat treatment simplification by exploiting the microstructural features resulting from LPBF.

Optimizing fused deposition modelling: the impact of geometrical slicing parameters on mechanical properties

This comprehensive study explores the intricate world of 3D printing, with a focus on Fused Deposition Modelling (FDM). It sheds light on the critical factors that influence the quality and mechanical properties of 3D printed objects. Using an optical microscope with 40X magnification, the shapes of the printed beads is correlated to specific slicing parameters, resulting in a 2D parametric model. This mathematical model, derived from real samples, serves as a tool to predict general mechanical behaviour, bridging the gap between theory and practice in FDM printing. The study begins by emphasising the importance of geometric parameters such as layer height, line width and filament tolerance on the final printed bead geometry and the resulting theoretical effect on mechanical properties. The introduction of VPratio parameter (ratio between the area of the voids and the area occupied by printed material) allows the quantification of the variation of geometric slicing parameters on the improvement or reduction of mechanical properties. The study also addresses the effect of overhang and the role of filament diameter tolerances. The research continues with the introduction of 3D FEM (Finite Element Analysis) models based on the RVE (Representative Volume Element) to verify the results obtained from the 2D model and to analyse other aspects that affect mechanical properties and not directly observable with the 2D model. The study also proposes a model for the examination of 3D printed infill structures, introducing also an innovative methodology called “double RVE” which speeds up the calculation of mechanical properties and is also more computationally efficient. Finally, the limitations of the RVE model are shown and a so-called Hybrid RVE-based model is created to overcome the limitations and inaccuracy of the conventional RVE model and homogenization procedure on some printed geometries.

Novel methods of state estimation and parameter identification for high-performance motorcycles

This research activity aims at providing a reliable estimation of particular state variables or parameters concerning the dynamics and performance optimization of a MotoGP-class motorcycle, integrating the classical model-based approach with new methodologies involving artificial intelligence. The first topic of the research focuses on the estimation of the thermal behavior of the MotoGP carbon braking system. Numerical tools are developed to assess the instantaneous surface temperature distribution in the motorcycle's front brake discs. Within this application other important brake parameters are identified using Kalman filters, such as the disc convection coefficient and the power distribution in the disc-pads contact region. Subsequently, a physical model of the brake is built to estimate the instantaneous braking torque. However, the results obtained with this approach are highly limited by the knowledge of the friction coefficient (μ) between the disc rotor and the pads. Since the value of μ is a highly nonlinear function of many variables (namely temperature, pressure and angular velocity of the disc), an analytical model for the friction coefficient estimation appears impractical to establish. To overcome this challenge, an innovative hybrid solution is implemented, combining the benefit of artificial intelligence (AI) with classical model-based approach. Indeed, the disc temperature estimated through the thermal model previously implemented is processed by a machine learning algorithm that outputs the actual value of the friction coefficient thus improving the braking torque computation performed by the physical model of the brake. Finally, the last topic of this research activity regards the development of an AI algorithm to estimate the current sideslip angle of the motorcycle's front tire. While a single-track motorcycle kinematic model and IMU accelerometer signals theoretically enable sideslip calculation, the presence of accelerometer noise leads to a significant drift over time. To address this issue, a long short-term memory (LSTM) network is implemented.

Two stroke cycle, novel combustion concepts and electrification for a new generation of internal combustion engines

The pursuit of decarbonization and increased efficiency in internal combustion engines (ICE) is crucial for reducing pollution in the mobility sector. While electrification is a long-term goal, ICE still has a role to play if coupled with innovative technologies. This research project explores various solutions to enhance ICE efficiency and reduce emissions, including Low Temperature Combustion (LTC), Dual fuel combustion with diesel and natural gas, and hydrogen integration. LTC methods like Dual fuel and Reactivity Controlled Compression Ignition (RCCI) show promise in lowering emissions such as NOx, soot, and CO2. Dual fuel Diesel-Natural Gas with hydrogen addition demonstrates improved efficiency, especially at low loads. RCCI Diesel-Gasoline engines offer increased Brake Thermal Efficiency (BTE) compared to standard diesel engines while reducing specific NOx emissions. The study compares 2-Stroke and 4-Stroke engine layouts, optimizing scavenging systems for both aircraft and vehicle applications. CFD analysis enhances specific power output while addressing injection challenges to prevent exhaust short circuits. Additionally, piston bowl shape optimization in Diesel engines running on Dual fuel (Diesel-Biogas) aims to reduce NOx emissions and enhance thermal efficiency. Unconventional 2-Stroke architectures, such as reverse loop scavenged with valves for high-performance cars, opposed piston engines for electricity generation, and small loop scavenged engines for scooters, are also explored. These innovations, alongside ultra-lean hydrogen combustion, offer diverse pathways toward achieving climate neutrality in the transport sector.

