CASTING IMPROVEMENT BASED ON METAHEURISTIC OPTIMIZATION AND NUMERICAL SIMULATION

This paper presents the use of metaheuristic optimization techniques to support the improvement of casting process. Genetic algorithm (GA), Ant Colony Optimization (ACO), Simulated annealing (SA) and Particle Swarm Optimization (PSO) have been considered as optimization tools to define the geometry of the casting part’s feeder. The proposed methodology has been demonstrated in the design of the feeder for casting Pelton turbine bucket. The results of the optimization are dimensional characteristics of the feeder, and the best result from all the implemented optimization processes has been adopted. Numerical simulation has been used to verify the validity of the presented design methodology and the feeding system optimization in the casting system of the Pelton turbine bucket

Use of bio-inspired techniques to solve complex engineering problems: industrial automation case study

Nowadays local markets have disappeared and the world lives in a global economy. Due to this reality, every company virtually competes with all others companies in the world. In addition to this, markets constantly search products with higher quality at lower costs, with high customization. Also, products tend to have a shorter period of life, making the demanding more intense. With this scenario, companies, to remain competitive, must constantly adapt themselves to the market changes, i.e., companies must exhibit a great degree of self-organization and self-adaptation. Biology with the millions of years of evolution may offer inspiration to develop new algorithms, methods and techniques to solve real complex problems. As an example, the behaviour of ants and bees, have inspired researchers in the pursuit of solutions to solve complex and evolvable engineering problems. This dissertation has the goal of explore the world of bio-inspired engineering. This is done by studying some of the bio-inspired solutions and searching for bio-inspired solutions to solve the daily problems. A more deep focus will be made to the engineering problems and particularly to the manufacturing domain. Multi-agent systems is a concept aligned with the bio-inspired principles offering a new approach to develop solutions that exhibit robustness, flexibility, responsiveness and re-configurability. In such distributed bio-inspired systems, the behaviour of each entity follows simple few rules, but the overall emergent behaviour is very complex to understand and to demonstrate. Therefore, the design and simulation of distributed agent-based solutions, and particularly those exhibiting self-organizing, are usually a hard task. Agent Based Modelling (ABM) tools simplifies this task by providing an environment for programming, modelling and simulating agent-based solutions, aiming to test and compare alternative model configurations. A deeply analysis of the existing ABM tools was also performed aiming to select the platform to be used in this work. Aiming to demonstrate the benefits of bio-inspired techniques for the industrial automation domain, a production system was used as case study for the development of a self-organizing agent-based system developed using the NetLogo tool. Hoje em dia os mercados locais desapareceram e o mundo vive numa economia global. Devido a esta realidade, cada companhia compete, virtualmente, com todas as outras companhias do mundo. A acrescentar a isto, os mercados estão constantemente à procura de produtos com maior qualidade a preços mais baixos e com um grande nível de customização Também, os produtos tendem a ter um tempo curto de vida, fazendo com que a procura seja mais intensa. Com este cenário, as companhias, para permanecer competitivas, têm que se adaptar constantemente de acordo com as mudanças de mercado, i.e., as companhias têm que exibir um alto grau de auto-organização e auto-adaptação. A biologia com os milhões de anos de evolução, pode oferecer inspiração para desenvolver novos algoritmos, métodos e técnicas para resolver problemas complexos reais. Como por exemplo, o comportamento das formigas e das abelhas inspiraram investigadores na descoberta de soluções para resolver problemas complexos e evolutivos de engenharia. Esta dissertação tem como objectivo explorar o mundo da engenharia bio-inspirada. Isto é feito através do estudo de algumas das soluções bio-inspiradas existentes e da procura de soluções bio-inspiradas para resolver os problemas do dia-a-dia. Uma atenção especial vai ser dada aos problemas de engenharia e particularmente aos problemas do domínio da manufactura. Os sistemas multi-agentes são um conceito que estão em linha com os princípios bio-inspirados oferecendo uma abordagem nova para desenvolver soluções que exibam robustez, flexibilidade, rapidez de resposta e reconfiguração. Nestes sistemas distribuídos bio-inspirados, o comportamento de cada entidade segue um pequeno conjunto de regras simples, mas o comportamento emergente global é muito complexo de perceber e de demonstrar. Por isso, o desenho e simulação de soluções distribuídas de agentes, e particularmente aqueles que exibem auto-organização, são normalmente uma tarefa árdua. As ferramentas de Modelação Baseada de Agentes (MBA) simplificam esta tarefa providenciando um ambiente para programar, modelar e simular, com o objectivo de testar e comparar diferentes configurações do modelo. Uma análise mais aprofundada das ferramentas MBA foi também efectuada tendo como objectivo seleccionar a plataforma a usar neste trabalho

