An additive manufacturing filter for topology optimization of print-ready designs

Additive manufacturing (AM) offers exciting opportunities to manufacture parts of unprecedented complexity. Topology optimization is essential to fully exploit this capability. However, AM processes have specific limitations as well. When these are not considered during design optimization, modifications are generally needed in post-processing, which add costs and reduce the optimized performance. This paper presents a filter that incorporates the main characteristics of a generic AM process, and that can easily be included in conventional density-based topology optimization procedures. Use of this filter ensures that optimized designs comply with typical geometrical AM restrictions. Its performance is illustrated on compliance minimization problems, and a 2D Matlab implementation is provided.Structural Optimization and Mechanic

Design optimization of shape memory alloy structures

This thesis explores the possibilities of design optimization techniques for designing shape memory alloy structures. Shape memory alloys are materials which, after deformation, can recover their initial shape when heated. This effect can be used for actuation. Emerging applications for shape memory alloys are e.g. miniaturized medical instruments with embedded actuation, as well as microsystem components. However, designing effective shape memory alloy structures is a challenging task, due to the complex material behavior and the close relationship between geometry, electrical, thermal and mechanical properties of the structure. In this thesis, various approaches are developed to combine optimization algorithms with computational modeling of shape memory alloy structures. The focus is on the shape memory behavior of NiTi alloys that exhibit the R-phase/austenite transformation. Dedicated computationally efficient constitutive models are formulated to capture this behavior and predict the performance of designs. The considered optimization approaches include deterministic shape optimization, shape optimization under bounded-but-unknown uncertainty, gradient-based shape optimization and topology optimization. Together they provide a collection of efficient and systematic techniques to generate well-performing designs. Their applicability and effectiveness is evaluated by application to design studies of realistic complexity, involving the design of miniature grippers and steerable catheters. The developed design optimization techniques are expected to be of great use for the design of future instruments and devices that utilize shape memory alloy actuation.Mechanical Maritime and Materials Engineerin

Interface-Complexity: And its influence on performance and workload

This research project on the field of human machine systems, especially human control of complex systems has as objective to find a relation between interface complexity and workload and a relation between interface complexity and performance. Complexity is split into three parts: system complexity, task complexity and interface complexity. The border between task complexity and interface complexity is very narrow and only exist on higher abstraction levels.Biomechanical EngineeringMechanical, Maritime and Materials Engineerin

Topology optimization of 3D self-supporting structures for additive manufacturing

The potential of topology optimization to amplify the benefits of additive manufacturing (AM), by fully exploiting the vast design space that AM allows, is widely recognized. However, existing topology optimization approaches do not consider AM-specific limitations during the design process, resulting in designs that are not self-supporting. This leads to additional effort and costs in post-processing and use of sacrificial support structures. To overcome this difficulty, this paper presents a topology optimization formulation that includes a simplified AM fabrication model implemented as a layerwise filtering procedure. Unprintable geometries are effectively excluded from the design space, resulting in fully self-supporting optimized designs. The procedure is demonstrated on numerical examples involving compliance minimization, eigenfrequency maximization and compliant mechanism design. Despite the applied restrictions, in suitable orientations fully printable AM-restrained designs matched the performance of reference designs obtained by conventional topology optimization.Accepted Author ManuscriptStructural Optimization and Mechanic

Topology optimization for multi-axis machining

This paper presents a topology optimization approach that incorporates restrictions of multi-axis machining processes. A filter is defined in a density-based topology optimization setting, that transforms an input design field into a geometry that can be manufactured through machining. The formulation is developed for 5-axis processes, but also covers other multi-axis milling configurations, e.g. 2.5D milling and 4-axis machining by including the appropriate machining directions. In addition to various tool orientations, also user-specified tool length and tool shape constraints can be incorporated in the filter. The approach is demonstrated on mechanical and thermal 2D and 3D numerical example problems. The proposed machining filter allows designers to systematically explore a considerably larger range of machinable freeform designs through topology optimization than previously possible.Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Structural Optimization and Mechanic

