Dry grinding technology for automotive gears manufacturing: process modeling and optimization

The following thesis focused on the dry grinding process modelling and optimization for automotive gears production. A FEM model was implemented with the aim at predicting process temperatures and preventing grinding thermal defects on the material surface. In particular, the model was conceived to facilitate the choice of the grinding parameters during the design and the execution of the dry-hard finishing process developed and patented by the company Samputensili Machine Tools (EMAG Group) on automotive gears. The proposed model allows to analyse the influence of the technological parameters, comprising the grinding wheel specifications. Automotive gears finished by dry-hard finishing process are supposed to reach the same quality target of the gears finished through the conventional wet grinding process with the advantage of reducing production costs and environmental pollution. But, the grinding process allows very high values of specific pressure and heat absorbed by the material, therefore, removing the lubricant increases the risk of thermal defects occurrence. An incorrect design of the process parameters set could cause grinding burns, which affect the mechanical performance of the ground component inevitably. Therefore, a modelling phase of the process could allow to enhance the mechanical characteristics of the components and avoid waste during production. A hierarchical FEM model was implemented to predict dry grinding temperatures and was represented by the interconnection of a microscopic and a macroscopic approach. A microscopic single grain grinding model was linked to a macroscopic thermal model to predict the dry grinding process temperatures and so to forecast the thermal cycle effect caused by the process parameters and the grinding wheel specification choice. Good agreement between the model and the experiments was achieved making the dry-hard finishing an efficient and reliable technology to implement in the gears automotive industry.

Modeling and optimization techniques for advanced vehicular networking

This thesis deals with optimization techniques and modeling of vehicular networks. Thanks to the models realized with the integer linear programming (ILP) and the heuristic ones, it was possible to study the performances in 5G networks for the vehicular. Thanks to Software-defined networking (SDN) and Network functions virtualization (NFV) paradigms it was possible to study the performances of different classes of service, such as the Ultra Reliable Low Latency Communications (URLLC) class and enhanced Mobile BroadBand (eMBB) class, and how the functional split can have positive effects on network resource management. Two different protection techniques have been studied: Shared Path Protection (SPP) and Dedicated Path Protection (DPP). Thanks to these different protections, it is possible to achieve different network reliability requirements, according to the needs of the end user. Finally, thanks to a simulator developed in Python, it was possible to study the dynamic allocation of resources in a 5G metro network. Through different provisioning algorithms and different dynamic resource management techniques, useful results have been obtained for understanding the needs in the vehicular networks that will exploit 5G. Finally, two models are shown for reconfiguring backup resources when using shared resource protection.

Development and optimization of fibre-shaped dye-sensitized solar cells employing an innovative fully organic sensitizer

The quality of human life depends to a large degree on the availability of energy. In recent years, photovoltaic technology has been growing extraordinarily as a suitable source of energy, as a consequence of the increasing concern over the impact of fossil fuels on climate change. Developing affordable and highly efficiently photovoltaic technologies is the ultimate goal in this direction. Dye-sensitized solar cells (DSSCs) offer an efficient and easily implementing technology for future energy supply. Compared to conventional silicon solar cells, they provide comparable power conversion efficiency at low material and manufacturing costs. In addition, DSSCs are able to harvest low-intensity light in diffuse illumination conditions and then represent one of the most promising alternatives to the traditional photovoltaic technology, even more when trying to move towards flexible and transparent portable devices. Among these, considering the increasing demand of modern electronics for small, portable and wearable integrated optoelectronic devices, Fibre Dye-Sensitized Solar Cells (FDSSCs) have gained increasing interest as suitable energy provision systems for the development of the next-generation of smart products, namely “electronic textiles” or “e-textiles”. In this thesis, several key parameters towards the optimization of FDSSCs based on inexpensive and abundant TiO2 as photoanode and a new innovative fully organic sensitizer were studied. In particular, the effect of various FDSSCs components on the device properties pertaining to the cell architecture in terms of photoanode oxide layer thickness, electrolytic system, cell length and electrodes substrates were examined. The photovoltaic performances of the as obtained FDSSCs were fully characterized. Finally, the metal part of the devices (wire substrate) was substituted with substrates suitable for the textile industry as a fundamental step towards commercial exploitation.

