IMTS 2024 Conference: End-to-end AM Quality for a Production Case Study: Data Driven Process Control Using Machine Learning

Learn about end-to-end AM workflows for serial production.

End-to-end AM Quality for a Production Case Study: Data Driven Process Control Using Machine Learning with Oqton
End-to-end AM Quality for a Production Case Study: Data Driven Process Control Using Machine Learning with Oqton
GIE Media's Manufacturing Group

Thursday September 12 2:15 PM CST
IMTS64 Room W192-A

Learn about the presentation
The issues plaguing additive manufacturing are common to many other emergent technologies – complicated workflows, over-reliance on manual interventions from specialists, and inconsistent product quality. To make additive a mature industrial production method, it needs to be faster, simpler and more reliable. Baker Hughes and Oqton made a significant step toward this goal by developing an end-to-end AM workflow for the serial production of a Variable Resistance Trim (VRT) component. Built on a fully digital framework, it includes simulation, real-time process monitoring, anomaly analysis, and data preparation automation. Each part produced has a unique digital fingerprint with an extensive digital data thread, including the steps of post-processing that produce automatically robust quality assurance documentation enabling a transparent traceability end to end, that complies with the most stringent regulatory process in our industry. This lays the foundation for an additive production workflow that delivers consistent quality and the documentation necessary for certification.

    Meet your presenter
    With 10 years of experience using additive manufacturing (AM), Rodrigo Enriquez Gutierrez, senior application engineer – Customer Success Team at Oqton has utilized all seven additive manufacturing technologies in the industry. Most of his experience is inside the aerospace and defense verticals, but throughout his career he has been exposed to other AM verticals such as biomedical, robotics, and automotive industries. He received a B.S. in Mechanical Engineering from University of Texas at El Paso in 2015.

    Currently, his work focuses on helping Oqton customers achieve their goals with additive manufacturing, while using Oqton’s 3DXpert and MOS (Manufacturing Operating System). This allows him to continue seeking new boundary-pushing parts on a regular basis.