Learning Objectives
By the end of this section, students will be able to:
- Describe the use cases of simulation and their impact on the digital thread.
- Differentiate between part simulation, build simulation, and other simulation types.
- Understand the importance and several means of build monitoring.
Part simulation is a part of the additive manufacturing workflow that has been becoming increasingly more prevalent. Visualizing and analyzing the structural properties of a part is an integral step in producing high-quality, effective components with the hope to reduce iterations and failed builds. We discussed simulation approaches in earlier chapters and the aim of this section is to tie in the technical approaches we’ve covered with the digital thread structure. There are two main facets of simulation that will be considered for this context including part simulation and build/process simulation. Ultimately, these two approaches are heavily reliant on one another as well as the full context of the printing of a part.
Part simulation
Part simulation consists of evaluating a part’s mechanical performance when subjected to a variety of different conditions. The simulated introduction of different physical loads, temperatures, deformations, etc can help identify the structural integrity of the various geometries that make up a part. These processes are incredibly useful in finalizing the design of a part that will be efficient in terms of material usage (and therefore cost) as well as mechanically sound enough to withstand its intended use. An additional aspect of part simulation is design optimization. Many simulation softwares geared towards additive manufacturing include the capability to optimize a part’s design for 3D printing. Design optimized parts take advantage of unique capabilities of 3D printing, utilizing geometries that would not be possible with traditional manufacturing. The two main types of part optimization we will go over are lattice creation and generative design. Both of these design optimization techniques result in parts that require less material to print, while maintaining the same physical properties of the original design.
Lattice creation
Lattice structures are design configurations based on a repeated unit cell which can be a hexagonal, honeycomb, or other geometric pattern. Integrating lattice structures into a part’s design can help reduce the part’s mass, and therefore weight without compromising the structural integrity of the design. Many software programs are capable of automatically generating them into an existing part. Companies like Altair, Autodesk, and nTopology provide software that automatically integrate optimized lattice structures into your part designs, with options to change parameters such as the unit cell geometry or lattice density.
Generative design
The second type of part optimization, generative design, achieves similar results to lattice structures in that it helps reduce a part’s weight without compromising strength. The generative design process is iterative, meaning that the simulation tool will attempt to meet a set of defined constraints through many different design options. The generative design software will identify the most integral geometries of a part and keep them as intact as possible, while removing any unnecessary mass that serves little to no purpose in terms of structural strength. Also similar to lattice structures, generative designs are unique to additive manufacturing and would be impossible to produce using traditional manufacturing methods.
Build Simulation
Build simulation is another part of the additive manufacturing workflow and is used to visualize and obtain insight of the 3D printing process of a part. Build simulations can be used to predict the layer-by-layer results of a 3D printing process under specified machine parameters and can help ensure that 3D printing jobs will not fail. Mitigating possible print failures is a key aspect of maintaining the efficiency and cost effectiveness of an additive manufacturing operation.
Process optimization: Simulating part builds helps identify all of the possible reasons for print failure, and will allow you to ensure that parts are printed at the best possible quality. Process simulation software will allow you to set the different parameters for the 3D printing process, and visualize how they will affect the final printed part.
Build speed: Nearly all AM build simulation software will indicate the time in which it takes the specified printer to produce a part. This information is valuable not only for identifying the turnaround time on individual parts, but also for ensuring efficient machine utilization. Additionally, the mechanical properties of printed parts is affected by the orientation in which it was printed. Different orientations can result in different print times, as an orientation that requires more support will generally take longer than a print that does not. Build speed is a key element to track in an additive manufacturing production line, as it plays a notable role in optimizing machine uptime and ensuring that production is as efficient as possible.
Part optimization: The insights gained from process simulation can also be used to identify important mechanical properties of the part to be printed. Part deformation is one of the key factors you are able to simulate, and provides an accurate representation of how a 3D printed part will deform after being printed. Additionally, the temperature gradient of a printed part can be simulated to help identify areas where mechanical properties may be affected. Since this simulation can be quicker than the full deformation simulation, it is useful for locating places where deformation may occur.