Formulations and Metaheuristics for combinatorial optimization problems

Combinatorial optimization problems have been strongly addressed throughout history. Their study involves highly applied problems that must be solved in reasonable times. This doctoral Thesis addresses three Operations Research problems: the first deals with the Traveling Salesman Problem with Pickups and Delivery with Handling cost, which was approached with two metaheuristics based on Iterated Local Search; the results show that the proposed methods are faster and obtain good results respect to the metaheuristics from the literature. The second problem corresponds to the Quadratic Multiple Knapsack Problem, and polynomial formulations and relaxations are presented for new instances of the problem; in addition, a metaheuristic and a matheuristic are proposed that are competitive with state of the art algorithms. Finally, an Open-Pit Mining problem is approached. This problem is solved with a parallel genetic algorithm that allows excavations using truncated cones. Each of these problems was computationally tested with difficult instances from the literature, obtaining good quality results in reasonable computational times, and making significant contributions to the state of the art techniques of Operations Research.

Optimization and development of low environmental impact propulsion systems

The aim of the Ph.D. research project was to explore Dual Fuel combustion and hybridization. Natural gas-diesel Dual Fuel combustion was experimentally investigated on a 4-Stroke, 2.8 L, turbocharged, light-duty Diesel engine, considering four operating points in the range between low to medium-high loads at 3000 rpm. Then, a numerical analysis was carried out using a customized version of the KIVA-3V code, in order to optimize the diesel injection strategy of the highest investigated load. A second KIVA-3V model was used to analyse the interchangeability between natural gas and biogas on an intermediate operating point. Since natural gas-diesel Dual Fuel combustion suffers from poor combustion efficiency at low loads, the effects of hydrogen enriched natural gas on Dual Fuel combustion were investigated using a validated Ansys Forte model, followed by an optimization of the diesel injection strategy and a sensitivity analysis to the swirl ratio, on the lowest investigated load. Since one of the main issues of Low Temperature Combustion engines is the low power density, 2-Stroke engines, thanks to the double frequency compared to 4-Stroke engines, may be more suitable to operate in Dual Fuel mode. Therefore, the application of gasoline-diesel Dual Fuel combustion to a modern 2-Stroke Diesel engine was analysed, starting from the investigation of gasoline injection and mixture formation. As far as hybridization is concerned, a MATLAB-Simulink model was built to compare a conventional (combustion) and a parallel-hybrid powertrain applied to a Formula SAE race car.

Development of thermochemiluminescence-based sensitive probes: synthesis, optimization, and characterization of c2- and c7-substituted acridine-containing 1,2-dioxetanes

After initial efforts in the late 1980s, the interest in thermochemiluminescence (TCL) as an effective detection technique has gradually faded due to some drawbacks, such as the high temperatures required to trigger the light emission and the relatively low intensities, which determined a poor sensitivity. Recent advances made with the adoption of variably functionalized 1,2-dioxetanes as innovative luminophores, have proved to be a promising approach for the development of reagentless and ultrasensitive detection methods exploitable in biosensors by using TCL compounds as labels, as either single molecules or included in modified nanoparticles. In this PhD Thesis, a novel class of N-substituted acridine-containing 1,2-dioxetanes was designed, synthesized, and characterized as universal TCL probes endowed with optimal emission-triggering temperatures and higher detectability particularly useful in bioanalytical assays. The different decorations introduced by the insertion of both electron donating (EDGs) and electron withdrawing groups (EWGs) at the 2- and 7-positions of acridine fluorophore was found to profoundly affect the photophysical properties and the activation parameters of the final 1,2-dioxetane products. Challenges in the synthesis of 1,2-dioxetanes were tackled with the recourse to continuous flow photochemistry to achieve the target parent compound in high yields, short reaction time, and easy scalability. Computational studies were also carried out to predict the olefins reactivity in the crucial photooxygenation reaction as well as the final products stability. The preliminary application of TCL prototype molecule has been performed in HaCaT cell lines showing the ability of these molecules to be detected in real biological samples and cell-based assays. Finally, attempts on the characterization of 1,2-dioxetanes in different environments (solid state, optical glue and nanosystems) and the development of bioconjugated TCL probes will be also presented and discussed.