Analysis and prediction of fretting fatigue life.

Fretting fatigue is a fatigue damage process that occurs when two surfaces in contact with each other are subjected to relative micro-slip, causing a reduced fatigue life with respect to the plain fatigue case. Fretting has been now studied deeply for over 50 years, but still no univocal design approach has been universally accepted. This thesis presents a method for predicting the fretting fatigue life of materials based on the material specific fatigue parameters. To validate the method, a set of fretting fatigue experimental tests have been run, using a newly designed specimen. FE analyses of the tests were also run and the SWT parameter was retrieved and it was found to be useful to successfully identify which samples failed. Finally, S-N curves were retrieved by using two different fatigue life predicting methods (CoffinManson and Jahed-Varvani). The two different methods were compared with the experimental results and it was found that the Jahed-Varvani method gave accurate results in terms of fretting fatigue life.

6G enabled IoV scenarios with distributed intelligence mechanisms

The Internet of Vehicles (IoV) paradigm has emerged in recent times, where with the support of technologies like the Internet of Things and V2X , Vehicular Users (VUs) can access different services through internet connectivity. With the support of 6G technology, the IoV paradigm will evolve further and converge into a fully connected and intelligent vehicular system. However, this brings new challenges over dynamic and resource-constrained vehicular systems, and advanced solutions are demanded. This dissertation analyzes the future 6G enabled IoV systems demands, corresponding challenges, and provides various solutions to address them. The vehicular services and application requests demands proper data processing solutions with the support of distributed computing environments such as Vehicular Edge Computing (VEC). While analyzing the performance of VEC systems it is important to take into account the limited resources, coverage, and vehicular mobility into account. Recently, Non terrestrial Networks (NTN) have gained huge popularity for boosting the coverage and capacity of terrestrial wireless networks. Integrating such NTN facilities into the terrestrial VEC system can address the above mentioned challenges. Additionally, such integrated Terrestrial and Non-terrestrial networks (T-NTN) can also be considered to provide advanced intelligent solutions with the support of the edge intelligence paradigm. In this dissertation, we proposed an edge computing-enabled joint T-NTN-based vehicular system architecture to serve VUs. Next, we analyze the terrestrial VEC systems performance for VUs data processing problems and propose solutions to improve the performance in terms of latency and energy costs. Next, we extend the scenario toward the joint T-NTN system and address the problem of distributed data processing through ML-based solutions. We also proposed advanced distributed learning frameworks with the support of a joint T-NTN framework with edge computing facilities. In the end, proper conclusive remarks and several future directions are provided for the proposed solutions.

Development and testing of methodologies for the control and diagnosis of innovative combustion systems

In pursuit of aligning with the European Union's ambitious target of achieving a carbon-neutral economy by 2050, researchers, vehicle manufacturers, and original equipment manufacturers have been at the forefront of exploring cutting-edge technologies for internal combustion engines. The introduction of these technologies has significantly increased the effort required to calibrate the models implemented in the engine control units. Consequently the development of tools that reduce costs and the time required during the experimental phases, has become imperative. Additionally, to comply with ever-stricter limits on 〖"CO" 〗_"2" emissions, it is crucial to develop advanced control systems that enhance traditional engine management systems in order to reduce fuel consumption. Furthermore, the introduction of new homologation cycles, such as the real driving emissions cycle, compels manufacturers to bridge the gap between engine operation in laboratory tests and real-world conditions. Within this context, this thesis showcases the performance and cost benefits achievable through the implementation of an auto-adaptive closed-loop control system, leveraging in-cylinder pressure sensors in a heavy-duty diesel engine designed for mining applications. Additionally, the thesis explores the promising prospect of real-time self-adaptive machine learning models, particularly neural networks, to develop an automatic system, using in-cylinder pressure sensors for the precise calibration of the target combustion phase and optimal spark advance in a spark-ignition engines. To facilitate the application of these combustion process feedback-based algorithms in production applications, the thesis discusses the results obtained from the development of a cost-effective sensor for indirect cylinder pressure measurement. Finally, to ensure the quality control of the proposed affordable sensor, the thesis provides a comprehensive account of the design and validation process for a piezoelectric washer test system.

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.

