Senvol showcases a novel approach for material allowables, driven by machine learning.

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Guess what? The nominations for the 3D Printing Industry Awards 2023 are officially open! If you’re interested in finding out who the leaders in 3D printing are, mark your calendars for November 30th. That’s when the winners in twenty different categories will be unveiled during an exciting live awards ceremony in London.

But that’s not all. Senvol, a specialist in additive manufacturing data, has recently demonstrated a revolutionary machine learning approach to material allowables. Their machine learning software, Senvol ML, accurately predicts material performance, making the process of material development more cost-effective, flexible, and time-efficient compared to the traditional Metallic Materials Properties Development and Standardization approach.

Senvol partnered with EWI and Pilgrim Consulting to carry out this impressive work. They also had the expertise of Battelle and Hector Sandoval, an LM Fellow at Lockheed Martin, as technical advisers. The project was administered by the National Center for Manufacturing Sciences through the AMMP Other Transaction Agreement program.

According to Senvol President Annie Wang, material allowables development is usually a costly and time-consuming endeavor. However, their program successfully demonstrated a new approach that leverages machine learning, making the development of additive manufacturing material property allowables much more efficient. Dr. William E. Frazier, retired Chief Scientist for Air Vehicle Engineer at NAVAIR and President of Pilgrim Consulting LLC, also praised Senvol’s machine learning-enabled approach for addressing a major industry challenge.

You might be wondering why material allowables development is so expensive and time-consuming in the first place. Well, it’s because a substantial amount of empirical data is required to be generated at a fixed processing point, making any major changes to the process costly and time-consuming. This is where Senvol’s machine learning approach comes in handy.

During the program, Senvol focused on a 17-4 PH Stainless Steel material processed through a powder bed fusion 3D printer. By leveraging their ML software, they were able to develop statistical substantiated material properties comparable to material allowables while optimizing data generation requirements. The best part is that this machine learning approach is flexible and can adapt to changes in the additive manufacturing process, making it ideal for long-term sustainment.

President Zach Simkin explained that while machine learning for additive manufacturing processes and material development is quite mature, the use of machine learning for material allowables development is still a work in progress. The exciting news is that Senvol has already made two successful demonstrations of the machine learning approach to allowables and is eager to continue partnering with the government and industry to advance this area further.

Before we conclude, it’s important to note that the project did not develop true allowables due to budget and programmatic restrictions. However, the project team made several simplifying decisions and the potential for this approach is tremendous.

This is just one example of how AI and machine learning software are making their way into additive manufacturing. Many companies in the industry are already incorporating these technologies, and we can expect more exciting developments in the future.

AI and ML: Revolutionizing Quality Control in 3D Printing

In the rapidly advancing field of 3D printing, companies are constantly seeking ways to improve the reliability and efficiency of their processes. One avenue that holds great promise is the integration of artificial intelligence (AI) and machine learning (ML) capabilities into their software offerings. By harnessing the power of AI and ML, companies can optimize toolpath generation, automate quality assurance, and achieve higher success rates in the first-time printing of parts.

One company leading the way in this regard is Ai Build, based in London. Ai Build offers ML and AI-driven toolpath generation and automated quality assurance through its AiSync software. Luke Rogers, the Commercial Director at Ai Build, recently emphasized the importance of ML capabilities in providing automated quality control. He explained that in large-format 3D printing, there is typically only a 40% success rate when printing a part for the first time. However, by using machine learning and incorporating the data back into the toolpath, the first-time print success rate can easily increase to 90% and above.

Another company taking advantage of AI and ML technology is AON3D, a Montreal-based high-temperature 3D printer manufacturer. During the RAPID + TCT 2023 event, AON3D offered a sneak peek at its new machine learning-driven thermal optimization software. This tool, built specifically for Material Extrusion (MEX), utilizes a core technology simulation engine to provide fast and accurate predictions on heat flow behavior within printed objects. The company claims that this software has the potential to enable cost reduction, improve part reliability and consistency, optimize performance, and facilitate better technical decision making.

The integration of AI and ML capabilities into 3D printing processes holds immense potential. By leveraging the power of these technologies, companies can greatly improve the success rates of first-time prints, minimize material waste, enhance part reliability, and ultimately deliver better products to their customers. AI and ML algorithms are capable of analyzing vast amounts of data, spotting patterns, and making predictions, enabling printers to operate with greater efficiency, accuracy, and predictability.

As the additive manufacturing industry continues to evolve, the demand for professionals with expertise in AI and ML is expected to rise. Those interested in pursuing a career in this exciting field can explore job opportunities on the 3D Printing Jobs platform, where a selection of roles in the additive manufacturing industry are available.

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The integration of AI and ML into 3D printing software is transforming quality control processes and revolutionizing the industry. By harnessing the power of these technologies, companies can unlock new levels of efficiency, reliability, and success in the 3D printing of parts.

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