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NOV-DEC 2018

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INTECH NOVEMBER/DECEMBER 2018 23 FACTORY AUTOMATION To be successful with this method, users need to build these calibrated strain libraries, and in most cases, they are specific to a machine type or even a specific machine. Although it is an elegant and simple approach (also used in the welding commu- nity), issues with this method will arise as machines are retired, new machines are introduced into pro- duction, and the volumes of data and designs keep increasing over time. The other approach relies on a fully thermo- mechanical solution to the process simulations. Scanning strategies can be used in lumped ther- mal models to predict the thermal profile as the part is being built, layer by layer (or multiple layers togeth er). The thermal profiles then drive the me- chanical simulations for a more accurate predic- tion of the distortions. The main advantage of this method is that the fidelity of the simulation can be controlled. At the lower end, running very accurate simulations in the micro-second level (or lower) can capture the physics behind the manufacturing process down to melt-pool levels, phase change, solidification, and microstructure evolution. These simulations are run on representative cube models (at mm level) and help get to accurately predicting residual stresses, voids, cracks, and so on, factors that will affect the service life of the functional parts. At the higher end of the scale, at the part level, a multiscale approach is used to map the lower- level scales to predict overall part distortions and stresses. The drawback of this method is that a fun- damental understanding of the physics is required to create simulation models. Often, these models are part of the company intellectual competence and as such mature over time. However, as hard- ware vendors bring to market machines with new processes, faster build rates, materials choices, and open frameworks, what works for metal powder bed processes may not apply to them. In a powder bed fabrication process, thermal energy selec- tively fuses regions of a powder bed; in a binder jetting process, a liquid bonding agent is deposited to join the material powder. In a direct energy deposi- tion process, a nozzle that is mounted on a multi-axis arm deposits mol - ten material, and in photo polymerization, liquid photopolymer is selectively cured by light-activated polymerization. While each process family uses a different raw material supply form (i.e., powder, wire feed, liquid resin, ink), each process family manu - factures parts consisting of different material types. For example, powder bed fabrication produces metallic and plastic parts; binder jetting produces metallic, plastic, and ceramic parts; material extru - sion produces plastic and composite. Adding to the complexity is the fact that each process family includes many subprocess types that are differentiated by technical details and pat- ents, such as close or open system, input/output formats, how raw material is included, how raw material is selectively heated, different types and sequences of heating and cooling sources, and how machine manufacture and environmental condi- tions are controlled. Under powder bed fabrication alone, there are a number of subprocess types, e.g., selective laser sintering (SLS), selective laser melt- ing (SLM), electron beam melting (EBM), and di- rect metal laser sintering (DMLS). Under directed energy deposition, there are laser cladding, direct FAST FORWARD l Most digital tools address some of the critical additive production challenges but rely on others to complete the entire process—causing loss in production. l An organization needs a model-based approach that integrates design, materials, manufacturing, and production to successfully evolve additive from the lab to a production environment. l An unbiased public benchmark is crucial to building trust in the additive community. Figure 1. Thermo-mechanical and eigenstrain approaches for process simulation

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