Studio di modelli a raggi per Superfici Intelligenti Riconfigurabili

In questa trattazione viene presentato lo studio di un modello a raggi che permette di simulare il comportamento macroscopico delle Superfici Intelligenti. L’ottimizzazione del canale radio, in particolare dell’ambiente di propagazione, si rivela fondamentale per la futura introduzione del 6G. Per la pianificazione della propagazione in ambienti ampi, risulta necessario implementare modelli di simulazione. I modelli a raggi possono essere integrati più facilmente all’interno di algoritmi per le previsioni di campo computazionalmente efficienti, come gli algoritmi di Ray Tracing. Nell’elaborato si è analizzato il comportamento del modello, con riferimento a tre funzioni tipiche realizzate da una Superficie Intelligente: il riflettore anomalo, la lente focalizzante e la lente defocalizzante. Attraverso una griglia di ricevitori è stato calcolato il campo dell’onda nelle varie configurazioni. Infine, si sono confrontati i risultati ottenuti con quelli del modello elettromagnetico.

Sistemi integrati per il monitoraggio dell’ambiente e la salvaguardia dei libri nei locali universitari

Negli ultimi anni l’evoluzione tecnologica ha avuto un incremento esponenziale; ogni anno, numerose innovazioni, hanno portato notevoli cambiamenti sulla vita dei consumatori e sul modo in cui essi interagiscono. Al giorno d’oggi, la tecnologia ha raggiunto livelli tali da renderne necessario l’utilizzo per poter soddisfare vari tipi di bisogni. In questa situazione, lo sviluppo di Internet ha consentito di poter entrare in una nuova era, quella dell’Internet of Things (IoT). Questo nuovo modello sarebbe in grado di portare grossi benefici nella vita di tutte le persone, partendo dalle grandi aziende, fino ad arrivare ai singoli consumatori. L’idea di questo progetto di tesi è di posizionare un dispositivo, in grado di poter rilevare temperatura ed umidità dell’ambiente, nei locali universitari nei quali sono immagazzinati i libri, in modo tale da poter monitorare l’andamento termico degli spazi ed eventualmente effettuare delle operazioni di ripristino della temperatura e dell’umidità per evitare il danneggiamento e il deterioramento dei materiali. In questo documento di tesi andremo ad approfondire l’implementazione del dispositivo IoT in grado di rilevare i dati dell’ambiente. Nello specifico analizzeremo l’ambito applicativo di questo dispositivo, l’implementazione del sistema su una scheda Raspberry Pi 4, sfruttando anche un componente aggiuntivo contenente in sensori necessari al funzionamento del sistema, e vedremo nello specifico anche l’implementazione della pagina Web creata per la visualizzazione dei dati. Negli ultimi anni abbiamo vissuto una grande crisi a livello sanitario e oggi stiamo passando un periodo di difficoltà economica dovuta all’aumento del costo di alcune materie prime quali elettricità e gas. I futuri sviluppi su questo progetto potrebbero portare a risolvere in piccolo alcuni di questi problemi.

Sviluppo di sensori indossabili per il monitoraggio di essudato di ferite

I sensori indossabili rappresentano una frontiera della ricerca scientifica e, allo stesso tempo, a livello commerciale sono un nuovo mercato emergente. La possibilità di studiare diversi parametri fisiologici con dispositivi versatili e di dimensioni ridotte è importante per raggiungere una comprensione più profonda dei diversi fenomeni che avvengono nel nostro corpo. In maniera simile, la composizione dell’essudato di una ferita è finemente legata all’evoluzione del processo di guarigione, che comporta diversi meccanismi di rigenerazione del tessuto connettivo e dell’epitelio. Grazie ai dispositivi indossabili, si apre la possibilità di monitorare i componenti chiave di questi processi. I wearable devices costituiscono quindi sia uno strumento diagnostico, che uno strumento clinico per l’identificazione e la valutazione di strategie terapeutiche più efficienti. Il mio lavoro di tirocinio si è incentrato sulla fabbricazione, caratterizzazione e sperimentazione delle performance di transistor elettrochimici a base organica (OECT) tessili per la misurazione dei livelli di pH ed acido urico. L’obbiettivo del gruppo di ricerca è quello di realizzare un cerotto intelligente che abbia due diversi elementi sensibili, uno per l’acido urico e l’altro per la concentrazione idrogenionica. Per il raggiungimento di tale scopo, si è sfruttato uno dei semiconduttori organici più utilizzati e studiati nell’ultimo periodo, il PEDOT, ovvero il poli(3,4-etilen-diossi-tiofene), che risulta anche uno dei materiali principalmente impiegati nella ricerca sui sensori tessili.