If you are a manufacturer, you are certainly familiar with the concept of generative design by now. Yet its concrete definition is still confusing.
A large number of players in the industrial sector would have us believe that generative design is simply a branch of topological optimization or proceduralmodeling. But in reality, generative design is a much deeper shift in approach than that. It’s an artificial intelligence-powered process that harnesses the power of the cloud to advance innovation by exploring thousands of possibilities, rather than just improving an existing design like topology optimization does.
If this notion has not escaped you, you are probably already convinced of the revolutionary possibilities that generative design offers for the future of industrial automation. Nonetheless, you might think that the technology is only useful for complex geometries that can only be produced using additive manufacturing techniques.
It’s true that most of the generative design examples you’ve seen so far are rather complex, with supernatural-looking metal printed parts. One of the reasons for this is that generative design is not connected to traditional means of manufacturing. And if your business can’t afford a $ 1.7 million metal 3D printer, the technology that goes with it seems difficult or relevant.
Indeed, many aspects of industrial automation are initially too expensive and too complicated to adopt in their entirety. In the 1960s, when the first industrialrobots appeared, only companies like General Motors could afford to buy them.
Generative design, the automated process of creating a geometric design based on simulation results generated by understanding the manufacturing process, appears to be the latest unattainable technology. But the good news for manufacturers is that the scope of generative design automation is expanding to include new industrial processes that support traditional manufacturing.
Take the case of generative design software: faced with a manufacturing constraint such as molding or machining, it is capable of producing designs that you can actually manufacture using the tools and equipment that are probably already in your stores. workshops. And these outcomes are not only possible, they are affordable.
If we take, for example, three versions of a metal wheelchair support part, derived from the same generative design process, the parts are roughly the same: they have the same functional and behavioral requirements, the same material., and the same raw form. The only difference is in the manufacturing process, but as we will see, not all processes are the same.
The original part is cast in metal, at a unit cost of approximately € 13.45 after complete depreciation of the tools. Using the 3-axis milling machine can be done on a more common machining center, but it costs almost $ 90 to manufacture, due to the time it takes to shape the organic shape. The third solution, 2.5-axis machining, is utopian because it produces a part that does everything the molded part can do, but at a cost of € 22.46. For virtually the same price, you get the best solution to solve your design problem, without the need for custom tooling, and using existing machinery in your shop.
Of course, the manufacturing process has a huge influence on what kind of geometry you produce, and it’s finally possible for anyone to apply generative design technology to accessible manufacturing means. However, the promise of industrial automation does not end with generative design.
To take the next big step in automation, a digital pipeline is needed to enable a seamless flow of operations from concept to physical product.
Let’s take a look at the classic flow of product development today: an engineer performs design geometry and then passes it on to someone else for simulations. This person must finalize the simulations and validate them before handing them over to a third person who creates the machining instructions in G code form. In many cases, this G code file is then copied to a USB stick and then transported to the workshop, where the machining operator can transfer it to the machine control and finally start cutting the metal.
This waterfall model is linear and totally inefficient. The solution is an automated agile product development process, which allows for a form of convergence so that a person can start working on the simulation studies before the design is even complete. With the feedback from the simulations, another person can start working on the manufacturing instructions, also before the full design is complete.
This will allow your company to work not as a factory of the XIX th century, but as a highly competitive sports team. By bringing all the elements of the process in parallel, you will shorten the overall amount of time it takes to create a product, and this will lead to better innovation, improved product behavior, lower costs, and faster time to market.: all these elements are essential for a company to be at the top.
In order for all of this to work, you will need to create this digital pipeline – a direct link between the manufacturing instructions produced in the software and the machine tool. In this case, the G code is created upstream and sent directly to the machine tool without the engineer even being aware of it.
Let’s take an example: when you want to print something on paper, you send it to the network, directly from your word processor. You do not copy files to the network and you do not need to plug in a USB key to make the printer understand what you have typed on your computer. The same goes for manufacturing comprising a network of machines and CAD / CAM applications.
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