Development of a Computer-Based methodology for tolerance selection and optimization applied to the automotive sector

The design optimization of industrial products has always been an essential activity to improve product quality while reducing time-to-market and production costs. Although cost management is very complex and comprises all phases of the product life cycle, the control of geometrical and dimensional variations, known as Dimensional Management (DM), allows compliance with product and process requirements. Hence, the tolerance-cost optimization becomes the main practice to provide an effective application of Design for Tolerancing (DfT) and Design to Cost (DtC) approaches by enabling a connection between product tolerances and associated manufacturing costs. However, despite the growing interest in this topic, a profitable application in the industry of these techniques is hampered by their complexity: the definition of a systematic framework is the key element to improving design optimization, enhancing the concurrent use of Computer-Aided tools and Model-Based Definition (MBD) practices. The present doctorate research aims to define and develop an integrated methodology for product/process design optimization, to better exploit the new capabilities of advanced simulations and tools. By implementing predictive models and multi-disciplinary optimization, a Computer-Aided Integrated framework for tolerance-cost optimization has been proposed to allow the integration of DfT and DtC approaches and their direct application for the design of automotive components. Several case studies have been considered, with the final application of the integrated framework on a high-performance V12 engine assembly, to achieve both functional targets and cost reduction. From a scientific point of view, the proposed methodology provides an improvement for the tolerance-cost optimization of industrial components. The integration of theoretical approaches and Computer-Aided tools allows to analyse the influence of tolerances on both product performance and manufacturing costs. The case studies proved the suitability of the methodology for its application in the industrial field, providing the identification of further areas for improvement and refinement.

Distributed optimization and games over networks: a system theoretical perspective

Several decision and control tasks involve networks of cyber-physical systems that need to be coordinated and controlled according to a fully-distributed paradigm involving only local communications without any central unit. This thesis focuses on distributed optimization and games over networks from a system theoretical perspective. In the addressed frameworks, we consider agents communicating only with neighbors and running distributed algorithms with optimization-oriented goals. The distinctive feature of this thesis is to interpret these algorithms as dynamical systems and, thus, to resort to powerful system theoretical tools for both their analysis and design. We first address the so-called consensus optimization setup. In this context, we provide an original system theoretical analysis of the well-known Gradient Tracking algorithm in the general case of nonconvex objective functions. Then, inspired by this method, we provide and study a series of extensions to improve the performance and to deal with more challenging settings like, e.g., the derivative-free framework or the online one. Subsequently, we tackle the recently emerged framework named distributed aggregative optimization. For this setup, we develop and analyze novel schemes to handle (i) online instances of the problem, (ii) ``personalized'' optimization frameworks, and (iii) feedback optimization settings. Finally, we adopt a system theoretical approach to address aggregative games over networks both in the presence or absence of linear coupling constraints among the decision variables of the players. In this context, we design and inspect novel fully-distributed algorithms, based on tracking mechanisms, that outperform state-of-the-art methods in finding the Nash equilibrium of the game.

Optimization of an innovative T6 heat treatment for the AlSi10Mg alloy processed by Laser-based Powder Bed Fusion: effect on microstructure, mechanical properties, and tribological behavior

Laser-based Powder Bed Fusion (L-PBF) technology is one of the most commonly used metal Additive Manufacturing (AM) techniques to produce highly customized and value-added parts. The AlSi10Mg alloy has received more attention in the L-PBF process due to its good printability, high strength/weight ratio, corrosion resistance, and relatively low cost. However, a deep understanding of the effect of heat treatments on this alloy's metastable microstructure is still required for developing tailored heat treatments for the L-PBF AlSi10Mg alloy to overcome the limits of the as-built condition. Several authors have already investigated the effects of conventional heat treatment on the microstructure and mechanical behavior of the L-PBF AlSi10Mg alloy but often overlooked the peculiarities of the starting supersatured and ultrafine microstructure induced by rapid solidification. For this reason, the effects of innovative T6 heat treatment (T6R) on the microstructure and mechanical behavior of the L-PBF AlSi10Mg alloy were assessed. The short solution soaking time (10 min) and the relatively low temperature (510 °C) reduced the typical porosity growth at high temperatures and led to a homogeneous distribution of fine globular Si particles in the Al matrix. In addition, it increased the amount of Mg and Si in the solid solution available for precipitation hardening during the aging step. The mechanical (at room temperature and 200 °C) and tribological properties of the T6R alloy were evaluated and compared with other solutions, especially with an optimized direct-aged alloy (T5 alloy). Results showed that the innovative T6R alloy exhibits the best mechanical trade-off between strength and ductility, the highest fatigue strength among the analyzed conditions, and interesting tribological behavior. Furthermore, the high-temperature mechanical performances of the heat-treated L-PBF AlSi10Mg alloy make it suitable for structural components operating in mild service conditions at 200 °C.