Support Structures: Support structures, generally, are needed when a part has overhanging features that are not supported by anything below them. These structures are usually printed at different settings (density, infill, etc) than the actual part, and are constructed so that they are easily removed from the final printed part. Most build simulation software will automatically generate supports based on the overhanging features of the part. The need for supports also varies from printer to printer, with some machines being capable of printing larger degree overhangs without any support structures. It is important to determine your printer’s capabilities before including support structures in your prints. This can be done with a number of “overhang tests” that can be found online. While including support structures will increase a part’s build time, and results in additional post-processing steps, it may be necessary to ensure that a part is properly printed.
| Company | Relation to Digital Thread | Application or Use Case |
|---|---|---|
| Dassault Systemes | Dassault Systeme’s SIMULIA Portfolio offers software that can simulate structural mechanics, computational fluid dynamics, and electromagnetic fields. | Eviation Aircraft set out to create a nine-passenger, 100% electric-powered aircraft that could reach a cruising speed of 280 miles per hour. Utilizing Dassault Systemes’s software, Eviation was able to simulate the aerodynamic behavior of their design, and identify the dynamic and static loads that would occur during flight. The cloud capabilities of the software allowed the easy sharing of ideas and collaboration across different disciplines. |
| nTopology | nToplogy’s platform provides the tools to effectively lightweight part design, as well as automatically optimize the topology of the part’s geometry. | Cobra Aero, a US-based UAV designer, wanted to find a way to design and manufacture lighter engines in order to decrease drag on their UAVs and increase flying efficiency. Utilizing the nTop platform, Cobra Aero was able to introduce an internal lattice structure to their air-cooled engine cylinder, replacing the external fin structure which they used previously. In testing, the new design was more efficient at cooling in every possible case. |
Build Monitoring
As we continue to build out a model of the digital thread that for the AM workflow the area that arguably has the most data (in terms of pure memory required) is monitoring of each layer in the build. Any 3D printing process is built on the concept of consolidating layers upon other layers to build a fully dense part. Most printing processes have a layer thickness somewhere in the range of 40-150 microns which translates to potentially thousands of build layers per part. This ultimately becomes the single biggest area for variability for a 3D Printed part. From a 3D printing digital thread perspective there are a few concepts that we will cover.
The first area is simply being able to document process details about each build during the process of construction as well as compiling data from each individual layer. Depending on the printer modality there are different levels of detail that can and should be monitored during the process. In nearly all printers some elements of the machine operation should be documented (temperature, layer thickness, inert gas flow, energy inputs). For most machines, the operator has some levers to pull on this when they start the build. Lower cost printers generally do not have much in the way of sensors that can output to an accessible software data stream. This leaves the user in a trusting position to make sure that the part is constructed in a way that meets the requirements. As the industry targets more applications in the production environment, the demand from customers is that machine manufacturers allow access to a variety of sensors within the machines to enable full build visibility.
Some examples of build monitoring platforms are shown in the following table. EOS and Renishaw enable build monitoring data through their DMLS Platforms. 3D Systems Examines the Build Process of Polymer Laser Sintering
| Company | Relation to Digital Thread | Application or Use Case |
|---|---|---|
| EOS | EOSTATE is a modular solution for end-to-end build monitoring of all production and quality-relevant data in industrial 3D printing. | MTU Aero Engines has been using EOSTATE for process development and quality assurance in the additive series production of borescope bosses for Airbus A320neo engines. This has allowed them to completely eliminate any downstream quality assurance procedures. |
| Renishaw | Renishaw’s InifniAM enables near real-time insight into ongoing builds and allows for the in depth analyses of completed builds. | IMR, a Dublin-based manufacturing company that has been working with a variety of clients to explore the capabilities of 3D printing medical devices. Spinal implants was a device that was identified that could benefit from being additively manufactured. Metal AM would allow for the integration of lattice structures into the implant’s design. Using Renishaw’s RenAM 500M metal 3D printer, IMR was able to confirm that their AM spinal implants had mechanical properties greater than those of their traditionally manufactured counterparts. |
| 3D Systems | 3D Systems offers DMP Monitoring, a real-time process monitoring tool that allows the user to see, analyze, and fine-tune their metal additive manufacturing processes. | Node-Audio manufactures high-fidelity speakers with a distinct cabinet design that distinguishes itself from the rest of the hi-fi speakers on the market. Using a 3D Systems SLS printer and 3D audio simulation software, Node-Audio was able to design speakers that produced sound similar to that of a live experience. |
The second area that is important to consider due to the layer upon layer process is that any error in a single layer could potentially be catastrophic to the build process or render the part not usable. So while we have been talking about many aspects of the digital thread being historic in nature (recording data that has occurred) there is another forward-looking application: Manufacturers can use information during the build process to prevent or solve for error-prone directions in building a part. While this is an opportunity, it has a significant data load. Even a simple thermal scan of each layer of a build will result in terabytes of data that needs to be stored and analyzed. To do that at scale for multiple machines on every build is extremely challenging. However, there are tools that are being developed to quickly analyze this information use it to solve for issues in the build.
Ultimately, all of this information could be documented and transferred for every part that is generated, allowing manufacturers and product companies to have a before, during, and after set of data for the construction of the component with the hope of providing a robust quality summary.