Organizations make big investments in Additive Manufacturing. AM machines, new materials, experts in AM processes, testing, analysis, and simulation – no expense is spared. These costs feel justified in the light of the benefits that AM can bring – parts that can be printed-to-order, new lightweight components with previously unachievable shapes, or reduced manufacturing lead times.

But, there is one aspect we might be forgetting. Will anyone think of the data? Specifically, are we investing enough into managing the complex data created from our AM projects and, if we do, are we thinking about it early enough?

It’s an important consideration, as the success of Additive Manufacturing is more dependent on the exact process parameters than most other, more traditional, manufacturing methods.

To really understand and optimize processes, we need to collect and analyze data from a variety of sources:

  • An AM machine operator recording machine parameters during a build
  • A materials engineer recording the composition and properties of the powder
  • A test technician recording the properties of the resulting part
  • A simulation expert generating modeled properties for several scenarios

In a lot of cases, this data is recorded and kept within their respective departments. But, if our data is kept in silos, we put ourselves at a disadvantage. There is simple efficiency: what if our test technician needs quick access to powder property data when the materials engineer isn’t available? Then there is the fact that it is often useful to see the data in the right context: can the technician see which batch of powder was used for the particular test they are analyzing?

As well as problems with accessibility, unmanaged data makes it hard to achieve full traceability, or a “digital thread”, through our AM process. For example, could the simulation expert seek to validate a model against experiment trace everything back to the composition of the powder that was used?

So, how do we solve this?

Well, first, it’s important we find a system that can capture every piece of data we generate and make it readily available to anyone who should be authorized to see it. Second, we need to make sure we have complete confidence in this system, which we get if we have full traceability – i.e., related data is automatically linked so that we can follow analysis results back to their source. To achieve these two things, we need a database system with good AM-specific data structures, so that we know what data to capture and how to link it.

Finally, with all our data in one, centralized location, we’ll want to make the most of this by having tools that can give it context. For example, having ways to visualize and understand the relationships between our material properties and process parameters, or graphs that would allow us to visually compare data from our experiments and simulation.

GRANTA MI:Additive Manufacturing is one approach to this problem. It was developed from the experience gained in our involvement with several leading AM projects and is a direct answer to all of the questions that we posed above.

If you want to find out more about how you can use GRANTA MI to manage your Additive Manufacturing process data, visit our website.

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