From estimating material and energy consumption to gauging impacts on supply chains, here’s how simulation applies to 3D printing.
Most engineers understand why simulation is crucial in additive manufacturing (AM), even if only in principle. Whether you’re talking about thermal simulations, mechanical simulations or process simulations, applying these methods and technologies to 3D printing often means the difference between success and failure. Nevertheless, there’s a big difference between understanding how simulation applies to AM in general and knowing what practical applications actually look like.
So, here are four diverse examples of using simulation in additive manufacturing.
1) Simulating energy and material consumption in binder jetting
One of the first questions engineers ask when presented with a new technology is, “How much is this going to cost?” It’s undoubtedly an important question, but it’s not always the easiest one to answer. For AM, what it really boils down to is the amount of energy and material consumed during the 3D printing process. Xin Xu, a graduate student in the department of mechanical engineering at McGill University, tackled this question head on in a master’s thesis entitled An energy consumption and material efficiency simulation method for additive manufacturing.
The simulation considers part geometry, print orientation, layer thickness and various other process parameters to generate a model of energy and material consumption for the binder jetting process. Created using MATLAB, the simulation was validated experimentally based on the following input parameters and process variables:
| Input Parameters | Process Variables |
| Build Volume (mm) | Layer Thickness (µm) |
| Powder Refresh Rate (%) | Drying Time (s) |
| Powder Density (g/cm3) | Printing Saturation (%) |
| Binder Consumption of Flushing (ml/time) | Powder Recycling Ratio (%) |
| Frequency of Flushing (time/layer) | Cleaning Frequency |
| Cleaner Consumption (ml/time) |
Taking this approach, the majority of simulated outputs for material and energy consumption were >90% accurate, demonstrating the usefulness of even relatively simple numerical simulations for AM processes. One important caveat: this approach doesn’t include the energy consumption of post-processing operations or calculations of material waste.
2) Modeling heat transfer, fluid flow and solidification in L-PBF
A much more complex example of simulation in 3D printing comes from an engineer and a materials scientist at Ohio State University and Oak Ridge National Laboratory, respectively. In their paper, published in the journal Additive Manufacturing, Y.S. Lee and W. Zhang discuss their development of a computational framework with mesoscale resolution for laser powder bed fusion (L-PBF) of Inconel 718.
The framework combines a simple powder packing model based on a discrete element method and a 3D transient heat and fluid flow simulation (referred to in the paper as “the molten pool model”). The idea is to use this framework to capture the complex interactions between the laser beam and the powder particles during the L-PBF process. By calculating solidification parameters using thermal gradients and cooling rate data, the researchers were able to assess solidification morphology and grain size using previously established models.
3) High-fidelity modelling of thermal stress in additive manufacturing
Another example of process simulation comes from researchers at the National University of Singapore (NUS). In a paper published in Materials & Design, Fan Chen and Wentao Yan explain how they combined a finite element method (FEM) with computational fluid dynamics (CFD) to create an improved model for predicting thermal stress. While their simulations focused on single tracks, multiple tracks and multiple layers of electron beam melting (EBM), the researchers claim that their approach is applicable to a variety of fusion-based AM processes, including selective laser melting (SLM), directed energy deposition (DED) and wire arc additive manufacturing (WAAM).
Using their improved model, the researchers were able to identify the process parameters with the greatest impact on mechanical failures correlating with high-stress regions, such as cracking and porosity.
4) The impact of additive manufacturing on supply chain design
Going beyond the 3D printing process itself, simulation can also be a tool for understanding how implementing additive manufacturing technology can affect supply chains. A team of engineers from the University of Campania in Italy did just that by creating a discrete event simulation model using Excel to compare AM with traditional manufacturing methods for aerospace spare parts.
Their model of a traditional manufacturing supply chain included one supplier, one OEM, two regional distributors and eight local distributors, while the AM supply chain was modeled in two different ways:
- A centralized model with one supplier, two OEMs and eight local distributors
- A decentralized model with one supplier and eight OEMs
The researchers evaluated these three different models on 11 different service level scenarios, ranging from 65% to 95%, and included variables for final customer demand, lead times and travel distances based on industry data attained via literature review. Each model was evaluated in terms of supply chain lead times and customer satisfaction. What they found was that “regardless of the service level, additive manufacturing reports a better result.”
However, it’s worth noting that, “the significant number of machines affects the KPIs linked to the production stage and such aspect could limit the economic feasibility of AM technology.” In other words, if you have enough 3D printers, you can construct a decentralized supply chain that performs better in terms of holding stock, supply chain costs and customer satisfaction.
These are often touted as benefits of AM, but this simulation actually provides evidence to back them up.
