The paper, “Progressive measurement and monitoring for multi-resolution data in surface manufacturing considering spatial and cross correlations,” based on Shao’s PhD work, outlines how he and his colleagues developed a new methodology for efficiently measuring and monitoring surface variations by fusing in-plant multi-resolution measurements and process information. The researchers also presented a real-world case study to show the effectiveness of their method.
The Institute of Industrial and Systems Engineers’ Transactions on Quality and Reliability Engineering is a top journal in the area of quality, reliability, and manufacturing. The Best Application Paper is an annual award selected from among all papers published in the journal.
Shao was formally recognized by IISE at the Industrial and Systems Engineering Research Conference in Pittsburgh on May 22.
Shao earned his PhD in mechanical engineering from the University of Michigan in 2016, and joined the MechSE department in August 2016. His research interests lie at the crossroads of digital manufacturing and big data analytics, and manufacturing system control and automation.