Incremental sheet forming process is a relatively new method of forming which is increasingly being used in the industry. Complex shapes can be manufactured using this method and the forming operation doesn't require any dies. High strains of over 300 % can also be achieved. Incremental sheet forming method is used to manufacture many different components presently. Prototype examples include car headlights, tubs, train body panels and medical products. The work done in the thesis deals with the prediction of the steady state forces acting on the tool during forming. Prediction of forces generated would help to design the machine against excessive vibrations. It would help the user to protect the tool and the material blank from failure. An efficient design ensures that the tool would not get deflected out of its path while forming, improving the accuracy of the finished part. To study the forces, experiments were conducted by forming pyramid and cone shapes. An experimental arrangement was set up and experimental data was collected using a data acquisition system. The effect that the various process parameters, like the thickness of the sheet, wall angle of the part and tool diameter had on the steady state force were studied. A three dimensional model was developed using commercial finite element software ABAQUS using a new modeling technique to simulate the deformation of the sheet metal blank during incremental sheet forming. The steady state forces generated for any shape, with any set of parameters used, could be predicted using the numerical model. The advantage of having a numerical model is that the forces can be predicted without doing experiments. The model was used to predict the steady state forces developed during forming of pyramid and cone shapes. The results were compared and were seen to be reasonably close to the experimental results. Later, the numerical model was validated by forming arbitrary shapes and comparing the value obtained from simulations to the value of the measured steady state forces. The results obtained from the numerical model were seen to match very well with the experimental forces for the new shapes. The numerical model developed using the new technique was seen to predict forces to a reasonable extent with less computational time as compared to the models currently available.

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Manual material handling (MMH) tasks are associated with the development of work-related musculoskeletal disorders (WMSDs). Minimizing the frequency and intensity of handling objects is an ideal solution, yet MMH remains an integral part of many industry sectors, including manufacturing, construction, warehousing, and distribution. Physical exposure assessment can help identify high-risk tasks, guide the development and evaluation of ergonomic interventions, and contribute to understanding exposure-risk relationships. Physical exposure can be evaluated using self-assessment, observational methods, and direct measurements. Nevertheless, implementing these methods in situ can be challenging, time consuming, expensive, and infeasible or inaccurate in many cases. Thus, there is a critical need to improve physical exposure assessments to protect workers and save costs. This dissertation assessed the accuracy of a markerless motion capture system (MMC) to quantify physical exposures during MMH tasks using three studies. Specifically, the first study investigated the performance of an MMC system, together with machine learning algorithms, for classifying diverse MMH tasks during a simulated complex job. In the second study, the feasibility of predicting dynamic hand forces was determined, using alternative measures, such as kinematics from MMC and/or in-sole pressure systems, coupled with a machine learning algorithm. Finally, in the third study, we systematically evaluated MMC for assessing biomechanical demands, by comparing outputs from a full-body musculoskeletal model driven by kinematic and kinetics from gold standard input and estimates derived from the MMC and in-sole pressure measurement system. Overall, the findings of these studies demonstrated the potential of using MMC to classify several common occupational tasks and to estimate the associated biomechanical demands for a given worker (automatically and with minimal physical contact). Additionally, the methods developed here can help stakeholders rapidly assess an individual worker's exposure to physical demands during diverse tasks. / Doctor of Philosophy / Manual material handling (MMH) tasks expose workers to known risk factors for work-related musculoskeletal disorders (WMSDs) such as back and shoulder pain. Accurately quantifying workplace exposures to these risk factors is an essential aspect of identifying high-risk working conditions and for