Our goal with AdditiveLab software is to provide an open platform to enable solving all sorts of complex problems. One complex problem that we were requested to solve was the optimization of AM build configurations to reduce material and subsequent process times.
This was the challenge we were approached with by a dental implant manufacturer that felt like their implants were over-supported and could be optimized. Some background information: one of the unique competencies of the manufacturer was extremely short delivery times; in some cases, lead times were in the range of 24 hours. To ensure short delivery times, the manufacturer developed a build preparation workflow that utilized extremely sturdy cone support structures that ensured the failure-free production of the dental implants. It was important to the manufacturer that the current workflow was maintained, and whatever optimization could be done, should not interfere with it.
The figure above shows a representative part support configuration of a dental implant that was generated with an optimized workflow that utilizes sturdy support structures in order to ensure failure-free manufacturing. The way we approach this project was to determine which supports are exposed to higher loading during the additive manufacturing process. During the process, residual stresses deform the part-support configuration which must be counteracted by the supports, thus, what we needed to understand first was which supports are exposed to higher loading and which ones are exposed to lesser loading and may have potential for volume reduction. We ran process simulations and immediately saw that different cones were exposed to different loads (reaction forces) as illustrated in the images below and concluded after additional tests that we can use the reaction forces as a measure to optimize the cone structures.
The figures above show the simulation results of the building process. The color-coding shows the reaction forces at the bottom of the support structure with red indicating higher reaction forces.
After understanding the effect of reaction forces on the cones during the building process, we developed an algorithm that allowed us to identify each cone (this was necessary since the manufacturer did not want to change their workflow), and iteratively optimize the cone diameter based on the reaction forces. One example result of the optimization with this algorithm is illustrated in the figures below:
The figures above show the dental design with the originally designed support structures (left) and the optimized structures (right).
The figures above show the bottom view of the dental design with the originally designed support structures (left) and the optimized structures (right). The difference between the cone support diameters is illustrated with the optimized design needing 40% less material without compromising the structural integrity during the building process.
Now here is the interesting part; with this optimization, we were able to reduce the material volume of the support structures by 40% which will also reduce the building time.
This project illustrates a nice real case example where we used the AdditiveLab Python API to program scripts that allowed a fairly complex optimization, which led to better performance of the additive manufacturing process.
Do you have similar challenges you would like to explore with optimization based on simulation results? Just get in touch with us!
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