Simulation software giant Ansys has been getting increasingly involved in AM from both a process/workflow angle and a simulation angle. This latter aspect, which focuses on the ability to fine-tune a build process so that it is more repeatable and reliable, has recently been explored via the use of artificial intelligence and Ansys machine learning technology, including Ansys Granta MI.
A free, downloadable White Paper from Ansys (described in an article by article by Scott Wilkins, Lead Product Marketing Manager, and Sak Arumugan, Lead Product Manager) shows how taking a data-driven approach is key to making this happen. Being data-driven requires efficient access to the right data. Even where this is available, real-world AM datasets are typically sparse and noisy, placing constraints on their analysis. It can also be difficult to extract value from increasing volumes of data. Conversely, in some areas, too little data is available. Navigating these challenges to optimize AM materials and processes stretches the analytical capabilities of many teams.
Ansys machine learning offers a solution. It can use existing AM project data to build predictive models that identify key process-property relationships, guide your testing program, and propose optimal processing parameters. Ansys Granta MI manages AM project data, a prerequisite for successful analysis. At the same time, an integrated machine learning algorithm, Alchemite from Intellegens, can extract value from this data, even when it is sparse, noisy, and in high volumes. The result is an out-of-the-box solution for data-driven AM. This new level of insight into AM data enables engineers to control the AM process and optimize material and part performance. They achieve results with a greatly reduced number of experimental test cycles. This accelerates time to market and can support end-to-end total cost savings for AM workflows of ~10%.
The challenges of AM
The key challenges for the large-scale adoption of AM in the industry are speed and reliability. Ansys’ research starts from the premise that overcoming these challenges is made even more difficult because every AM process has its own set of process parameters, including material properties, manufacturing settings, and changing environmental conditions.
The choice of materials alone leads to a large number of variables. The powder chemistry of an alloy can vary widely because of small levels of impurities that can change the composition of every batch. For example, Ansys the study asks what percentage of variance in the concentration of titanium in a Ti-6Al-4V alloy powder can be tolerated before an AM build fails. Or how do levels of impurities such as oxygen, iron, carbon, and nitrogen affect the tensile strength of the resulting AM part?
Possible variations in processing parameters in a laser-powered AM machine (border power, border speed, hatch distance, hatch speed, hatch offset, volumetric energy density, etc.) add to the complexity of the AM challenge. Factoring in heat treatments, non-destructive testing (NDT) inspection, and mechanical testing variables, leads to a major data management challenge. Being able to predict what will happen plays a large role in ensuring a high rate of build success when so many process-property combinations exist.
A data-driven approach to AM
Ansys Granta MI is the Ansys product for capturing material information. It enables companies to collect, organize, and store their own materials data gathered from testing of incoming raw materials and outgoing end products. This customized data is invaluable in fine-tuning the AM process for a company’s proprietary materials intellectual property.
Even with the data management capabilities of Granta MI, real-world AM datasets are typically sparse and noisy. The term “sparse” is used to indicated that there are gaps in certain lines or columns of a dataset, while “noisy” points to the fact that there can be a large spread between different measurements of the same property. Another problem is that even though there may be gaps in the data, these material datasets can become too large to efficiently extract useful data.
In order to handle these massive quantities of data, Ansys partnered with Intellegens to incorporate its Alchemite algorithm to statistically fill in the gaps and reduce the noise in the data. Machine learning (ML) algorithms, often based on neural network approaches, build models from sample data, known as training data. Based only on what they have “learned” from this data without further explicit programming, these models can predict missing values and outputs for a new set of input variables. Ansys machine learning can also be used in AM to optimize performance by predicting which inputs will best achieve desired outputs, identify outliers, anomalies, or clusters in the data, as well as which inputs are strong drivers of which outputs
Machine learning uses existing AM project data to build predictive models that identify key process-property relationships, guide your testing program, and propose optimal processing parameters. In short, Alchemite can extract value from your data even when it is sparse, noisy, and in high volumes. When you have too little data, ML helps you focus your data acquisition efforts efficiently by identifying key parameters that are missing. When you have too much data, Alchemite can extract the key parameters that are critical to predicting AM build success. Learn more about how the Ansys solution works for AM data with our on-demand webinar.
Using machine learning in AM
This new level of insight into AM data enables engineers to control the AM process and optimize material and part performance. You can achieve results with a greatly reduced number of experimental test cycles, which helps accelerate time to market and leads to substantial end-to-end total cost savings for AM programs.
To make the software easy to learn and use, Granta MI for AM solutions has been distilled into three apps: a training dataset app, an optimization app, and a visualization app.
The training dataset app helps engineers create the training dataset for the neural network, while the optimization app lets them take the model created by the neural network and interrogate it to get answers to your specific questions regarding AM processes. Finally, the visualization app lets engineers quickly understand their data via graphic insights displays.
According to Ansys’ calculations, AM processes that combine a data management system with ML are predicted to result in a 50-90% reduction in the number of experiments needed to establish the correct AM processing parameters. This and other benefits will lead to a 10% cost reduction in the global AM market. Learn more from the Ansys white paper “How Machine Learning Helps Getting Additive Manufactured Parts to Market Faster.”
For AM to continue growing as an industrial manufacturing process, manufacturers will have to rely on improvements that reduce the number of build failures and give them confidence in their workflows. They can’t afford to keep wasting money on expensive metal powders that only end up on the scrap pile.
Ansys will continue to add features to Granta MI and work with Intellegens to make ML an even more important part of the AM build process, ensuring that the benefits of additive manufacturing will be available to future generations of engineers.