Integrated component-support topology optimization for additive manufacturing with post-machining

Purpose: The purpose of this paper is to communicate a method to perform simultaneous topology optimization of component and support structures considering typical metal additive manufacturing (AM) restrictions and post-print machining requirements. Design/methodology/approach: An integrated topology optimization is proposed using two density fields: one describing the design and another defining the support layout. Using a simplified AM process model, critical overhang angle restrictions are imposed on the design. Through additional load cases and constraints, sufficient stiffness against subtractive machining loads is enforced. In addition, a way to handle non-design regions in an AM setting is introduced. Findings: The proposed approach is found to be effective in producing printable optimized geometries with adequate stiffness against machining loads. It is shown that post-machining requirements can affect optimal support structure layout. Research limitations/implications: This study uses a simplified AM process model based on geometrical characteristics. A challenge remains to integrate more detailed physical AM process models to have direct control of stress, distortion and overheating. Practical implications: The presented method can accelerate and enhance the design of high performance parts for AM. The consideration of post-print aspects is expected to reduce the need for design adjustments after optimization. Originality/value: The developed method is the first to combine AM printability and machining loads in a single topology optimization process. The formulation is general and can be applied to a wide range of performance and manufacturability requirements.Structural Optimization and Mechanic

Combined optimization of part topology, support structure layout and build orientation for additive manufacturing

Additive manufacturing (AM) enables the fabrication of parts of unprecedented complexity. Dedicated topology optimization approaches, that account for specific AM restrictions, are instrumental in fully exploiting this capability. In popular powder-bed-based AM processes, the critical overhang angle of downward facing surfaces limits printability of parts. This can be addressed by changing build orientation, part adaptation, or addition of sacrificial support structures. Thus far, each of these measures have been studied separately and applied sequentially, which leads to suboptimal solutions or excessive computation cost. This paper presents and studies, based on 2D test problems, an approach enabling simultaneous optimization of part geometry, support layout and build orientation. This allows designers to find a rational tradeoff between manufacturing cost and part performance. The relative computational cost of the approach is modest, and in numerical tests it consistently obtains high quality solutions.Structural Optimization and Mechanic

Topology optimization of part and support structures for additive manufacturing considering machining forces

Structural Optimization and Mechanic

Topology optimization for additive manufacturing with controllable support structure costs

Advances in additive manufacturing (AM) allow economical production of components with unprecedented geometric complexity. This offers exciting opportunities for innovative designs, and particularly topology optimization has been identified as a key technique to fully exploit the capabilities of AM. However, also AM involves manufacturing restrictions, such as limitations on the inclination of overhanging parts. To deal with this problem, either sacrificial supporting structures can be added during the process, or only self-supporting designs can be considered. Both approaches have disadvantages, as support structures add material and post-processing costs, while demanding exclusively self-supporting designs may impose strong restrictions on achievable performance. With current methods, designers are limited to a choice between these two extremes. To open up a wider range of designs, this paper presents and demonstrates a topology optimization formulation that allows the designer to find trade-off solutions between design performance and support structure costs, considering both printing and removal costsStructural Optimization and Mechanic

Expected improvement based infill sampling for global robust optimization of constrained problems

A novel adaptive sampling scheme for efficient global robust optimization of constrained problems is proposed. The method addresses expensive to simulate black-box constrained problems affected by uncertainties for which only the bounds are known, while the probability distribution is not available. An iterative strategy for global robust optimization that adaptively samples the Kriging metamodel of the computationally expensive problem is proposed. The presented approach is tested on several benchmark problems and the average performance based on 100 runs is evaluated. The applicability of the method to engineering problems is also illustrated by applying robust optimization on an integrated photonic device affected by manufacturing uncertainties. The numerical results show consistent convergence to the global robust optimum using a limited number of expensive simulations.Structural Optimization and Mechanic