Shoulder exoskeleton is a popular solution to work-related shoulder disorders and muscle fatigue. With a wide range of exoskeletons designed, a comprehensive report on how the use of shoulder exoskeletons changes shoulder biomechanics is still missing. In this project, the impact of exoskeletons on shoulder biomechanics was investigated with the musculoskeletal simulation OpenSim. This study proposed a "human-in-the-loop" optimization-based design tool for shoulder exoskeletons. This design tool incorporates the predicted biomechanical effects of a shoulder exoskeleton from musculoskeletal simulations into design considerations. This design tool was validated with a case study designing a shoulder exoskeleton based on a compliant beam and testing the design in the musculoskeletal simulation and experiments.

The exoskeleton design tool is a coupling of finite element analysis and OpenSim. OpenSim calculates the deformation of the exoskeleton with human motion, and the finite element analysis calculates the force exerted from the exoskeleton upon deformation. Then OpenSim computes muscle activities under the external force from the exoskeleton. By merging muscle activities and the resultant glenohumeral joint reaction force to an objective function, the optimization-based design loop is closed by looking for the best objective value iteratively.

Several exoskeletons were designed by the new design tool to assist different types of tasks. The design tool exhibited good ability in finding optimal solutions for a range of design choices and design requirements. Simulated tests of designed exoskeletons showed significant effects on reducing muscle activities and good robustness in resisting the influence of perturbed motions in arm-elevated tasks. An exoskeleton was selected to be tested with an experiment set up in the same way as the simulated test. Experiment results supported the performance of the exoskeleton predicted in the simulated test.

This project established a method to comprehensively predict the effect of an exoskeleton on shoulder biomechanics and provided a more comprehensive understanding of biomechanical effects of shoulder exoskeletons. This facilitated the “human-in-the-loop” design process of shoulder exoskeletons which could greatly save money and time investments into prototyping, testing, and validation.

This research aimed to optimize the process parameters of small copper parts printed with binder jetting. The optimisation was distinguished by dividing the parameters in print and post-process parameters. Binder jetting is an additive manufacturing technique that combines layers of the material in powder form with a binder. After the printing process the printed samples need curing, debinding and sintering. The binder acts as a temporary glue to hold the shape and is later removed during the debinding. The sintering fuses the powder particles together, below the melting temperature. With binder jetting it is the aim to achieve the high density parts, as there is no compression of powder during the printing process. This often leads to low density parts with binder jetting. The two problems that needed to be solved, were: how can the bleeding issues be stopped and at what temperature, time and environment can the printed copper be sintered. Multiple print sessions were done to: first test the known print parameters, second optimize the print parameters and third, print with optimized print parameters. ThermoGravimetric Analysis (TGA) was conducted to determine the correct debinding and sintering temperature of the copper samples. The bleeding issues could be solved by adjusting the saturation level. The saturation level was reduced from 104 % to 72 %. The lower percentage gave 0.5 mm dimensional accurate samples and no bleeding issues. Compression testing was done to compare the mechanical behaviour to that of conventionally manufactured copper. However the discs used for compression testing needed to be bigger in size than the cubes used for optimizing the sintering process. This lead to not fully sintered discs used for the compression testing. Meaning the achieved compressive strength and elasticity modulus were 1 % of conventionally manufactured copper. Three working sinter sessions were compared to see which gave the highest achieved density. Sintering 10 hours in argon at 1093 ℃ or in vacuum at 1080 ℃ both achieved the same high densities. Shell printed cubes performed better with an average density of 73 ± 8 % in argon and 74 ± 7 in vacuum. The samples placed on the top level inside the sintering oven achieved higher densities than in lower levels. Two shell printed cubes in argon achieved a density of 83 ± 0.5 % with a volumetric shrinkage of 40 ± 1 % and two shell printed cubes in vacuum achieved a density of 82 % with a volumetric shrinkage of 40 %. Solid cubes reached lower densities, with 67 ± 10 % in argon and 71 ± 7 % in vacuum. As with the shell printed cubes, the solid cubes have a high deviation due to the placement of the samples on different levels. The solid cubes had less volumetric shrinkage and rather high deviation due to the same reason, with 25 ± 16 % in argon and 35 ± 11 % in vacuum. The density is measured with a caliper and with Archimedes which gave quite different results. Densities measured with Archimedes were up to 22 % higher due to the porosity of the samples. All the measurements done by other copper binder jetted parts are done with Archimedes. Thus it seems the 74 % and 82 % is low when compared to 91 % density achieved in literature, but it is not known how accurate these measurements are.

Hydrofoil crafts with fully submerged foils can provide fast and economical waterway transport. However, their operation requires reliable onboard control systems to ensure the safety and comfort of their passengers, especially in rough sea conditions. This thesis project is focused on the dynamical modelling and the design of motion control systems for an experimental scale hydrofoil craft that is available at TU Delft, namely the Hydrofoil Education and Research Platform (HEARP).

The development of the dynamical model of HEARP is done by taking inspiration from the dynamics of marine crafts and aircraft and relying on different assumptions to obtain a simple and low-order model. The resulting model is a linearized state-space model with three degrees of freedom, namely heave, roll, and pitch, and includes the influence of regular waves. Because of inaccurate available data for the mass properties of HEARP, variations of the system parameters due to nonlinearities, and changes in the operating conditions, different uncertainties are assigned to most system parameters.

