Robustness- and complexity-oriented characterization of supply networks’ structures

In the past period the efficiency aspects of production were emphasized, sometimes even overemphasized. As a result, the vulnerability of production structures was put in the background, and consequently, by now, it is usually beyond its acceptable degree. The frequently changing and uncertain environment which manufacturing companies are facing in our days requires robustness on every level of the production hierarchy from the process / machine level, through the system and enterprise levels, up to the level of supply chains and networks. As to the supply networks, the question may arise, what level of complexity is required for achieving a certain degree of robustness while, naturally, keeping the efficiency aspects in mind as well. In order to be able to give appropriate answers to this question, it is indispensable to quantify the robustness and complexity of supply chains and networks. Structural (static) and operational (dynamic) robustness and complexity are distinguished in the paper, which focuses on the structural aspects. A complex network approach is used for this purpose, namely the structural - both robustness and complexity - nature of the networks is described by applying graph theoretical concepts. Appropriate, quantitative graph measures are introduced and their applicability for characterizing the robustness and complexity of supply chains and networks is investigated by using structures of three types, namely real and artificially generated ones, and structures taken from the literature. Finally, it is illustrated how a decision support system based on the approach described in the paper can contribute to the design and redesign of supply chains and networks striving for an appropriate balance between the robustness, complexity and efficiency aspects of the problem

Multi-robot spot-welding cells: An integrated approach to cell design and motion planning

The necessity to manage several vehicle models on the same robotized assembly cell has made the cell design and the robot off-line motion planning two fundamental activities. Industrial practice and state-of-the-art methods focus on the technical issues of each activity, but no integrated approach has been yet proposed, resulting in a lack of optimality for the final cell configuration. The paper introduces a formalization of the whole process and proposes a heuristic multi-stage method for the identification of the optimal combination of cell design choices and motion planning. The proposed architecture is depicted through a real case for welding application

Supporting Interoperability of Virtual Factories

The manufacturing industry is entering a new era. This emerging era starts with the integration of new ICT technologies and collaboration applications into traditional manufacturing practices and processes, such as manufacturing 2.0. Manufacturing 2.0 has been conceptualised as a system that goes beyond the factory floor, and paradigms of “manufacturing as an ecosystem” have emerged. The virtual factory is one of the important concepts and foundations central to the realization of future manufacturing. In this paper, we take a look into the current research on virtual factories and propose a new approach to improve interoperability through the integration of different proprietary, legacy and existing solutions

Development and first applications of a statistical analysis toolbox for edge magnetic turbulence studies in the TCV tokamak

LAUREA MAGISTRALENonostante più di mezzo secolo di ricerca, il traguardo della fusione termonucleare controllata ed economicamente sfruttabile è ancora lontano. L’energia da fusione nucleare, infatti, potrebbe non essere parte della soluzione all’attuale crisi climatica. Ciononostante, ottenere accesso a una fonte energetica pulita, quasi inesauribile e non intermittente sarebbe una rivoluzione per il genere umano. Molte tipologie di reattore a fusione sono state proposte nel tempo. Fra questi, il tokamak è quello più studiato, grazie alle sue migliori prestazioni. Infatti, condizioni di operatività a livello reattore sono raggiunte con questa configurazione. ITER, progetto che rappresenta la prossima iterazione di questa tecnologia, dovrà dimostrare la fattibilità di utilizzo di un tokamak come reattore, producendo il guadagno fisico Q=10. Per soddisfare questo obiettivo ITER è dieci volte la dimensione del più grande tokamak ora in funzione, JET. Il primo plasma di ITER è pianificato per il 2025. Uno dei problemi dei tokamak riguarda il trasporto nel plasma. La teoria del trasporto neoclassico sottostima di un ordine di grandezza i coefficienti di trasporto del plasma. Ciò è dovuto al cosiddetto trasporto anomalo, generato dalla turbolenza. A causa di ciò, per ottenere nuovi parametri si utilizzano delle leggi semi-empiriche. La validità dell’estrapolazione è indebolita quando è effettuata a diversi ordini di grandezza dai dati a disposizione. Questo è il caso per la progettazione di DEMO, il successore di ITER. Una conoscenza più profonda dei fenomeni turbolenti è necessaria per migliorare gli attuali modelli di trasporto. Lo scopo di questa tesi è lo sviluppo di un codice in grado di effettuare un’analisi statistica del campo magnetico di bordo in TCV. Vista la recente installazione di oltre 200 sonde magnetiche dentro tale macchina, TCV è la migliore scelta per effettuare questo tipo di analisi. Il codice è validato con dati sperimentali reali e i principali risultati ottenuti sono: osservazione di una legge di scala differente da quelle di riferimento, presenza di dissipazione durante la cascata energetica e descrizione dei segnali acquisiti come fBm.The path towards economically viable controlled thermonuclear fusion is very long, even after decades of cutting edge research. Fusion power might not be deployed in time to contribute to the solution of the ongoing climate change crisis. Nevertheless, a clean, almost limitless and non-intermittent energy source will be a revolutionary achievement for human civilization. Multiple reactor designs have been proposed in the past seven decades. Among these, the tokamak is the most studied one because of its superior performance. Its successful development allowed the achievement of stable operation at near-reactor conditions. This means that tokamaks have almost met all requirements for burning plasmas sustain. ITER is the next iteration of this technology. Its aim is to demonstrate the viability of tokamak reactors by achieving the physics power gain Q=10. In order to do so, it is designed to be almost an order of magnitude larger than the largest currently operating tokamak in the world, JET. ITER’s first plasma operation is planned for 2025. One of the current major issues regards transport phenomena. Neoclassical transport theory underestimates experimental parameters by one order of magnitude. This enhanced behavior is due to anomalous transport, which is turbulence driven. To deal with this discrepancy, empirical scaling laws are used to obtain new parameters from the available databases. The validity of the procedure is weakened as it is performed very far away from the available data. This is a problem for DEMO’s design, the next iteration after ITER. To improve transport models, a better understanding of turbulence is needed. The aim of this work is the development of a statistical analysis toolbox for the study of edge magnetic field turbulence inside TCV. This device is the best choice to perform this study, since its fast magnetic acquisition system was recently upgraded to handle more than 200 signals. The code is validated with real experimental data. The main results of this work are: a different scaling behavior from theoretical references, the detection of dissipation during the energy cascade, and the fact that turbulence follows fBm functions

