In our first blog of this series “Generative Design insights: a game changer for product development and industrial 3D printing” we already gave an overview of generative design and additive manufacturing. In the following articles, we would now like to examine some aspects in more detail.
Given global trends towards resource scarcity and reducing use of resources, the optimization of parts is becoming more and more important. Superfluous material should be saved, and the parts must become lighter. Particularly for moving products, from cars and airplanes to robots and machines, lightweight construction is an essential topic in product development for reducing energy consumption. In recent years, generative design has become a way to quickly and easily create precisely such desirable lightweight geometries.
But why is Generative Design such a prominent topic nowadays, when the field of topology optimization for the mentioned requirements has already been around for decades? Classical topology optimization is characterized by high manual effort, long computation times and manual redesign, where a large part of the optimization refinements are lost and reduce the result quality. Due to these limitations in the applicability of topology optimization, the process is in most cases only used for special components. In addition, the production of more complex lightweight designs using classical production methods is also challenging and associated with high costs.
Generative Design is driven by various technological aspects such as cloud computing and additive manufacturing. By applying Generative Design, a company can get much more efficient and innovative, among many other benefits.
In recent years, there have been changes in various areas in this respect that make a new approach possible. On the one hand, the industrialization of additive manufacturing means that a manufacturing technology can be used productively which, with its geometric freedom, can also produce highly complex lightweight designs without any difficulties. In order to use these diverse potentials effectively and comprehensively, novel designs must therefore be generated to an increasing extent. This is hardly possible with the lengthy and costly process of classical topology optimization.
Generative Design is not a reinvention of part optimization; based on the finite element method (FEM), it certainly follows the classical approach of structural mechanical analysis. However, the complete optimization process is re-imagined: instead of a time-consuming, manual process, the focus is on a streamlined, user-oriented procedure to free the user from tedious routine work and to offer more opportunities for creativity and conceptual design.
Some providers try to speed up existing algorithms or simply achieve a fast result through sheer computing power. Others rely on a new approach. Instead of an uneven mesh, standardized voxels are used for computation. With this method and specially adapted algorithms trimmed for speed, high-performance design iterations can be run extremely quickly, optimizing the mechanical performance of the part while keeping weight to a minimum – all while considering the given requirements. This enables key challenges in today’s business world to be met: optimized parts and product development processes increase the efficiency of the company and its products. In addition, lightweight parts are more sustainable and, with fast and simple geometry generation, can be developed more flexibly and individually. This strengthens the competitiveness and positioning of the company.
The fact that these factors are not only theoretical and are becoming increasingly important is shown by the considerable market growth that analysts are attributing to the generative design sector in the future. With an average annual growth rate of 24%, the market is expected to increase significantly in the future, forecasts ABI Research. A market volume of around $45 billion is therefore expected for the generative design sector in 2030.
Increasing data processing and computing power as well as the trend of cloud computing increasingly enable the virtualization of manufacturing through simulation. A Digital Twin – a completely digital image of production and the product – is created. From design to production simulation and beyond, the essential steps of the product are run through and optimized digitally. Generative Design provides the starting point here with the component design, which can then be followed by various other simulations, such as production simulation and the calculation of further post-processing steps, so that the part runs through the production process virtually, piece by piece. Virtualization is thus an essential element of the smart factory.
With connected systems, numerous relevant data can be generated, which can lead to new insights at various points. An example of generative design can be that the milling machine reports back vibrations during the post-processing of functional surfaces of the part, which then flow into the design generation and produce an adapted, better design. This can lead to a more efficient production, reducing scrap while being leaner and more sustainable in the process operation. Thus, with virtualization based on simulation tools and generative design – as a disruptive technology for product optimization – a company can become sustainably more efficient and competitive.
Learn more about how to create more efficient, sustainable products and download the free Generative Design Infographic
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