Parallélisation d'un algorithme d'optimisation par colonies de fourmis pour la résolution d'un problème d'ordonnancement industriel

Les problèmes d'optimisation combinatoire peuvent être retrouvés, sous différentes formes, dans un grand nombre de sphères d'activité économique au sein de notre société. Ces problèmes complexes représentent encore un défi de taille pour bon nombre de chercheurs issus de domaines scientifiques variés tels les mathématiques, l'informatique et la recherche opérationnelle, pour ne citer que quelques exemples. La nécessité de résoudre ces problèmes de façon efficace et rapide a entraîné le prolifération de méthodes de résolution de toutes sortes, certaines étant plus spécifiques à un problème et d'autres étant plus génériques. Ce mémoire réunit différentes notions du parallélisme et des métaheuristiques afin d'apporter une méthode de résolution performante à un problème d'optimisation combinatoire réel. Il démontre que l'introduction de stratégies de parallélisation à un algorithme d'Optimisation par Colonies de Fourmis permet à ce dernier d'améliorer considérablement ses facultés de recherche de solutions. Le succès de cette approche dans la résolution d'un problème d'ordonnancement industriel rencontré dans une entreprise de fabrication d'aluminium montre l'intérêt pratique de ces méthodes et leurs retombées économiques potentielles. Ce travail de recherche, loin d'être une fin en soi, représente plutôt une première exploration des possibilités offertes par deux domaines fort prometteurs de l'informatique et de la recherche opérationnelle. L'union de méthodes d'apprentissage intelligentes et d'une puissance de calcul imposante pourrait fort bien se révéler un outil performant pour la résolution de problèmes d'une telle envergure

Inteligentno upravljanje, modeliranje i optimizacija procesa livenja

In the framework of the doctoral dissertation, it is presented a development of control, modeling and optimization of the casting process, based on application of artificial i.e. computational intelligence methods. Improving of production processes in foundries, in the framework of researches that contains doctoral dissertation consist of modeling of metal melting process, optimization of sand casting system and control of gravity casting process. The first part of research deals with modeling of melting metal in furnaces. It is a complex process due to dynamics of non-linear chemical reactions. Conventional mathematical and thermodynamical models, which are certainly helpful, are not fully reliable due to complex interactions of process variables. Neural networks, as one of the key techniques of computational intelligence, are able to identify internal relationships through training examples. As such, they are used as an effective tool in defining the needed chemical composition of the molten metal by providing alloying process improvement. Proper and complete mold filling is the main objective of the sand casting process, which ensures high quality of the casting part. To this end, the designs of the gating system and feeding system is of major importance, as improper designing of the system results in a number of defects in the casting process, e.g. low quality of the casting part. Тherefore, the gating system aims at providing a smooth, uniform and complete filling of the mold with pure, molten metal. Smooth filling eliminates turbulence, uniform filling ensures continuous mold filling, whereas complete filling allows the molten metal to reach minor, end cavities of the mold. During cooling casting part comes to the volume metal shrinkage, ie. reducing the volume of casting part. The importance of feeders is in their function to compensate the lack of molten metal in the casting part due to volume shrinkage. The second part of research is an optimal construction of gating system and feeders, which satisfy the requirements with high quality casting part and savings of materials. The combination of global optimization method and CAD/CAM software has provided the possibility of effective optimization, design and verification of optimal solutions by numerical simulation. Unlike some other industrial processes, many casting plants have not benefited from the advanced automation therefore the process of filling the mold cavity is still manually performed. Automated systems that minimize the participation of workers and enhance the precision of casting, are also the subject of research in this doctoral dissertation. The part of research, which is dedicated to the development of casting process control, contains a proposal of an automated system in casting plants, development of laboratory simulator of the casting process and the development of possibility of application of intelligent control of mold filling

