Engineering Data Science: Defect Detection and AI Use Cases in Manufacturing

Jeffrey Chowaniec, Solution Engineer at Altair presents at the 2025 ATCx Unlocking Data Science & AI conference.

One of the biggest challenges in additive manufacturing is quality control of complex geometries where quality analysis of the product through imaging like XCT is expensive and not feasible in a rapid prototyping environment. At Penn State University, this issue manifested in the fabrication of new acoustic liners for aircraft engines. Through collaboration with Altair RapidMiner Senior Data Scientist Jeff Chowaniec, the problem was solved by leveraging advanced AI models to detect real-time defects using imagery from the printing process. This presentation showcases how Altair's powerful AI tools, such as RapidMiner AI Studio, bridge the gap between engineers and data scientists, enabling seamless integration of domain expertise and advanced analytics. Using an intuitive drag-and-drop interface, this end-to-end solution provides an accessible and effective approach to identifying defects in single image frames—empowering engineers to gain actionable insights without requiring deep programming expertise.