The work presented in this report is an attempt to allow density-based topology optimization algorithms to take into account machining costs when optimizing. To this end, and estimation model for the cost of 2.5-axis CNC milling was developed based on the principles of Design for Manufacturing. This estimation model was implemented in a generic topology optimization algorithm in five steps of complexity. The behaviour of the model was evaluated at each step by means of a set of experiments, showing how the addition of the cost estimation influences the optimization results. The most promising results from the experiments are validated with Autodesk Fusion 360, with which the designs were programmed to be machined on a CNC mill. The machining times from this software were used to calculate the actual machining costs and these were compared to the machining costs estimated by the optimizer. Two formulations of the method were found to be useful for estimating the machining cost of designs. A simple formulation that uses the shape factor and the differentiation between internal and external pockets to estimate the cost resulted in a cost saving of 13% at the expensive of a compliance increase of 9%. The more complex formulation uses multiple pocket domains to evaluate pocket-specific properties. This allowed a more accurate estimation of the costs, but did not result in a better performing optimization. The cost saving was comparable at 12%, but the compliance increased by 16%. It was concluded that there is a use for both methods. The simple formulation allows to find cheaper designs, but does not allow much control in the cost estimation. The complex formulation however can be used to fine-tune the method for a specific situation, which could enable it to perform better that the simple formulation.

Offshore wind turbines have seen a significant increase in size over the past decades. A critical consequence of this increase in size is a substantial increase in the torque transferred from the wind turbine rotor. This leads to the necessity of larger and heavier drivetrains to adhere to structural failure requirements and subsequently to larger and heavier tower and support structures increasing the total mass and cost of the turbine. Hence, reducing the mass-to-torque ratio of the drivetrain has become a key design challenge. The company Delft Offshore Turbines (DOT) proposes to replace the conventional drivetrain in the top of the turbine with a hydraulic drivetrain, which has a lower mass-to-torque ratio. Although this concept shows promise, finding a low mass design that fulfils the infinite fatigue life requirement can be challenging. Using topology optimization to minimize mass while constraining the structural requirements could, therefore, be instrumental in the realization of this concept. The design case by DOT can be classified as rotating machinery. In general rotating machinery is commonly subjected to periodic loading that varies non-proportionally in time. This results in fluctuating stresses causing material fatigue. A consequence of the non-proportionality of loading is that the time response needs to be computed to evaluate for fatigue, which adds additional computation cost. However, another common aspect found in rotating machinery is that parts are cyclic symmetric, which allows for a potential reduction in computation cost. In this thesis a method is presented to implement infinite fatigue life constraints into density based topology optimization for structures subjected to non-proportional loading, while cyclic symmetric properties are exploited to reduce computation cost. It was found that when the load case on a cyclic symmetric part adheres to certain conditions, a single static FE-analysis can provide multiple time steps for a quasi-static analysis. Decreasing the computational burden roughly proportional to the unique number of time steps obtained. The largest local variations in stress are estimated using a smooth min/max function and aggregated into a global constraint. The method was tested on several numerical problems as well as applied to the DOT design case. The results showed that the method was able to properly constrain a global fatigue constraint while minimizing mass, achieving final designs that might not be trivial to find by hand.

Topology optimization is a valuable tool for the optimization of all kinds of structures. It can create highly efficient but complex designs. This complexity can make these structures challenging to clean. However, cleanability is often a requirement in, for example, the medical or food industries. Currently, no method exists to reduce the complexity and create these cleanable structures using topology optimization. The goal of this project is to create a method that can generate two-dimensional cleanable structures. In this context, cleanability has been defined using two requirements. Every section of the surface must be visible, and no sharp edges can appear on the exterior. These requirements are met by generating a structural and cleanable shell around the design. The shell needs to be optimized to minimize the compliance of the entire structure. The addition of this shell has been achieved by the use of a hybrid method. This method combines two structural optimization methods, a level-set method, and the modified SIMP approach. The level-set method acts as a shape optimization method. Its shape forms a boundary in which the modified SIMP method is used to generate a design. Material is placed at this boundary of the level-set shape to create a shell that encloses the structure. Experiments on several sets of boundary conditions show a successful creation of a shell in every case. The resulting designs are not guaranteed to be cleanable, but a satisfactory result has been achieved in every case by changing some parameters. This method sets the stage for further development toward the application of topology optimization to create cleanable designs.