Revisão dos tempos-padrão no setor de construção de pneus

Este relatório de estágio foi desenvolvido no âmbito do estágio de final de curso, em ambiente industrial, do Mestrado em Engenharia e Gestão Industrial da ESEIG. O estágio foi realizado na Continental Mabor S.A., situada em Vila Nova de Famalicão, mais especificamente no Departamento de Engenharia Industrial. A indústria dos pneus está inserida num mercado cada vez mais competitivo e exigente, em que os custos e os prazos de entrega devem ser cada vez menores e a qualidade do produto cada vez maior. Por esses motivos, é necessária uma constante melhoria dos processos produtivos. Para que este esforço de melhoria contínua seja bem-sucedido é necessário recorrer por vezes a prémios de produção, para incentivar uma maior produtividade. Contudo cada pneu tem tempos de produção diferentes de máquina para máquina e é preciso corrigir esses tempos em prol de uma justa atribuição de prémios. O presente trabalho teve como principal objetivo atualizar as perturbações e atualizar os tempos-máquina no setor de construção de pneus. Inicialmente foi recolhido o tempo de aplicação do capply (material que pertence ao pneu) para futuramente verificar se será possível prever o tempo de aplicação de capply, consoante os metros de aplicação. Como não foi possível estabelecer uma correlação, não será exposta a análise até se chegar a essa conclusão. Depois fez-se o estudo das perturbações, com recurso à cronometragem, na maior das cinco divisões da fábrica - a construção. Por último fez-se a recolha dos tempos-máquina de vários tipos de construção de pneus em todos os módulos de construção de pneus (KM e PU), através do método filmagem. Conseguiu-se singularizar os tempos para cada máquina de forma a tornar, mais justo os prémios de produção atribuídos aos operadores, no curto prazo, e optimizar a produção e o tempo-padrão.

Kitchen robot prod. line development. Proj. of Bowden cables cutting and thermal forming machine.

This document presents particular description of work done during student’s internship in PR Metal company realized as ERASMUS PROJECT at ISEP. All information including company’s description and its structure, overview of the problems and analyzed cases, all stages of projects from concept to conclusion can be found here. Description of work done during the internship is divided here into two pieces. First part concerns one activities of the company which is robotic chefs (kitchen robot) production line. Work, that was done for development of this line involved several tasks, among them: creating a single-worker montage station for screwing robots housing’s parts, improve security system for laser welding chamber, what particularly consists in designing automatically closing door system with special surface, that protects against destructive action of laser beam, test station for examination of durability of heating connectors, solving problem with rotors vibrations. Second part tells about main task, realized in second half of internship and stands a complete description of machine development and design. The machine is a part of car handle latch cable production line and its tasks are: cutting cable to required length and hot-forming plastic cover for further assembly needs.

Speed control of induction machine based on direct torque control method

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica e de Computadores

Machine learning Gaussian short rate

Dissertação para obtenção do Grau de Doutor em Estatística e Gestão do Risco

Board independence and innovation

Legislation introduced in the U.S. in 2002/2003 significantly changed board composition of public firms by imposing a 50% independent directors’ ratio. Research on the effect of independent directors is not consensual, implying that this exogenous shock is a unique opportunity to study their importance. This study answers the question of whether or not independent directors can effectively mitigate agency conflicts between shareholders and the management, having a positive impact on the choice of successful R&D projects. We find that an increase of board independence has a positive impact on patent counts. Hence, the results support that independent directors truly spur innovation and risk taking.

Almirante: Evaluating the board game: Board game, startup, discounted cash flow, venture capital method, real options

This thesis evaluates a start-up company (Jogos Almirante Lda) whose single asset is a board game named Almirante. It aims to conclude whether it makes sense to create a company or just earn copyrights. The thesis analyzes the board game’s market, as part of the general toy’s market, from which some data exists: European countries as well as the USA. In this work it is analyzed the several ways to finance a start-up company and then present an overview of the valuation of the Jogos Almirante based on three different methods: Discounted Cash Flow, Venture Capital Method and Real Options.

Machine ethics via logic programming

Machine ethics is an interdisciplinary field of inquiry that emerges from the need of imbuing autonomous agents with the capacity of moral decision-making. While some approaches provide implementations in Logic Programming (LP) systems, they have not exploited LP-based reasoning features that appear essential for moral reasoning. This PhD thesis aims at investigating further the appropriateness of LP, notably a combination of LP-based reasoning features, including techniques available in LP systems, to machine ethics. Moral facets, as studied in moral philosophy and psychology, that are amenable to computational modeling are identified, and mapped to appropriate LP concepts for representing and reasoning about them. The main contributions of the thesis are twofold. First, novel approaches are proposed for employing tabling in contextual abduction and updating – individually and combined – plus a LP approach of counterfactual reasoning; the latter being implemented on top of the aforementioned combined abduction and updating technique with tabling. They are all important to model various issues of the aforementioned moral facets. Second, a variety of LP-based reasoning features are applied to model the identified moral facets, through moral examples taken off-the-shelf from the morality literature. These applications include: (1) Modeling moral permissibility according to the Doctrines of Double Effect (DDE) and Triple Effect (DTE), demonstrating deontological and utilitarian judgments via integrity constraints (in abduction) and preferences over abductive scenarios; (2) Modeling moral reasoning under uncertainty of actions, via abduction and probabilistic LP; (3) Modeling moral updating (that allows other – possibly overriding – moral rules to be adopted by an agent, on top of those it currently follows) via the integration of tabling in contextual abduction and updating; and (4) Modeling moral permissibility and its justification via counterfactuals, where counterfactuals are used for formulating DDE.

