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Simulation Improves 3D Print Quality

New simulation and optimization tools (leveraging GPU acceleration) are improving 3D printing performance and predictability.

New simulation and optimization tools (leveraging GPU acceleration) are improving 3D printing performance and predictability.

Designers need simulation to optimally orient parts in 3D printer build trays and to create the best support structures for a given geometry. Image courtesy of Siemens Digital Industries Software.

With companies moving beyond prototyping into production-scale 3D printing use cases, there is growing emphasis on the need to print the part correctly the first time without wholesale reliance on institutional knowledge and free from the costly trial-and-error.

The need to get a part right the first time it’s printed has sparked interest in a new class of simulation software designed for additive manufacturing (AM). These solutions range from tools that optimize build plate layout and support placement to more sophisticated capabilities such as design identification and recalibration in the virtual world to address part deformation before the print runs. To do this, a primary focus must be on robust simulation capabilities.

Simulation can improve performance and save money. “Build failures are extremely common—there could be upwards of 10 failures on production-scale parts, which run into costs of tens of thousands of dollars,” says Erik Denlinger, principal researcher on AM simulation at Autodesk. “The ability to provide insight into if and how a part might fail is extremely valuable.”

Designing for AM requires engineers to take into consideration such factors as residual stress from heating and cooling of each print layer; the placement of support structures and their effect on warping and post-processing; topology optimization; and the effect of print orientation on build time, strength and surface finish.

With 3D print simulation, companies can avoid warping, deformation and other issues that can result in costly waste and failed 3D prints. Leveraging the power of professional engineering workstations, such as the Dell Precision family of desktop and mobile computers, and high-performance NVIDIA RTX GPUs, these tools make it possible to incorporate generative design and topology optimization, as well as simulation of the entire additive workflow.

3D print quality can vary significantly, even when creating the same geometry on the same printer, just by using different support structures or a different part orientation. 3D printer companies are working to address these issues via their own tools and through partnerships with existing simulation software vendors.

Stratasys has a long-standing partnership with MSC Software, part of the Hexagon Group, to collaborate on AM material and process modeling. The company is working with MSC’s e-Xstream Engineering group to create tighter integration between its platforms and Digimat, a multi-scale material and structure modeling platform that can predict behavior for a large mix of composite materials. Simufact Additive, also from Hexagon, focuses on predicting distortion and automated distortion compensation for metal binder jetting sintering technology.

3D Systems, meanwhile, has been developing AM-specific simulation capabilities as part of its 3DXpert AM software. In addition to the ability to position and modify part orientation, optimize structures with lattice, and infill features and analyze design supports, 3Dxpert has simulation capabilities for predicting issues that might result in build failure or printer damage.

The company recently expanded its simulation capabilities with the acquisition of Additive Works. The German company focuses on simulation-based optimization and automation of the AM print preparation and workflow. Additive Works’ software allows a manufacturing engineer to rapidly determine optimum print setup, such as part orientation and support structures, as well as directly adapt the process setup for effective thermal management and distortion compensation, 3D Systems says.

GPU Acceleration

Because AM simulation is often computationally intensive, many software providers are leveraging GPUs to provide faster and more accurate results.

For example, the Digimat FFT solver discussed above takes advantage of NVIDIA CUDA libraries for GPU-accelerated  computations. The third major update of nTopology, for example, introduced GPU acceleration for seamless interactivity. This gives nTop users a performance boost when visualizing workflows that use complex field-driven geometry. Users can preview design changes in real time and regenerate parts with complex geometry in seconds. The company recommends the NVIDIA Quadro™ P2200 GPU or the NVIDIA Quadro RTX™ 5000 GPU for high-performance scenarios.

Dyndrite offers the Dyndrite Additive Toolkit for improving 3D printing workflows. The toolkit directly imports CAD design files and uses the data to drive additive manufacturing processes. The fully native GPU Kernel handles additive-specific computations and is naturally scalable with access to additional GPU nodes, both locally and in the cloud. The company developed the kernel using the NVIDIA Quadro RTX™ 6000. (You can learn more via this recent NVIDIA GTC session.)

The Live Sinter software from Desktop Metal also leverages the GPU to simulate the deformation of parts during sintering. Sintering can cause parts to shrink by as much as 20%, and improperly supported parts risk deformation, cracking and distortion. Live Sinter helps reduce the need for supports and improves adherence to dimensional tolerances. The product runs on an NVIDIA GPU-based multiphysics engine. (The Desktop Metal Live Parts generative design platform also leverages GPU acceleration.)

e-Xstream engineering (as noted above, part of Hexagon’s Manufacturing Intelligence division) also leverages the GPU for additive manufacturing simulation. The latest Digimat software enables businesses to simulate the 3D printing process and calculate the total cost of producing each part including the material use, employee time, energy and required post-processing steps.

