Support Structure Generation

May 15, 2017


Tech Brief: Support Structure Generation

What is it?

exaSIM™ is a predictive simulation tool that enables Additive Manufacturing (AM) machine operators to rapidly optimize their support structures for AM fabrication to minimize the occurrence of blade crashes and support structure failure. In short, exaSIM saves time and money by reducing build failures when producing parts.

What is the hurdle?

Figure 1: P3 Corner Fitting: (Left) Par t p roduced using AM. (Right) Original die cast component (courtesy Tim Gornet, University of Louisville).

Figure 1: P3 Corner Fitting: (Left) Part produced using AM. (Right) Original die cast component (courtesy Tim Gornet, University of Louisville).

Metal powder bed AM processes such as metal laser sintering require support structures to act as a fixture to anchor the part to a baseplate during part fabrication. In an ideal scenario the support density should be as low as possible so that less material is consumed and supports can be easily removed. However, if the support density is too low, supports can fail due to the intense strain resulting from thermal stress accumulation in the part. exaSIM is the only support generation software that uses predicted residual stress accumulation as a criteria for support generation. This approach dramatically reduces a prominent cause of lost productivity, recoater damage, user time and material wasted due to build failure.

This Tech Brief describes the difficulty an experienced AM machine operator faced when producing an aerospace component that was originally die cast, but was prone to failure and had a very long lead time. The part was redesigned to be topology optimized, without taking into account AM process optimization. During the project the operator did not have access to exaSIM, which caused them to run multiple iterations to achieve the appropriate support structure to anchor the part to the baseplate without breaking the supports. The operator later gained access to exaSIM and ran a simulation to compare their final version of supports to those that were automatically generated by exaSIM.

How could simulation have been used to clear the hurdle?

Figure 3: exaSIM support failure prediction for geometry based supports. Grey indicates possible support failure locations and red indicates probable support failure locations.

Figure 3: exaSIM support failure prediction for geometry based supports. Grey indicates possible support failure locations and red indicates probable support failure locations.

The first support structure was strictly a “geometry based” support structure generated using a 3rd party support generation tool. For the second iteration, six solid anchors were added between the part and the baseplate (shown as dark blue squares in Figure 2) to supplement the geometry based supports. Both support structure methods failed. If the user had access to exaSIM at that time, he could have seen that geometry based support structures of this density would fail for this part. Figure 3 shows the results of an exaSIM support failure prediction for this part for iteration 1. The predicted failure locations match the experimental results very well.

After iterations 1&2, the machine operator continued to reinforce the support structure using iterations of building, failing, reinforcing the support structure and building again –eventually arriving at the support structure shown in Figure 4.

Figure 4: (Left) Final support structure with solid supports shown as dark blue boxes and lower density supports shown as light blue. (Right) Baseplate after removal of supports showing support structure remnants.

Figure 4: (Left) Final support structure with solid supports shown as dark blue boxes and lower density supports shown as light blue. (Right) Baseplate after removal of supports showing support structure remnants.

Figure 5: exaSIM auto-generated support structure.

Figure 5: exaSIM auto-generated support structure.

This was a time consuming and expensive process. exaSIM generates two types of support structures each time a simulation is run. Both the thick wall support and thin wall support types showed a high support density needed in regions where the user eventually added fully dense support structures (see Figure 5 & Figure 6 compared to Figure 4). An exaSIM user, once armed with this information, can either choose to use the auto‐generated support structure STL files as input into the AM machine or use these residual‐stress‐informed support structures as a guide to modify the layout of geometry based support structures generated using 3rd party software.

Figure 6: (Left) exaSIM thick wall support. (Right) exaSIM thin wall support.

Figure 6: (Left) exaSIM thick wall support. (Right) exaSIM thin wall support.

Did exaSIM auto‐generate the appropriate support structure?

In the words of the operator: “[exaSIM] simulates very much what we saw when we tried to build this part … We went through about 4 support structure iterations and ended up with solid supports on the one end and less on the other. Your simulation really shows this as you can tell how the end where we ended up with solid support has very tightly spaced supports ….”

Conclusion?

exaSIM effectively generates supports based upon residual stress predictions. Had exaSIM been used for the P3 corner fitting project, there were at least three wasted builds that could have been prevented. This would have led to ~75% reduction in schedule and cost to successfully fabricate this component. Based upon his experience with this and subsequent projects, this machine operator has grown to trust exaSIM’s supports and reports that he directly uses exaSIM supports in day‐to‐day operations with a 90% first‐time success rate. exaSIM blade crash detection, support failure prediction, and automatic support generation features enable AM machine operators to design supports which lower costs, reduce build failures and improve production schedules.

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