Drive up electrochemical and electrochemiluminescence signal through biosensing mechanism optimization

Biomarkers are biological indicators of human health conditions. Their ultra-sensitive quantification is of cardinal importance in clinical monitoring and early disease diagnosis. Biosensors are some worldwide simple and easy-to-use analytical devices as a matter of fact, biosensors using electrochemiluminescence (ECL) are one of the most promising biosensors that needs an ever-increasing sensitivity for improving its clinical effectiveness. The principal aspiration of this project is the investigation of the ECL generation mechanisms for enhancing the ECL intensity and the development of an ultrasensitive sensor, the use of metal-oxide materials (Mox) and the substitution of metal-free dyes. Novel dyes such as BODIPY, TADF are used to improve the sensitivity of ECL techniques thanks to their advantageous and tunable properties, enhancing the signal and also the ECL efficiency. Additionally, the use of Mox could be beneficial for the investigation of two different ECL mechanisms, which occur simultaneously. In this thesis, the investigation of size and distance effects on electrochemical (EC) mechanisms was carried out through the innovative combination of a standard detection system using different size of micromagnetic beads (MBs). That allowed the discovery of an unexpected and highly efficient mechanistic path for electrochemical generation at small distances from the electrode’s surface. The smallest MBs (0.1μm) demostrate an enhancement of electrochemical signal than the bigger one (2.8μm) until 4 times of magnitude. Finally, a novel ultrasensitive sensor, based on the coreactant-luminophores mechanism, was developed for the determination of whole viral genome specific for cardiac HBV and COVID-19 virus. In conclusion, the ECL and the use of EC techniques (such as amperometry), improved the understanding of mechanisms responsible for the ECL/EC signal led to a great enhancement in the signal.

Development of numerical tools for the simulation, design and optimization of a Helicon Plasma Thruster

This work thesis focuses on the Helicon Plasma Thruster (HPT) as a candidate for generating thrust for small satellites and CubeSats. Two main topics are addressed: the development of a Global Model (GM) and a 3D self-consistent numerical tool. The GM is suitable for preliminary analysis of HPTs with noble gases such as argon, neon, krypton, and xenon, and alternative propellants such as air and iodine. A lumping methodology is developed to reduce the computational cost when modelling the excited species in the plasma chemistry. A 3D self-consistent numerical tool is also developed that can treat discharges with a generic 3D geometry and model the actual plasma-antenna coupling. The tool consists of two main modules, an EM module and a FLUID module, which run iteratively until a steady state solution is converged. A third module is available for solving the plume with a simplified semi-analytical approach, a PIC code, or directly by integration of the fluid equations. Results obtained from both the numerical tools are benchmarked against experimental measures of HPTs or Helicon reactors, obtaining very good qualitative agreement with the experimental trend for what concerns the GM, and an excellent agreement of the physical trends predicted against the measured data for the 3D numerical strategy.

Analytics and optimization for emergency healthcare processes

This thesis deals with the analysis and management of emergency healthcare processes through the use of advanced analytics and optimization approaches. Emergency processes are among the most complex within healthcare. This is due to their non-elective nature and their high variability. This thesis is divided into two topics. The first one concerns the core of emergency healthcare processes, the emergency department (ED). In the second chapter, we describe the ED that is the case study. This is a real case study with data derived from a large ED located in northern Italy. In the next two chapters, we introduce two tools for supporting ED activities. The first one is a new type of analytics model. Its aim is to overcome the traditional methods of analyzing the activities provided in the ED by means of an algorithm that analyses the ED pathway (organized as event log) as a whole. The second tool is a decision-support system, which integrates a deep neural network for the prediction of patient pathways, and an online simulator to evaluate the evolution of the ED over time. Its purpose is to provide a set of solutions to prevent and solve the problem of the ED overcrowding. The second part of the thesis focuses on the COVID-19 pandemic emergency. In the fifth chapter, we describe a tool that was used by the Bologna local health authority in the first part of the pandemic. Its purpose is to analyze the clinical pathway of a patient and from this automatically assign them a state. Physicians used the state for routing the patients to the correct clinical pathways. The last chapter is dedicated to the description of a MIP model, which was used for the organization of the COVID-19 vaccination campaign in the city of Bologna, Italy.

