Intuitive Build Processor & Slice Viewer
Contact for Ansys Additive Manufacturing
AM Build Preparation | Maximize Efficiency
Control Your Variables and Minimize Build Failures
Three unique software tools from Ansys enable engineers to control some of the most challenging variables within metal AM: build orientation, distortion and deposition paths. In this video we discuss the first tool in the set, preparing you for success out-of-the-gate.
- Design Validation
- Material Analysis
- Process Simulation
- Distortion Compensation
- Build Failure Prediction
- Metal PBF Process Simulation
- Structural and Thermal Analysis
- Topology and Lattice Optimization
- Comprehensive Inlet and Outlet Condition
- Melt Pool, Porosity and Microstructure Predictions
Ansys Additive Suite | Core Benefits & Capabilities
Conventional Manufacturing Techniques Impose Stringent Design Thresholds
Manufacturing Challenge: For manufacturability and assembly constraints, the outdated simulation methods, in which models are investigated one at a time by physically defining their geometry, or dozens at a time by parametrically fluctuating their proportions, are simply not up to the task of exploring a potentially unlimited design space.
Manufacturing Instance: Injection Molding
- For example, manufacturability procedures for molding normally incorporate the need for draft angles to enable ejection of the part, identical wall thickness to minimize warpage, radiused corners to develop plastic flow during molding, and other manual exercises that slow the design process and increase operating expenses.
Manufacturing Solution: Three-dimensional printing eliminates these and other constraints for plastic and metal parts, making it economically viable to develop complex structures that deliver higher performance-to-weight and performance-to-cost proportions than can be reached with standard manufacturing practices.
- The design independence offered by Ansys additive manufacturing enables an infinite array of product models for prospective consideration.
- Take advantage of this freedom and nullify traditional wisdom. Be bold and design products based on completely new paradigms reshaping the industry.
Conventional Manufacturing Overlooks the Inherent Process-Structure-Property Link
Manufacturing Challenge: One of the core challenges of implementing additive manufacturing (AM) practices for the intent of production is the lack of understanding of its fundamental process-structure-property connection. Parts manufactured using AM machineries may be too unpredictable and erratic to meet the strict requirements for many business applications.
Manufacturing Instance: Mission-critical Applications
The lack of geometric accuracy of AM parts is a major barrier preventing its use in mission-critical applications. Quantifying the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning approach is out-of-range for traditional software capabilities.
Manufacturing Solution: Fortunately, a core objective in designing the Ansys Additive Suite software has been to characterize the underlying thermo-physical dynamic forces of the AM process, captured by melt pool signals, and calculating porosity during the build.
- In this, we have succeeded. Through a novel porosity prediction methodology embedded in the software, based on the temperature distribution of the top surface of the melt pool as the AM part is being built. The key word being “as” here. It’s the real-time calculations, be it a trial or live run, that separates Additive Suite from the traditional solutions.
- In addition, advance data analytic and machine learning methods are then employed to further analyze the 2D melt pool images streams to isolate the patterns of melt pool images and its association to porosity.
- Material analysis tool for Additive Manufactured parts
- Porosity prediction
- Microstructure prediction
- Grain morphology
- Material tuning wizards
Precise and timely build failures prediction techniques can cut the expenses of CI build cost by early detection of a designs potential failures with an integrated manufacturing workflow.
- Supervised learning provisions automatically applied to dichotomize the melt pools.
- Real-time monitoring toolkit embedment to detect anomalies in the microstructure.
- Morphological models and base metrics of analogous melt pools are compared.
Building components as a network of unit cells can significantly improve material utilization and performance. Many of our business partners have shared similar challenges of additively manufacturing parts that were not designed to be fabricated layer by layer. Our software is an all-encompassing suite designed to account for, and solve these nuances through multifaceted integration.