Minimizing Residual Stress and Distortion in Metal Additive Manufacturing Using an Intelligent Controller

Conference Abstract: Metal additive manufacturing (AM) is gaining attention in a variety of high-value industries, like aerospace, automotive and biomedical. The most common methods for metal AM involve the use of a high-power heat source, like a laser or electron beam, to melt the metal feedstock. This could be in the form of powder bed fusion or directed energy deposition of powder or wire. However, the concentrated heat energy used in such AM techniques often leads to high temperature gradients that result in residual stress, shape distortion, and cracks in the manufactured product. Researchers at the Smart and Sustainable Automation (S2A) Lab at the University of Michigan have developed an intelligent control algorithm, called SmartScan, for controlling the path of a high-power beam source (e.g., laser) during metal AM to minimize temperature gradients hence reduce thermal-induced defects. The method uses thermal models of the metal AM process to guide an optimization algorithm to determine the best laser path. Research at the University of Michigan has demonstrated the ability of SmartScan to reduce distortion by 50% and residual stress by 9x in 3D printed parts. This talk will briefly describe how SmartScan works and how researchers from the S2A Lab are working with Ulendo Technologies, Inc., a spin-off company from the University of Michigan, to further develop SmartScan for commercial deployment on metal AM machines. Specifically, Ulendo is working with the University of Michigan to develop SmartScan into a commercial software product called Ulendo HC (heat compensation) that will plug into the slicer software of metal AM machines to optimize laser scan paths to minimize warping, cracks and residual stress. The end result would be the ability to produce complex metal AM parts right the first time, thus reducing cost and increasing yield.