Additive Manufacturing (AM), has taken a central role in driving the transformation of American industrial regeneration and is now a viable alternative for conventional manufacturing processes. While the potential benefits of AM are vast and varied, such as operational sustainability and cost-effectiveness in the long term, there remain significant obstacles to overcome scaling the technology to mass industrial adoption. Current practices in AM often involve iterative trial-and-error methods, leading to significant material waste, production delays, and suboptimal part quality.
AlphaSTAR offers predictive digital twin technology that can provide up front assessment of build quality and as-built mechanical behavior based on material properties, printer toolpath, and print parameters. Utilizing an Integrated Computational and Materials Engineering (ICME) approach, we combine material science, virtual manufacturing, and virtual testing in a cohesive workflow that seeks to enhance overall quality and repeatability of AM processes. This type of reliable and predictive modeling can both increase confidence in AM and inform decision making from part designs and evaluation of effective toolpath strategies to fine tuning variabilities of hardware systems.
Utilize a detailed material model that corresponds to actual test data
Address thermoplastics, thermosets, and powder metal
Incorporate the effects of defects, manufacturing anomalies, and environmental conditions
Consider uncertainty and scatter
Model AM process parameters
Utilize weighting methodologies and sensitivity analysis
Identify material and process parameters that impact the build
Optimize the build to reduce defects
Eliminate fabrication trial and error
Reduce scrap rate, resulting in cost savings
Supports metal powder and polymer material systems – validated material databases for Metals/Polymers/Ceramics
Predicts mechanical properties with voids and anomalies at room and elevated temperatures
Assesses both material and process parameter sensitivities to be optimized to improve manufacturing process
In-service qualification of printed part, effect of voids and defects on in-service life, strength and durability
Predicts manufacturing anomalies (e.g. residual stress, warpage, heat affected zone, delamination, etc.)
Visualizes/Assesses printer path quality and highlights problematic bald spots, 2D/3D voids visualization
Creep diffusion model to predict local anomalies, voids and local surface roughness
Predict transient temperature & material phase / states – zeroth order model for thermal analysis (ZOM)
Predict damage/failure type, location and percentage of contribution of each failure type to fracture
Reduces scrap rate of materials in additive manufacturing, trial and error in manufacturing process
Characterizes AM materials in coordination with an ICME framework
Integrates 3D printing process simulation with assessment of properties, design evaluation and structural analysis
Provides improvements for process parameters and robust design optimization in order to minimize defects
Addresses manufacturing constraints to minimize processing defects to improve performance
Validates developed/enhanced models for AS-IS performance of 3DP parts
Download the latest case study.
Download the latest datasheets.
GENOA 3DP is an additive manufacturing design tool and software suite that simulates the 3D printing process to accurately determine voids, delamination, manufacturing anomalies and other irregularities and inefficiencies from micro to macro scales.
2601 Main Street #660 Irvine, CA 92614
(562) 961 - 7827
info@alphastarcorp.com
@2022 - 2023 AlphaSTAR. All rights reserved. Trademarks used herein are trademark or registered tradmarks of AlphaSTAR. All other names and brands are registered trademarks of their respective companies.
