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Advances in manufacturing technologies and computational engineering are key enablers for optimized designs necessary for product performance improvements. Amongst other manufacturing technologies, particularly Additive Manufacturing (AM) is pushing the envelope of feasible design complexity challenging design engineers as well as their Computer-Aided Design (CAD) tools. The research field of Design for Additive Manufacturing (DfAM) provides an exhaustive supply of specific engineering design knowledge and methodological approaches accordingly. To enable design engineers to put those approaches into practice, this research gathers and structures not yet addressed AM-related requirements on the state of the art CAD tools. Additionally, architectural CAD functions as well as features are being pointed out and envisioned design workflow adaptions introduced, necessary to enable engineers to holistically utilize AM design potentials with CAD systems of the mid-term future.

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The presented paper describes a shape optimization workflow using Bayesian strategies. It is applied to a novel automotive axle system consisting of leaf springs made from glass fiber reinforced plastics (GFRP). Besides the primary objectives of cost and mass reduction, the assembly has to meet multiple technical constraints with respect to various loading conditions. The related large-scale finite element model is fully parameterized by splines, hence the general shape of the guide curve as well as the spring’s height, width and material properties can be altered by the corresponding workflow. For this purpose, a novel method is developed to automatically generate high-quality meshes depending on the geometry of the respective springs. The size and complexity of the model demands the implementation of efficient optimization techniques with a preferably small number of required response function evaluations. Therefore, an existing optimization framework is extended by state-of-the-art Bayesian methods, including different kernel combinations and multiple acquisition function approaches, which are then tested, evaluated and compared. To properly address the use of GFRP as spring material in the objective function, an appropriate cost model is derived. Emerging challenges, such as conflicting targets regarding direct material costs and potential lightweight measures, are considered and investigated. The intermediate steps of the developed optimization procedure are tested on various sample functions and simplified models. The entire workflow is finally applied to the complete model and evaluated. Concluding, ideas and possibilities in improving the optimization process, such as the use of models with varying complexity, are discussed.

This paper presents a density-based shape optimization using an interface motion scheme as in level set methods. The aim is to generate optimal material distributions with high-quality interfaces within a uniform geometric representation for topology and shape optimization. This reduces the effort for post-processing and facilitates an automated conversion to CAD models. By using a density function, the proposed method can seamlessly adopt density-based topology optimization results as the initial design. Finite element analyses are performed using the same mesh and density field as for optimization. The interface motion is based on an advection equation and shape derivatives without a penalization of intermediate densities. This prevents the formation of large grey transition regions while avoiding mesh-dependent spatial oscillations of the interface. Thus, boundary-fitted meshes with smooth surfaces and sufficiently retained stiffness and volume can be extracted. In addition, an optional constraint for the mean curvature of the surface as well as the necessary shape derivative is introduced. Thus, the surface curvature can be limited to a technically justifiable value, which improves, for instance, the manufacturability and fatigue strength. The evaluation is carried out using a two-dimensional example starting from a topology-optimized design as well as four three-dimensional examples starting from a trivial design. The considered optimization objective is to minimize the volume with respect to compliance constraints and, partially, additional mean curvature constraints.