Network assimilation methodology and sectorization of water distribution systems by graph mining and optimization algorithm

Water Distribution Networks (WDNs) play a vital importance rule in communities, ensuring well-being band supporting economic growth and productivity. The need for greater investment requires design choices will impact on the efficiency of management in the coming decades. This thesis proposes an algorithmic approach to address two related problems:(i) identify the fundamental asset of large WDNs in terms of main infrastructure;(ii) sectorize large WDNs into isolated sectors in order to respect the minimum service to be guaranteed to users. Two methodologies have been developed to meet these objectives and subsequently they were integrated to guarantee an overall process which allows to optimize the sectorized configuration of WDN taking into account the needs to integrated in a global vision the two problems (i) and (ii). With regards to the problem (i), the methodology developed introduces the concept of primary network to give an answer with a dual approach, of connecting main nodes of WDN in terms of hydraulic infrastructures (reservoirs, tanks, pumps stations) and identifying hypothetical paths with the minimal energy losses. This primary network thus identified can be used as an initial basis to design the sectors. The sectorization problem (ii) has been faced using optimization techniques by the development of a new dedicated Tabu Search algorithm able to deal with real case studies of WDNs. For this reason, three new large WDNs models have been developed in order to test the capabilities of the algorithm on different and complex real cases. The developed methodology also allows to automatically identify the deficient parts of the primary network and dynamically includes new edges in order to support a sectorized configuration of the WDN. The application of the overall algorithm to the new real case studies and to others from literature has given applicable solutions even in specific complex situations.

Sensor networks optimization and software development for Structural Health Monitoring based on ultrasonic guided waves

The Structural Health Monitoring (SHM) research area is increasingly investigated due to its high potential in reducing the maintenance costs and in ensuring the systems safety in several industrial application fields. A growing demand of new SHM systems, permanently embedded into the structures, for savings in weight and cabling, comes from the aeronautical and aerospace application fields. As consequence, the embedded electronic devices are to be wirelessly connected and battery powered. As result, a low power consumption is requested. At the same time, high performance in defects or impacts detection and localization are to be ensured to assess the structural integrity. To achieve these goals, the design paradigms can be changed together with the associate signal processing. The present thesis proposes design strategies and unconventional solutions, suitable both for real-time monitoring and periodic inspections, relying on piezo-transducers and Ultrasonic Guided Waves. In the first context, arrays of closely located sensors were designed, according to appropriate optimality criteria, by exploiting sensors re-shaping and optimal positioning, to achieve improved damages/impacts localisation performance in noisy environments. An additional sensor re-shaping procedure was developed to tackle another well-known issue which arises in realistic scenario, namely the reverberation. A novel sensor, able to filter undesired mechanical boundaries reflections, was validated via simulations based on the Green's functions formalism and FEM. In the active SHM context, a novel design methodology was used to develop a single transducer, called Spectrum-Scanning Acoustic Transducer, to actively inspect a structure. It can estimate the number of defects and their distances with an accuracy of 2[cm]. It can also estimate the damage angular coordinate with an equivalent mainlobe aperture of 8[deg], when a 24[cm] radial gap between two defects is ensured. A suitable signal processing was developed in order to limit the computational cost, allowing its use with embedded electronic devices.

Models and algorithms for real-world optimization problems

This thesis deals with efficient solution of optimization problems of practical interest. The first part of the thesis deals with bin packing problems. The bin packing problem (BPP) is one of the oldest and most fundamental combinatorial optimiza- tion problems. The bin packing problem and its generalizations arise often in real-world ap- plications, from manufacturing industry, logistics and transportation of goods, and scheduling. After an introductory chapter, I will present two applications of two of the most natural extensions of the bin packing: Chapter 2 will be dedicated to an application of bin packing in two dimension to a problem of scheduling a set of computational tasks on a computer cluster, while Chapter 3 deals with the generalization of BPP in three dimensions that arise frequently in logistic and transportation, often com- plemented with additional constraints on the placement of items and characteristics of the solution, like, for example, guarantees on the stability of the items, to avoid potential damage to the transported goods, on the distribution of the total weight of the bins, and on compatibility with loading and unloading operations. The second part of the thesis, and in particular Chapter 4 considers the Trans- mission Expansion Problem (TEP), where an electrical transmission grid must be expanded so as to satisfy future energy demand at the minimum cost, while main- taining some guarantees of robustness to potential line failures. These problems are gaining importance in a world where a shift towards renewable energy can impose a significant geographical reallocation of generation capacities, resulting in the ne- cessity of expanding current power transmission grids.