The goal of AdditiveLab software is to provide an open platform that allows solving all kinds of complex problems. For example, one challenge posed to us by a dental implant manufacturer was to optimize AM construction configurations to reduce material and subsequent process times.
More specifically, this manufacturer considered that its implants were oversized and could be optimized. To carry it out, we had to take into account the following: one of the unique competences of the manufacturer was extremely short delivery times; in some cases, delivery times had to be met within 24 hours. In order to ensure these timelines, the manufacturer developed a construction preparation workflow that used extremely strong conical support structures that ensured the fail-free production of dental implants. However, it was important to maintain the current workflow and that any optimization that could be done did not interfere with it.
The way we approached this project was by determining which supports were exposed to a greater load during the additive manufacturing process. During the process, the residual stresses deform the part-support configuration that must be counteracted by the supports, therefore, what we needed to understand in the first place was which supports were exposed to a greater load and which to a lower one, being able to have potential for volume reduction.
We carried out simulations of the process and immediately saw that the different cones were exposed to different loads (reaction forces) as shown in the images below. By conducting additional tests, we concluded that we could use reaction forces as a measure to optimize cone structures.
After understanding the effect of reaction forces on the cones during the construction process, we developed an algorithm that allowed us to identify each cone (this was necessary, since the manufacturer did not want to change its workflow), and iteratively optimize the diameter of the cone based on the reaction forces.
The difference between the support diameters of the cones is shown with the optimized design that needs 40% less material without compromising structural integrity during the construction process.
Now comes the interesting part; with this optimization, we were able to reduce the material volume of the support structures by 40%, which also reduced the construction time.
This case study is a good example where you can see how we use AdditiveLab’s Python API to program scripts that transform complex optimization into a simple solution, which greatly improves the performance of the additive manufacturing process.
Do you have similar challenges that you would like to propose to us with optimization based on simulation results? Check out our Additive Manufacturing Simulation e-book to see everything we can do, or get in touch with us.