Smart Manufacturing strives to monitor every aspect of the manufacturing enterprise - from the individual machine-level to the factory-level - using data gathered from multiple sensors. Resulting efficiencies can reduce product defects and manufacturing costs by over 25 percent. When coupled with Additive Manufacturing, Smart Manufacturing promises to transform U.S. industry. For example, 20 pounds of raw material are currently required to make a one-pound part for the aerospace industry using subtractive machining. Additive Manufacturing can reduce this so-called buy-to-fly ratio of 20:1 to 2:1, while simultaneously reducing lead time from six months to one week. Realization of these potential manufacturing gains will advance the national prosperity and welfare by increasing U.S. advanced manufacturing competitiveness. Despite these advantages, industries are hesitant to adopt Additive Manufacturing due to process inconsistency - parts may have undetected defects, such as porosity, that make them unsafe for use in mission-critical applications. A potential solution to this problem is a concept called Smart Additive Manufacturing, which melds the ideas of Smart Manufacturing with Additive Manufacturing. Through this Faculty Early Career Development Program (CAREER) award, in-process sensor data will be utilized to understand the mechanisms of defect formation occurring during the Laser Powder Bed Fusion Additive Manufacturing process. Advanced data analysis approaches that incorporate the new fundamental understanding of defect evolution will be leveraged to realize a robust correct-as-you-build methodology. This foundational work will find application across many manufacturing sectors including aerospace and defense. The award will also facilitate a discovery-based learning approach to engage learners in hands-on exploration of Additive Manufacturing at multiple levels. A research collaboration with Navajo Technical University will be initiated to further broaden project impact and train the advanced manufacturing workforce of the future.
The research goal of this project is to establish a Smart Additive Manufacturing framework for alleviating the poor part quality in the Laser Powder Bed Fusion-based Additive Manufacturing of metals. Success will result in a hybrid Additive Manufacturing strategy that combines material deposition (additive) and material removal (subtractive) actions within the same machine potentially giving rise to zero-defect parts. The research challenges addressed by this award include: 1) understanding how and why certain defects are formed by isolating and quantifying the underlying process phenomena as they happen in real-time using in-process sensors, 2) advancing the mathematics of spectral graph theory to capture defects from heterogeneous sensors in real-time - a big data problem, and 3) forwarding reduced-order models to understand the physical thermomechanical dynamics, such as layer re-melting and reflow, that occur when defects are corrected with hybrid Laser Powder Bed Fusion.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