developing/evaluating workplace interventions to reduce WMSD risks. Current physical exposure assessment tools are labor-intensive, offer crude measures, and have limited application due to costs or feasibility. Using markerless motion capture (MMC) systems in the workplace could enable full or partial automation for the collection of critical measures such as the tasks a worker performs, the hand forces involved, and their biomechanical demands. New approaches are needed, though, since such automation is challenging due to variations in the type of input data required for different physical exposure assessments. In this dissertation, our goal was to assess the accuracy of MMC as a tool to quantify physical exposures during MMH tasks. In support of our goal, three studies were completed. In the first study, we investigated the accuracy of using data from MMC together with machine learning algorithms to classify diverse MMH tasks, and distinguish among different task conditions. Our results emphasized that classification performance was satisfactory, though it differed between feature sets, MMH tasks, and between males and females. The second study explored combining MMC and IPM data with machine learning algorithms to predict hand forces during MMH tasks. Our results were encouraging overall, but predictions were less accurate in pushing and pulling tasks. In the third study, we evaluated an approach for estimating biomechanical demands on data obtained from MMC and in-sole pressure measurement systems. We compared estimates from a musculoskeletal model driven by kinematics from a whole-body inertial measurement unit and kinetics from direct measures of hand loads, and kinematics from MMC. Our findings support using MMC and kinetics from predicted hand forces as input for estimating biomechanical demands. Overall, findings from these studies show that MMC can automatically classify common occupational tasks, predict dynamic hand forces, and estimate biomechanical demands with minimal physical contact. This new approach could allow stakeholders to assess worker's exposure and the efficiency of ergonomic interventions.

When performing a biomechanical analysis of human movement, knowledge about the ground reaction force (GRF) is necessary to compute forces and moments within joints. This is important when analysing a movement and its effect on the human body. To obtain knowledge about the GRF, the gold standard is to use force plates which directly measure all three components of the GRF (mediolateral, anteroposterior and normal). However, force plates are heavy, clunky and expensive, setting constraints on possible experimental setups, which make it desirable to exclude them and instead use a predictive method to obtain the full GRF. Several predictive methods exist. The node model is a GRF predictive method included in a musculoskeletal modeling software. The tool use motion capture and virtual actuators to predict all three GRF components. However, this model has not yet been validated during weighted leg press and weighted squat. Furthermore, the normal component of the GRF can be measured continuously during the activity with pressure sensitive insoles (PSIs), which might provide better accuracy of the GRF prediction. The objectives of this thesis were to investigate whether force plates can be exluded during weighted leg press and weighted squat and to investigate whether PSIs can improve the GRF prediction. To investigate this, the node model and a developed shear model was validated. The shear model computes the two shear GRF components based on data from PSIs, an external load acting upon the body and data from a motion capture system. Both the node model and the shear model were analysed with two test subjects performing two successive repetitions of both weighted squat and weighted leg press in a flywheel exercise device. During the leg press exercise, the node model had a mean coeffcient of correlation (Pearson's) ranging from 0.70 to 0.98 for all three directions with a mean root mean square error ranging between 8 % to 20 % of the test person's body weight. The developed shear model had a coeffcient of correlation (Pearson's) between 0.64 to 0.99 and a mean root mean square error between 3 % and 21 % of the test person's body weight. This indicates that it is possible to exclude force plates and instead predict the GRF during weighted leg press. During squat, neither the node model nor the shear model provided accurate results regarding the mediolateral and anteroposterior components of the GRF, suggesting that force plates can not yet be excluded to obtain the full GRF during weighted squat. The results of the normal component during leg press was somewhat improved with the shear model compared to the node model, indicating that using PSIs can improve the results to some extent.

