Chicago, IL – September 14, 2021 – Senvol President Annie Wang spoke earlier today at the annual RAPID + TCT Conference. The presentation, titled “Efficient Process Parameter Optimization Using Machine Learning,” focused on multiple case studies with Fortune 100 automotive company BMW. The case studies utilized Senvol’s additive manufacturing (AM) machine learning software, Senvol ML.
BMW has been using AM for more than 30 years to make end use parts in a wide variety of applications (both metal and polymer). The presentation detailed case studies of how BMW successfully leveraged the Senvol ML software to help the Additive Manufacturing Center’s metals group drastically improve the process of finding optimal process parameters for an aluminum alloy produced on a quad-laser powder bed fusion system.
The process parameters that were developed using the Senvol ML software exceeded the performance of the conventionally optimized parameters – even though taking less time. This was validated by making real end-use parts. Furthermore, by using the new process parameters BMW succeeded in building parts that had failed in previous builds. Finally, BMW was able to achieve such optimal process parameters while also reducing the number of data points needed. This resulted in fewer builds, less testing, less time and less cost.
The presentation described two different projects that were undertaken. The first project was focused on finding process parameters for good surface finish and high tensile strength; the second project was focused on minimizing distortion while also achieving high tensile strength. Furthermore, the presentation explained to the audience how a machine learning approach is different from conventional approaches and walked the audience through the steps that BMW took to rapidly find optimal parameters while minimizing data generation time and costs.
RAPID + TCT is North America’s preeminent event for discovery, innovation, and networking in additive manufacturing, and hosts one of the most experienced and influential communities in additive manufacturing technologies.
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