The use of multivariable feedback control methods for the motion control of hydrofoil crafts is limited, so this work is focused on exploiting such methods to improve the performance and robustness of such systems. The representation of the perturbed system using real parametric uncertainties is proved to be computationally expensive for the control design. Thus, the perturbed system is approximated by complex (dynamic) perturbations. A signal-based H-infinity optimal controller is designed using the nominal system, and a mu-synthesis optimal robust controller is designed using the approximated perturbed system.

The performance and robustness of the proposed controllers are evaluated in both frequency and time domains through simulations. From the results, it is concluded that both controllers offer high-performance system responses for both reference tracking and disturbance rejection of incident waves. Furthermore, by comparing the two controllers, it is observed that the mu-synthesis controller shows superior robustness for the modelled uncertainty. In contrast, the H-infinity controller has a slightly better performance when considering the perturbed systems with the real parametric uncertainty. The results of this thesis project can be used in the future to experimentally validate the accuracy of the proposed dynamical model and the performance of the designed controllers.

In this work, metamodel-based robust optimization is performed using measured scatter of noise variables. Principal component analysis is used to describe the input noise using linearly uncorrelated principal components. Some of these principal components follow a normal probability distribution, others however deviate from a normal probability distribution. In that case, for more accurate description of material scatter, a multimodal distribution is used. An analytical method is implemented to propagate the noise distribution via metamodel and to calculate the statistics of the response accurately and efficiently. The robust optimization criterion as well as the constraints evaluation are adjusted to properly deal with multimodal response. Two problems are presented to show the effectiveness of the proposed approach and to validate the method. A basketball free throw in windy weather condition and forming of B-pillar component are presented. The significance of accounting for non-normal distribution of input variables using multimodal distributions is investigated. Moreover, analytical calculation of response statistics, and adjustment of the robust optimization problem are presented and discussed.

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Production efficiency in metal forming processes can be improved by implementing robust optimization. In a robust optimization method, the material and process scatter are taken into account to predict and to minimize the product variability around the target mean. For this purpose, the scatter of input parameters are propagated to predict the product variability. Consequently, a design setting is selected at which product variation due to input scatter is minimized. If the minimum product variation is still higher than the specific tolerance, then the input noise must be adjusted accordingly. For example this means that materials with a tighter specification must be ordered, which often results in additional costs. In this article, an inverse robust optimization approach is presented to tailor the variation of material and process noise parameters based on the specified product tolerance. Both robust optimization and tailoring of material and process scatter are performed on the metamodel of an automotive part. Although the robust optimization method facilitates finding a design setting at which the product to product variation is minimized, the tighter product tolerance is only achievable by requiring less scatter of noise parameters. It is shown that the presented inverse approach is able to predict the required adjustment for each noise parameter to obtain the specified product tolerance. Additionally, the developed method can equally be used to relax material specifications and thus obtain the same product tolerance, ultimately resulting in a cheaper process. A strategy for updating the metamodel on a wider (noise) base is presented and implemented to obtain a larger noise scatter while maintaining the same product tolerance.@en

A robustness criterion that employs skewness of output is presented for a metamodel-based robust optimization. The propagation of a normally distributed noise variable via nonlinear functions leads to a non-normal output distribution. To consider the non-normality of the output, a skew-normal distribution is used. Mean, standard deviation, and skewness of the output are calculated by applying an analytical approach. To show the applicability of the proposed method, a metal forming process is optimized. The optimization is defined by an objective and a constraint, which are both nonlinear. A Kriging metamodel is used as nonlinear model of that forming process. It is shown that the new robustness criterion is effective at reducing the output variability. Additionally, the results demonstrate that taking into account the skewness of the output helps to satisfy the constraints at the desired level accurately.

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Robust optimization is a powerful method to find the parameters for a process at which its output is least sensitive to the variation of the input parameters. In this method, measured or estimated noise parameters are used to estimate the scatter of the output. At the optimum design, the variation in noise parameters leads to a minimum scatter of the output. If this minimum scatter of the output does not meet the specified tolerance, then the input noise must be adjusted accordingly. This means for example that materials with a tighter specification must be ordered, which usually incurs additional costs. In this article, an inverse method is presented to tailor the variation of noise parameters based on the allowable tolerance in the output. This method is successfully applied to a non-linear process, lab-type B-pillar part. The results show how to adjust the input noise parameters at a minimum cost to meet the required output tolerance.@en

Optimization under uncertainty requires proper handling of those input parameters that contain scatter. Scatter in input parameters propagates through the process and causes scatter in the output. Stochastic methods (e.g. Monte Carlo) are very popular for assessing uncertainty propagation using black-box function metamodels. However, they are expensive. Therefore, in this article a direct method of calculating uncertainty propagation has been employed based on the analytical integration of a metamodel of a process. Analytical handling of noise variables not only improves the accuracy of the results but also provides the gradients of the output with respect to input variables. This is advantageous in the case of gradient-based optimization. Additionally, it is shown that the analytical approach can be applied during sequential improvement of the metamodel to obtain a more accurate representative model of the black-box function and to enhance the search for the robust optimum.

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