NOV-DEC 2018

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24 INTECH NOVEMBER/DECEMBER 2018 WWW.ISA.ORG FACTORY AUTOMATION energy deposition (DED), laser metal deposition (LMD), laser engineered net shape (LENS), and laser or electron beam wire deposition. Furthermore, with many of these burgeoning processes, it is premature to predict if a single process or a mul - titude of processes will be in a compa- ny's tool kit. All indications point to the latter, where companies have a variety of machines at their disposal as they plan their additive road map. Herein lies the issue that could inhibit a true end-to-end digital thread. General framework Do I have to acquire another special- ized simulation software, develop another set of techniques, maintain libraries for individual parts and ma- chines? What if the process param- eters change? What if a set tool path for polymer extrusion does not get me the right part strength? Do I go back to the drawing board? One way to ad dress this concern is to be rigorous in the man- agement of the AM simulation chain: machines, processes, and materials. If not planned with care, chances for drowning in the data lake are high. There is a better way to address this issue, though, by providing researchers and analysts with a general-purpose simulation framework designed from the ground up to handle any machine, any process, and any material. In other words, a framework agnostic of the process, but driven at a deeper level by the science of energy and mate- rial handling. Energy decides how the ma terial evolves during the process. For metal powder, the laser sources fuse the powder, and as it solidifies, the as-built material properties decide the part strength and quality. For polymer extrusion, the energy source to fuse the pellets is the extrusion process. Each process inputs this energy as a series of events that are distributed in space and time, predetermined as part of the manufacturing process. The other issue is material han- dling. While material handling is ma- chine specific, the simulation frame- work only needs to know when and how the material is handled. Again, these can be treated as distributed events in space and time. For powder bed, it is the roller laying down a new layer of powder. For multi-jet, it is the ink that gets deposited before the lamp provides the fusion energy. As before with energy, these events are determined a priori by the planning algorithms (figure 2). Taking these independent events as inputs and automatically solving for dependent events, such as powder melting, liquid metal solidification, cooling surface evolution, temperature history, build stresses, and distortions, the simulation framework can proceed with the computations and predict the outcome of a certain manufacturing process or material. We also need to mesh the part and the underlying sup- port structures in a regular finite ele- ment sense—both at a geometry and voxel level—while intersecting it auto- matically with collected events (energy and material). This technique makes the simula- tions more efficient, as it can handle multiple layers of manufacturing events while still using a regular mesh. This open simulation framework not only addresses the multitude of exist- ing processes but can help accelerate the maturation of new processes: a new thermal mechanical physical pro- cess, a new chemical agent application method, a new UV light polymeriza- tion process, or even a new machine that uses a number of different heating devices simultaneously or sequentially to preheat and fuse the parts! Figure 2. Interface for virtual build planning supporting end-to-end digitalization of additive manufacturing while connecting additive to the rest of the organization's industry processes using the 3DEXPERIENCE platform.

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