Findings from measuring door-to-door travellers’ travel satisfaction with traditional and smartphone app survey methods in eight European cities

This study investigates how different travel satisfaction survey methods influence the reported level of door-to-door travel satisfaction among travellers. The travel satisfaction measurement survey tools tested consisted of two types of smartphone applications (a satellite navigation app and a game app), an on-line survey, a paper-based semi-structured questionnaire and a focus group. Each of the measurement tools comprised of a similar set of questions, but in different formats, aimed at exploring the pros and cons of each tool among different group of travellers. In total, 5,275 valid responses were collected during the survey period from eight European cities and five FIA (Federation Internationale de I'Automobile) national motorist networks. The analysis results, with ordered logit model of travellers' reported overall satisfaction, showed that the travel satisfaction reported by different survey methods and different travel modes and user groups, correlated with distinct groups of key determinants. The relationship between and within these key determinants, however, was far from straight forward. Some were more complex than others. Some issues, such as parking availability and security, that are mostly discussed by policy makers and users may not be the ones that directly correlate with the users' overall travel satisfactions. Consistent with previous studies, the travellers' mood and previous experience influenced the reported overall journey satisfaction

A Markov chain-based approach to model the variance of times-to-failure and times-to-repair in manufacturing systems

The development of manufacturing systems with high level of automation is thrust by high-volume demand for heterogeneous engineered products. The paper focuses on the usage of Phase-type distributions in the description of reliability parameters, both times-to-failure (TTFs) and times-to-repair (TTRs), for a workstation with several failure modes. Differently from classical analytical models based on exponential distributions, the variance of reliability parameters can be exactly captured, allowing a sounder performance evaluation of the production system in which the workstation operates. While state-of-the-art research works adopt single-station models accounting for variance of TTR and/or TTF of a single failure mode, the presented model framework can capture the variance of TTRs and TTFs of all workstation failure modes, or only a portion of them. The formalized approach has been validated against a simulator replicating the workstation behavior, grounding on data acquired from the field. The application on an industrial case study showed the numerical impact of accounting for the actual variance on performance evaluation, exploiting an asynchronous continuous model of two machines-one buffer line, with finite buffer capacity and deterministic processing times. Further developments may concern the integration of the model in Markov chain-based analytical models of longer manufacturing lines

Simulation study of large production network robustness in uncertain environment

Robustness is an important success factor for production networks in which the operation of enterprises is subjected to an uncertain environment. In this paper, the robustness of networks is studied as a function of network size. The study is performed through a simulation experiment in which the uncertain environment is modelled by introducing perturbations in demand. The decision-making model mimics the behaviour of socially connected human subjects. The results show how robustness and production rate are affected by system size and social network structure, and how this is relevant for the design and operation of future manufacturing systems.This work was partially supported by the Portuguese National Funding Agency for Science, Research and Technology (FCT), Grant No. UID/CEC/00319/2013, and by the Slovenian Research Agency, Grant No. P2-0270.info:eu-repo/semantics/publishedVersio

A systematic approach of process planning and scheduling optimization for sustainable machining

The lack of effective process planning and scheduling solutions for the sustainable management of machining shop floors, whose manufacturing activities are usually characterized by high variety and low volume, has been crippling the implementation of sustainability in companies. To address the issue, an innovative and systematic approach for milling process planning and scheduling optimization has been developed and presented in this paper. This approach consists of a process stage and a system stage, augmented with intelligent mechanisms for enhancing the adaptability and responsiveness to job dynamics in machining shop floors. In the process stage, key operational parameters for milling a part are optimized adaptively to meet multiple objectives/constraints, i.e., energy efficiency of the milling process and productivity as objectives and surface quality as a constraint. In the consecutive system stage, to achieve higher energy efficiency and shorter makespan in the entire shop floor, sequencing/set-up planning of machining features/operations and scheduling for producing multiple parts on different machines are optimized. Artificial Neural Networks are used for establishing the complex nonlinear relationships between the key process parameters and measured datasets of energy consumption and surface quality. Several intelligent algorithms, including Pattern Search, Genetic Algorithm and Simulated Annealing, are applied and benchmarked to identify optimal solutions. Experimental tests indicate that the approach is effective and configurable to meet multiple objectives and technical constraints for sustainable process planning and scheduling. The approach, validated through industrial case studies provided by a European machining company, demonstrates significant potential of applicability in practice

Optimal control of remanufacturing systems with uncertainty in quality identification

Uncertainty about quantity and quality of returned end-of-use products represents a relevant aspect in the design and operation of remanufacturing systems. This paper proposes a novel optimal control policy to maximize the expected margin of a motor OEM remanufacturing electric motors. The control policy targets multiple quality classes of returning cores, and it accounts for imperfect inspection as well as for finite production capacity of the remanufacturing system. The optimal policy allows to estimate the impact of variability of quality and quantity of cores on the resulting flow of remanufactured products which is fundamental to design the remanufacturing value chain