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Using Materials Data for Faster, Cheaper, More Repeatable Additive Manufacturing //
Using Materials Data for Faster, Cheaper, More Repeatable
Additive Manufacturing
This paper discusses key hurdles to be cleared for enabling
effective AM projects:
1) Traceability and capture of data across AM value chain
2) Efficient data analytics to enable the technical decision
making process
3) Integration between AM technology and simulation
solutions
We will spotlight how these challenges are being overcome by
using best practices and the right supporting tools for performing
AM simulation and data management. Additionally, we will
illustrate how you can get AM results faster than ever before.
As Additive Manufacturing (AM) technology evolves, better understanding of the data generated
during AM projects becomes vital to realizing its potential. This applies both to empirical data, from
production and testing, and to data generated by the various simulation packages that help users
understand process parameters and reduce trial and error. Simulation results can help to optimize
and calibrate your process. Testing validates simulation and ensures that final parts meet design
requirements. You need to capture and use data from both to speed your AM development process
and certify products at lower cost. See the diagram for an overview of the process.
WHITE PAPER
Figure 1: AM Value Chain
/ Business benefit
The resultant traceability through data capture
can typically result in cost savings of upward
of 25%, with double-digit repeatability
improvements achievable by using data
analytics. Finally, by allowing the free flow of
data through integrated design and simulation
tools, time to market for AM projects can be
decreased by up to 20%.
