In its current state, consumer appliance product design relies heavily on traditional design and manufacturing approaches that don’t make the most of new manufacturing methods, like Additive Manufacturing (AM), which are removing design constraints and opening up new possibilities. The optimal shape of a part is often organic and counter intuitive as found in nature, so designing it requires a different approach.
Finite element based topology optimization is a process of finding the optimal distribution of material and voids in a given design space, dependent on loading and boundary conditions, such that the resulting structure meets prescribed performance targets.
Topology optimization lets you specify where supports and loads are located on a volume of material and lets the software find the best shape. You can now easily perform light weighting of structures, extract CAD shapes and quickly verify the optimized design. You can also simulate spatially dependent materials like composite parts, 3-D printed components, and bones and tissues for more accurate results.This will help designers to optimize the product from root level without undergoing prototyping.
Although topology optimization has been an available design tool for a few decades, restrictions imposed by traditional manufacturing techniques have severely limited its usefulness. This is changing now with the continuous development and increased use of AM in industry. With AM, it is possible to print almost any geometry. Unlike sizing- and shape optimization, structures optimized through topology optimization can attain any shape within the design space. If we take for example parts to be produced through Additive Manufacturing the possibilities are almost endless considering Additive Manufacturing does not provide restrictions on the shape of the part, the only restriction with Additive Manufacturing is the minimal wall thickness.
Optimizing parts through topology optimization without applying manufacturing constraints will result in organic looking products. This is because the material will choose the shortest path from the load to the constraint, resulting in the most efficient shape mathematically.
This is the area used by solver for the optimization. When the solver has to perform an optimization it removes material from the design space that is not needed, it cannot add material, for this reason it is important to assign a design space that is large enough so the solver can keep removing material until it finds the best topology.
A geometric restriction places restrictions on the changes that the solver can make to the topology, this can be interpreted as the areas that are not allowed to change during the optimization process. In technical terms these regions are called the frozen regions. During the optimization the solver will leave these regions as they are while extracting surrounding material to find the best topology.
This step defines what we want to achieve with the optimization. Most of the time with topology optimizations it is the case that we want to maximize the stiffness while minimizing the volume of the model. The increased stiffness is achieved by minimizing the strain energy in the model. It is also possible to optimize towards other objectives such as certain Eigenfrequencies or local displacements.
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