Machine diagnosis based on artificial immune systems

Nowadays, many of the manufactory and industrial system has a diagnosis system on top of it, responsible for ensuring the lifetime of the system itself. It achieves this by performing both diagnosis and error recovery procedures in real production time, on each of the individual parts of the system. There are many paradigms currently being used for diagnosis. However, they still fail to answer all the requirements imposed by the enterprises making it necessary for a different approach to take place. This happens mostly on the error recovery paradigms since the great diversity that is nowadays present in the industrial environment makes it highly unlikely for every single error to be fixed under a real time, no production stop, perspective. This work proposes a still relatively unknown paradigm to manufactory. The Artificial Immune Systems (AIS), which relies on bio-inspired algorithms, comes as a valid alternative to the ones currently being used. The proposed work is a multi-agent architecture that establishes the Artificial Immune Systems, based on bio-inspired algorithms. The main goal of this architecture is to solve for a resolution to the error currently detected by the system. The proposed architecture was tested using two different simulation environment, each meant to prove different points of views, using different tests. These tests will determine if, as the research suggests, this paradigm is a promising alternative for the industrial environment. It will also define what should be done to improve the current architecture and if it should be applied in a decentralised system.

The impact of the Board of Director's composition on companies performance

The impact of the Board of Directors’ composition on companies’ performance This paper studies the impact that the board of directors’ composition has on companies’ performance in the Italian market. The research has been carried out by using a sample of 10 Italian companies, across different market sectors, over a period of 10 years (2005-2014). The characteristics of the BoD taken into consideration are the following: board size, board diversity (% of female directors), board independence and CEO duality. Results from the sample data collected concluded that these factors have a statistically significant impact on the performance of the companies that have been analysed.

Ontology-based representation and generation of workflows for micro-task human-machine computation

Doctoral Program in Computer Science

On-board electric vehicle battery charger with enhanced V2H operation mode

This paper proposes an on-board Electric Vehicle (EV) battery charger with enhanced Vehicle-to-Home (V2H) operation mode. For such purpose was adapted an on-board bidirectional battery charger prototype to allow the Grid-to-Vehicle (G2V), Vehicle-to-Grid (V2G) and V2H operation modes. Along the paper are presented the hardware topology and the control algorithms of this battery charger. The idea underlying to this paper is the operation of the on-board bidirectional battery charger as an energy backup system when occurs a power outages. For detecting the power outage were compared two strategies, one based on the half-cycle rms calculation of the power grid voltage, and another in the determination of the rms value based in a Kalman filter. The experimental results were obtained considering the on-board EV battery charger under the G2V, V2G, and V2H operation modes. The results show that the power outage detection is faster using a Kalman filter, up to 90% than the other strategy. This also enables a faster transition between operation modes when a power outage occurs.

The human eye as human-machine interface

Eye tracking as an interface to operate a computer is under research for a while and new systems are still being developed nowadays that provide some encouragement to those bound to illnesses that incapacitates them to use any other form of interaction with a computer. Although using computer vision processing and a camera, these systems are usually based on head mount technology being considered a contact type system. This paper describes the implementation of a human-computer interface based on a fully non-contact eye tracking vision system in order to allow people with tetraplegia to interface with a computer. As an assistive technology, a graphical user interface with special features was developed including a virtual keyboard to allow user communication, fast access to pre-stored phrases and multimedia and even internet browsing. This system was developed with the focus on low cost, user friendly functionality and user independency and autonomy.

Hand gesture recognition system based in computer vision and machine learning

"Lecture notes in computational vision and biomechanics series, ISSN 2212-9391, vol. 19"

Discrimination of Brazilian propolis according to the seasoning using chemometrics and machine learning based on UV-Vis scanning data

Propolis is a chemically complex biomass produced by honeybees (Apis mellifera) from plant resins added of salivary enzymes, beeswax, and pollen. The biological activities described for propolis were also identified for donor plants resin, but a big challenge for the standardization of the chemical composition and biological effects of propolis remains on a better understanding of the influence of seasonality on the chemical constituents of that raw material. Since propolis quality depends, among other variables, on the local flora which is strongly influenced by (a)biotic factors over the seasons, to unravel the harvest season effect on the propolis chemical profile is an issue of recognized importance. For that, fast, cheap, and robust analytical techniques seem to be the best choice for large scale quality control processes in the most demanding markets, e.g., human health applications. For that, UV-Visible (UV-Vis) scanning spectrophotometry of hydroalcoholic extracts (HE) of seventy-three propolis samples, collected over the seasons in 2014 (summer, spring, autumn, and winter) and 2015 (summer and autumn) in Southern Brazil was adopted. Further machine learning and chemometrics techniques were applied to the UV-Vis dataset aiming to gain insights as to the seasonality effect on the claimed chemical heterogeneity of propolis samples determined by changes in the flora of the geographic region under study. Descriptive and classification models were built following a chemometric approach, i.e. principal component analysis (PCA) and hierarchical clustering analysis (HCA) supported by scripts written in the R language. The UV-Vis profiles associated with chemometric analysis allowed identifying a typical pattern in propolis samples collected in the summer. Importantly, the discrimination based on PCA could be improved by using the dataset of the fingerprint region of phenolic compounds ( = 280-400m), suggesting that besides the biological activities of those secondary metabolites, they also play a relevant role for the discrimination and classification of that complex matrix through bioinformatics tools. Finally, a series of machine learning approaches, e.g., partial least square-discriminant analysis (PLS-DA), k-Nearest Neighbors (kNN), and Decision Trees showed to be complementary to PCA and HCA, allowing to obtain relevant information as to the sample discrimination.