SIMTech – Research Engineer Intern

Metal additive manufacturing (AM) processes such as laser powder bed fusion (LPBF) are affected by many process parameters, resulting in poor manufacturing repeatability. The internship applicant would be part of a project to integrate, gather data, and correlate data from multiple typical and new in-process sensors for a LPBF system with the aim of detecting defects and predicting microstructure through machine learning algorithms. Reliable defect detection and microstructure prediction will make the LPBF process more robust and thus achieve better quality assurance.

The intern would be involved in

• Reading journal papers to understand the subject matter.
• Preparing the metal AM system for experiments such as preparing the build file, pre-processing the input materials, and setting up the system for the build
• Monitoring the build and ensuring the data collection is running
• Unpacking the system by taking out the built parts from the machine,
• Physical properties measurements by testing the properties of the build part,
• Analyzing the data collected from the sensors, and correlating the physical measurements to the digital data sensors using statistical analysis and machine learning

The intern should come from a background in Mechanical Engineering, Electrical & Electronic Engineering, or Computer Science.