1794 Olympic Parkway, Suite #110
Park City, Utah, 84098
Contact: Dr. Chong Teng
Phone: (435) 602-4033
E-Mail: chong.teng [at] 3dsim.com (chong[dot]teng[at]3dsim[dot]com)
Project Title: Predictive Modeling Tools for Metal-Based Additive Manufacturing: A Composable Simulation Model for Metal Powder-bed Fusion Additive Manufacturing Processes
NIST Award(s): 2016-NIST-SBIR-02 (Award Number: 70NANB16H191)
Technology Developed: 3DSIM has developed a set of user-friendly, cloud-delivered, simulation tools to help accelerate production and innovation in metal Additive Manufacturing (AM), specifically Laser Powder Bed Fusion (LPBF). Metal additive manufacturing processes, such as LPBF, rely heavily on trial-and-error to obtain desired material properties, part dimensional accuracy, and build success. These trials cause significant wastage of material and machine hours as optimum build parameters are developed and tested.
Unique simulation tools have been developed with a level of granularity that allows for prediction of microstructure and part performance for a given set of process parameters. Composability and scaling are key aspects of the simulation architecture allowing simulation data to be easily transported between various solvers (thermal, mechanics, microstructure, etc.) with clear application boundaries and provisions for user defined outputs. 3DSIM’s periodicity-based thermal simulation tool has been equipped with the capability to provide real-time predictions of thermal feedback that different types of sensors would measure within their Field of View (FOV). This predictive capability has already been used by 3DSIM partners to help them select the appropriate sensor optics needed to measure thermal phenomena of interest for new types of machines on which they are installing in-situ monitoring equipment. 3DSIM’s mechanics solver is designed to simulate the layer by layer building process for a user specified LPBF process and it helps to ease the qualification process in AM applications by offering many features such as residual stress and distortion prediction for parts & supports, support removal simulation, distortion compensation, blade crash detection, etc.
To achieve scalability, 3DSIM employs a microservice architecture that is orchestrated using Docker containers inside of multiple Kubernetes clusters. Kubernetes is an orchestration framework developed by Google to handle very large deployments. Kubernetes allows 3DSIM to have redundant services running for all components, to scale up and down AWS EC2 instances based on demand, and to run batch simulation jobs on compute optimized hardware. The other key component of Kubernetes is that it is open sourced and has no external dependencies, which enables 3DSIM to run the simulation platform in AWS GovCloud. By running in AWS GovCloud, 3DSIM can offer its customers a higher level of security and data compliance.
Key Words: metal additive manufacturing, residual stress, distortion, supports design, build failure prediction, composable model, microstructure, metal powder bed fusion, 3D printing simulation, selective laser melting, thermal modeling
Uses of Technology/Products/Service: The exaSIMTM software tool empowers AM users to reduce trial and error iterations through prediction of part distortion before and after support removal, rapid residual stress estimation, and automatic generation of support structures and distortion compensated geometries. exaSIM utilizes advanced computational and composable solvers to predict residual stress and distortion in a layer-by-layer fashion.
The FLEXTM Software Tool empowers AM researchers, machine manufacturers, material scientists, designers and machine users to explore the complex cause and effect relationships involved in metal AM. FLEX enables direct prediction of thermal history, meltpool shape, material phase/state/microstructure transformations, and defect mechanisms based upon an accurate representation of the machine, material and scan strategies. FLEX and exaSIM are the only software tools capable of predicting phenomena at the meltpool-scale everywhere within a full-sized part.
Our software output includes color maps for the user for viewing thermal history, distortion trends, final residual stress, blade crash detection, and the maximum stress of components throughout the build as well as STL files for multiple types of supports and for distortion compensation. These outputs enable users to select the orientation, scan strategy, and process parameters which best meet their part design intent.
3DSIM’s predictive tools provide unprecedented insight into the complex physics-based phenomena associated with metal additive manufacturing. This insight speeds part qualification, reduces build failures, minimizes out-of-compliance parts, and saves time and money over traditional, iterative trial-and-error qualification methodologies.
Benefit to Company: NIST Phase II funding supported the development and maturation of composable algorithms and solvers to predict residual stresses and distortion. This funding helped us greatly accelerate the development and implementation of exaSIM and FLEX on a scalable cloud-based infrastructure. The cloud infrastructure developed and matured during the Phase II project offers the capability to run multiple simulations simultaneously to optimize various AM process parameters of interest and to pass information from the thermal analysis into a microstructure prediction tool. Moreover, it matures both 3DSIM’s thermal and mechanics solvers by adding more composable modules into the solvers such as distortion compensation, blade crash predictions, support failure prediction, and more.
Technology’s Impact on Company’s Growth: Strategic
How Product was Commercialized: The exaSIM Roadmap consisted of implementation of our advanced composable solvers (i.e., thermal and mechanics) from Q3 2016 through Q3 2017 in a Beta program. Here beta users tested the software and composable architecture while 3DSIM continued to implement new features and user functionality based on user interactions and priorities. The use of a beta program provided critical development, maturation, and increased functionality of the exaSIM product based on users who were early adopters of this simulation capability. Commercial sales of exaSIM are anticipated to increase significantly during Q4 2017, following completion of the beta program. Currently, the primary delivery mechanism for the commercial use of exaSIM is annual license subscriptions on a per instance basis. Future delivery mechanisms include a desktop version.
Past R&D and/or Sales from this Project: $1M
Estimated Future Annual R&D and/or Sales from this Project: $ 5-25M