Population-based algorithms for improved history matching and uncertainty quantification of Petroleum reservoirs

In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where the simulation model is calibrated to reproduce the historical observations from the field. In this inverse problem, different geological and petrophysical properties may provide equally good history matches. Such diverse models are likely to show different production behaviors in future. This ties the history matching with the second step, uncertainty quantification of predictions. Multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. These two steps facilitate decision making and have a direct impact on technical and financial performance of oil and gas companies. Population-based optimization algorithms have been recently enjoyed growing popularity for solving engineering problems. Population-based systems work with a group of individuals that cooperate and communicate to accomplish a task that is normally beyond the capabilities of each individual. These individuals are deployed with the aim to solve the problem with maximum efficiency. This thesis introduces the application of two novel population-based algorithms for history matching and uncertainty quantification of petroleum reservoir models. Ant colony optimization and differential evolution algorithms are used to search the space of parameters to find multiple history matched models and, using a Bayesian framework, the posterior probability of the models are evaluated for prediction of reservoir performance. It is demonstrated that by bringing latest developments in computer science such as ant colony, differential evolution and multiobjective optimization, we can improve the history matching and uncertainty quantification frameworks. This thesis provides insights into performance of these algorithms in history matching and prediction and develops an understanding of their tuning parameters. The research also brings a comparative study of these methods with a benchmark technique called Neighbourhood Algorithms. This comparison reveals the superiority of the proposed methodologies in various areas such as computational efficiency and match quality

Proposition d'un outil d'aide à la décision multicritère sous incertitudes à base de colonies de fourmis : une approche intégrée appliquée à la gestion des risques dans les projets d'ingénierie système.

Dans cette thèse nous proposons un outil d’aide à la décision multicritère qui permet aux décideurs de sélectionner un scénario optimal dans un graphe de projet qui contient toutes les alternatives de choix de conception et de réalisation d’un nouveau système, tout en tenant compte des risques inhérents aux choix réalisés. Le modèle du graphe est construit en considérant toutes les décisions collaboratives des différents acteurs impliqués dans le projet. Cet outil d’aide à la décision est basé principalement sur les techniques de l’optimisation combinatoire. En effet, nous avons choisi de travailler avec la métaheuristique ACO (algorithme d’optimisation par colonies de fourmis) vu sa capacité à fournir des solutions optimales dans un temps raisonnable. Les objectifs à minimiser sont le coût global du projet, sa durée totale de réalisation et l’incertitude sur ces critères (coût, durée). La modélisation des incertitudes a été abordée suivant deux approches différentes. La première approche consiste à modéliser l’incertitude en utilisant des intervalles simples et en la considérant comme un objectif à part entière à optimiser avec le coût et la durée. Quant à la deuxième approche, elle permet de modéliser l’incertitude sur les objectifs du projet (coût, durée) sous formes de distributions de probabilités. L’outil d’optimisation proposé dans la thèse fait partie d’un processus intégré et plus global qui se base sur les standards industriels (processus d’ingénierie système et de management de projet) qui sont largement connus et utilisés dans les entreprises. Ainsi, le travail développé dans cette thèse constitue un vrai guide pour les industriels dans leurs processus de conception et de réalisation des systèmes complexes innovants dans le domaine d’ingénierie système

Diseño de un sistema de recogida de residuos urbanos : enfoque multiobjetivo y uso de metaheurísticos

En este trabajo se desarrolla un método para resolver el problema de diseño de rutas, a lo largo de un horizonte de planificación predeterminado, para la recogida de la fracción orgánica residuos urbanos en un entorno rural. El objetivo en este problema es doble: minimizar el coste de las operaciones de rutas y mejorar el nivel de calidad, por lo que se adapta a un problema biobjetivo. Para resolver el problema se diseña un método ad hoc basado en estrategias heurísticas. Éste, sigue las ideas de la estrategia MOAMP, diseñada para problemas multi-objetivo. A continuación, y desde el punto de vista metodológico, se desarrollan estrategias de aceleración para algunos de los procedimientos del método propuesto. Por último, se compara, tanto en instancias reales como ficticias, con una adaptación a este problema, de una variante de un algoritmo genético, conocida como NSGA I