Ship Manoeuvring Simulators have proved to be powerful tools on analyzing the feasibility of new maritime maneuvers and new port constructions. In order to provide a complete immersive and real environment, such simulators must correctly represent the dynamics of the controlled vessel as well as the actuation of the tugboats, which have been extremely used over the last years due to the increasing complexity on the maritime maneuvers. Although few simulators can correctly model the dynamics of the tugboats, they still represent their actuation through the so-called \"vector tug model\". This is usually the case because it is expensive to run several integrated-simulators in real-time and the simulator centers do not have trained tugboat captains available. The vector tugs are usually represented as simplified external forces actuating on a vessel. The simplicity of such models causes a loss of realism during a maritime simulation due to the fact that neither the forces exerted on a towed vessel nor the tugboat\'s actuation position are accurate. In addition, tugboats\' actuation response time is usually not taken into account under the current vector tug models used on Ship Manoeuvring Simulators. The main objective of this work is to provide an innovative approach for vector tug actuation modeling in such a way that the towing force magnitude and actuation positions are accurate either in push or pull operation modes. The author will expand the static equilibrium model for tugboat force prediction presented in Brandner (1995) and Artyszuk (2014) and combine it along with optimization techniques in order to accurately obtain the tugboats\' actuation either working under the direct maneuver (i.e., tugboat uses solely its propeller power in order to exert force on a towed vessel) or working under the indirect maneuver (i.e., tugboats use the environmental disturbances and the hull drag in order to maximize their actuation force on a towed vessel). The implementation of the new mathematical model provides a new level of reality when vector tugs are used in Ship Manoeuvring Simulators. / Simuladores Navais têm provado ser poderosas ferramentas, tanto na análise de viabilidade de novas manobras portuárias, quanto na construção de novos portos. De modo a conseguir criar um ambiente imersivo e realista, tais simuladores devem conseguir representar corretamente a dinâmica de um navio a ser controlado e a atuação dos rebocadores portuários no mesmo. Embora alguns simuladores consigam modelar corretamente a dinâmica de rebocadores portuários, eles ainda representam tal atuação utilizando o modelo comumente chamado de \"rebocadores vetoriais\". Tal fato normalmente acontece pois é muito caro utilizar diversos simuladores conectados em tempo real. Além disso, em muitas ocasiões, os centros de simulação não têm disponível um comandante de rebocador treinado e capaz de manusear o mesmo de forma correta. Os rebocadores vetoriais normalmente são representados com modelos simplificados de forças externas atuantes em um navio a ser rebocado. A simplicidade de tais modelos gera uma grande perda de realismo durante uma simulação marítima dado que tanto as forças exercidas em um navio a ser rebocado quanto as posições de atuação dos rebocadores são imprecisas. Ainda, os tempos de resposta para a atuação dos rebocadores normalmente não é levado em conta nos modelos de rebocadores vetoriais presentes atualmente. O principal objetivo deste trabalho é prover uma abordagem inovadora para a modelagem da atuação de rebocadores vetoriais, de tal modo que a magnitude da sua força de reboque e seu posicionamento, tanto atuando no modo empurrar quanto no modo puxar, sejam fidedignos a realidade. O autor irá expandir o modelo de equilíbrio estático para predição de forças de atuação de rebocadores apresentado tanto em Brandner (1995) quanto em Artyszuk (2014), e irá introduzir técnicas de otimização de modo a obter a configuração precisa de atuação dos rebocadores tanto na manobra de modo direto quanto na manobra de modo indireto. As implementações propostas elevarão o nível de realidade de Simuladores Navais quando rebocadores vetoriais forem empregados.

Aircraft design must be lightweight and cost-efficient on the condition of aircraft certification. In addition to standard load cases, human-induced loads can occur in the aircraft interior. These are crucial for optimal design but difficult to estimate. In this study, a simple bipedal spring-mass model with roller feet predicted human-induced loads caused by human gait for use within an end-to-end design process. The prediction needed no further experimental data. Gait movement and ground reaction force (GRF) were simulated by means of two parameter constraints with easily estimable input variables (gait speed, body mass, body height). To calibrate and validate the prediction model, experiments were conducted in which 12 test persons walked in an aircraft mock-up under different conditions. Additional statistical regression models helped to compensate for bipedal model limitations. Direct regression models predicted single GRF parameters as a reference without a bipedal model. The parameter constraint with equal gait speed in experiment and simulation yielded good estimates of force maxima (error 5.3%), while equal initial GRF gave a more reliable prediction. Both parameter constraints predicted contact time very well (error 0.9%). Predictions with the bipedal model including full GRF curves were overall as reliable as the reference.