FA; UTFPR; RENAULT / Linhas robóticas de solda são comuns na indústria automobilística. Durante a produção de um veículo, sua estrutura metálica precisa ser soldada em um único corpo resistente. Isso é feito por meio de centenas de soldas a ponto por resistência, cada uma liga localmente duas ou mais placas metálicas. Distribuir eficientemente esses pontos entre robôs é particularmente desafiador, levando em conta que: cada robôs podem fazer acessar uma parte dos pontos de solda, há tempo de movimentação entre pontos e robôs podem colidir entre si se ocuparem o mesmo espaço físico ao mesmo tempo. Há muitas maneiras factíveis de distribuir pontos de solda. No entanto, cada uma gera um resultado econômico diferente: Se um robô soldar muitos pontos se tornará um gargalo e reduzirá a taxa média de produção.Obter o conjunto de decisões operacionais que gera o melhor desempenho é o objetivo de técnicas de otimização. Há uma ampla variedade de técnicas descritas na literatura de pesquisa operacional e ciência da computação: modelos matemáticos, algoritmos, heurísticas, meta-heurísticas, etc. No contexto industrial, tais técnicas foram adaptadas para diversas variantes de problemas práticos. No entanto, estas adaptações só podem resolver as variantes para as quais foram idealizadas. Se por um lado podem se traçar paralelos entre vários aspectos de linhas robóticas de solda e tais variantes, por outro o conjunto completo de características das linhas estudadas não é tratável por (ou convertível em) nenhuma delas. A presente dissertação desenvolve uma abordagem para otimizar tais linhas, baseada em um modelo de programação linear inteira mista desenvolvido para descrever o problema. Ela também apresenta um estudo de caso para discutir e ilustrar possíveis dificuldades de aplicação e como superá-las. O modelo apresentado foi aplicado a dados de uma linha robótica de solda da fábrica, composta por quarenta e dois robôs, quatro modelos de veículos e mais de setecentos pontos de solda por veículo. A média ponderada da redução em tempo de ciclo obtida pelo modelo foi de 17.5%. Variantes do modelo, concebidas para auxiliar trabalhos futuros, são apresentadas e discutidas. / Robotic welding manufacturing lines are production lines common in automobile industries. During a vehicle's production, the vehicle's metal structure must be welded in a single resistant body. This is made by hundreds of spot-welding points, each of which tie locally two or more metal plates. Efficiently distributing these welding points amongst robots is particularly challenging, taking in account that: not all robots can perform all weld points, robots must move their welding tools between weld points, and robots might interfere with one another if they use the same geometrical space. There are multiple feasible manners to distribute the welding points. However, each of these forms generates different economical results: If a robot performs too many points, it will become a line bottleneck and reduce average throughput. To find the set of operational decisions that yields the best output is the goal of optimization techniques. There are a wide variety of such techniques described in operations research and computer sciences literature: mathematical models, algorithms, heuristics, meta-heuristics, etc. In the industrial context, these techniques were adapted to related line balancing problems. However, these adaptations can only solve the specific variants they were designed to address. While parallels can be drawn between aspects of robotic welding lines and many of such variants, the full combined set of characteristics of the studied lines is not treatable by (or convertible to) any of them. This dissertation develops a framework to optimize such lines, based on mixed-integer linear programing model developed to describe the problem. It also presents a case study to discuss and illustrate possible difficulties and how to overcome them. The presented model was applied to data from the factory's robotic welding lines composed of forty-two robots (divided in thirteen stations), four vehicle models and over seven hundred welding points for each vehicle. The weighted average reduction percentage in cycle time obtained by the model was 17.5%. Model variants, designed to aid further works are presented and discussed.

Indiana University-Purdue University Indianapolis (IUPUI) / Crashworthiness design is gaining more importance in the automotive industry due to high competition and tight safety norms. Further there is a need for light weight structures in the automotive design. Structural optimization in last two decades have been widely explored to improve existing designs or conceive new designs with better crashworthiness and reduced mass. Although many gradient based and heuristic methods for topology and topometry based crashworthiness design are available these days, most of them result in stiff structures that are suitable only for a set of vehicle components in which maximizing the energy absorption or minimizing the intrusion is the main concern. However, there are some other components in a vehicle structure that should have characteristics of both stiffness and flexibility. Moreover, the load paths within the structure and potential buckle modes also play an important role in efficient functioning of such components. For example, the front bumper, side frame rails, steering column, and occupant protection devices like the knee bolster should all exhibit controlled deformation and collapse behavior. This investigation introduces a methodology to design dynamically crushed thin-walled tubular structures for crashworthiness applications. Due to their low cost, high energy absorption efficiency, and capacity to withstand long strokes, thin-walled tubular structures are extensively used in the automotive industry. Tubular structures subjected to impact loading may undergo three modes of deformation: progressive crushing/buckling, dynamic plastic buckling, and global bending or Euler-type buckling. Of these, progressive buckling is the most desirable mode of collapse because it leads to a desirable deformation characteristic, low peak reaction force, and higher energy absorption efficiency. Progressive buckling is generally observed under pure axial loading; however, during an actual crash event, tubular structures are often subjected to oblique impact loads in which Euler-type buckling is the dominating mode of deformation. This undesired behavior severely reduces the energy absorption capability of the tubular structure. The design methodology presented in this paper relies on the ability of a compliant mechanism to transfer displacement and/or force from an input to desired output port locations. The suitable output port locations are utilized to enforce desired buckle zones, mitigating the natural Euler-type buckling effect. The problem addressed in this investigation is to find the thickness distribution of a thin-walled structure and the output port locations that maximizes the energy absorption while maintaining the peak reaction force at a prescribed limit. The underlying design for thickness distribution follows a uniform mutual potential energy density under a dynamic impact event. Nonlinear explicit finite element code LS-DYNA is used to simulate tubular structures under crash loading. Biologically inspired hybrid cellular automaton (HCA) method is used to drive the design process. Results are demonstrated on long straight and S-rail tubes subject to oblique loading, achieving progressive crushing in most cases.