Manufacturers can CT scan a part and import the 3D RAW image to build a finite element model of its two-phase microstructure (e.g., carbon fiber-reinforced polymer) in Digimat and model its behavior. By embedding this validated material model in its CAE tools, a design engineer can perform analyses that account for variations within a manufactured part to reduce material use or avoid points of failure.

When refining new manufacturing processes, users can capture information about the part, material, 3D printer or process used and their physical tests as they work using material lifecycle management. The company’s MaterialCenter software captures a traceable, validated database of trusted material properties so that they can be used in the design phase of a product.

Leveraging the GPU, these solutions allow engineers to perform these tasks interactively and generate results in minutes. Benchmarks show the time required to analyze the stiffness of a material is reduced by 98%. This rapid solve time, combined with the introduction of a command line interface, also enables the use of Digimat finite element models within automated cloud-based optimization workflows on high-performance computing platforms.

A Plethora of Simulation Tools

Other emerging simulation tools take a variety of different approaches. Autodesk, for example, offers multi-scale modeling methodologies as part of its Netfabb AM suite and simulation extensions for the Fusion 360 software. The technique breaks the process into two pieces: the small-scale simulation (1x1 mm, for instance) at high resolution at the part and material level, followed by applying those results to the rest of the build volume as a way to circumvent the computational expense,

The Materialise Process Tuner is an online platform the company says will “help manufacturing companies, service bureaus and machine builders speed up the process tuning required for mass-manufacturing 3D printed parts. This allows them to reduce the cost and waste associated with printing hundreds of test samples before finding the optimal process parameters.”

Process Tuner uses automation and simulation to predict sub-optimal prints.

Teton Simulation Software has launched its Smart Slice for Ultimaker Essentials simulation tool for optimizing 3D printer performance. Initially, the product was launched in Ultimaker's Cura Essentials Marketplace.

Smart Slice uses FEA analysis within the slicing environment to automatically provide optimal strengthening settings for a given print, eliminating the need to iteratively test actual prints. The tool validates a print configuration in advance, adjusts the slicing parameters that can influence part strength, and then provides that data so the final print can be completed.

The underlying philosophy for Siemens Digital Industries Software is that simulation tools will play an upfront role in determining how to orient a part in a printer for the least amount of distortion and predict the microstructure and macrostructure behavior of parts to direct material deposit or identify gaps and overheating areas on the build plate. Simulation also can be tapped to predict material properties from the manufacturing process applied or to map out and plan AM factories.

Siemens offers NX Build Optimizer for orienting parts and NX Path Optimizer, technology for powder bed fusion parts that combines physics-based simulation and machine learning. The Simcenter3D simulation portfolio offers various capabilities, including process capabilities for predicting and addressing distortion of metal parts and multi-scale modeling capabilities for predicting failure in advanced materials. Siemens is also partnering with a range of AM printer providers, including EOS and Evolve, to optimize its simulation tools for specific platforms and AM technologies.

EOS and Ansys are collaborating to provide an enhanced, streamlined workflow for developing AM parts. The new workflow teams EOS' metal systems for additive manufacturing with Ansys simulation solutions. This integration will help improve part geometries by predicting and compensating for distortion and other issues to reduce build failure and upgrade manufacturing processes to increase productivity. It will also improve material property selection by forecasting how design changes will affect the microstructure of parts.

While these new simulation tools can improve 3D printing outcomes, the software must be integrated into engineering workflows. Users will need to be educated on the potential benefits, companies will need to make the software more accessible to a range of potential users, and there will need to be a greater integration between the design and manufacturing functions. In addition, the engineers responsible for these processes should be equipped with suitably configured workstations in order to take full advantage of these new software tools. Dell Precision workstations (including both tower and mobile workstations) and professional NVIDIA GPUs allow engineers to optimize the performance of these 3D print simulation tools.  

“Design simulation experts lock down performance in CAD, but with AM, it’s not locked down until you define the steps of manufacturing,” says James Berlin, senior software product manager, manufacturing and alliances, at Stratasys. “There’s work to be done reconciling redistributed responsibilities between someone designing parts and someone manufacturing parts. Simulation needs to bridge that gap and take into account the intent of both areas.”

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