Penn State Research Eyes Traditional Quality Method to Propel Big Growth in Additive Manufacturing

July 6, 2021

According to Dr. Hui Yang, professor of Industrial Engineering at Penn State, quality excellence in additive manufacturing (AM) could have a big socioeconomic impact and major outcomes in the industry’s profitability, sustainability and efficiency. Dr. Yang recently led a study that proposes a new data-driven methodology for quality control in additive manufacturing.

“Ultimately, everyone wants to do high-precision, high-end manufacturing, but if quality suffers at any step during production, you lose the competitive advantage needed for the global market.”

The team of Penn State researchers, in partnership with teams at the University of Nebraska-Lincoln and the National Institute of Standards and Technology (NIST), are tapping into the popular Six Sigma approach, often found in traditional manufacturing settings, to ensure AM and its mass customization potential, can meet the needs of vital sectors like aerospace, healthcare and automotive.

How Six Sigma Can Make Additive Manufacturing More Profitable

The researchers’ method to achieve quality excellence hinges on Six Sigma, a popular approach that uses data-driven tactics to eliminate defects, drive profits and improve quality of products.

“Six-sigma AM quality improvement has the potential to substantially improve production-scale viability of AM and enable wider exploitation of AM capabilities beyond the current rapid prototyping status quo,” Dr. Hui Yang said. “Ultimately, everyone wants to do high-precision, high-end manufacturing, but if quality suffers at any step during production, you lose the competitive advantage needed for the global market.”

Yang believes that achieving this quality excellence “will spur the growth of advanced manufacturing in the nation and the world.”

Yang noted that, in terms of profitability, quality excellence can allow for quick scaling of process conditions to changing requirements. On sustainability, these processes can improve the economy of resources and energy by reducing waste, scrap and rework. Lastly, quality excellence in AM can improve efficiency by minimizing efforts required towards obtaining the best quality product

Yang believes that achieving this quality excellence “will spur the growth of advanced manufacturing in the nation and the world.”

“Identifying the Sticking Points”

As part of the study, the team of researchers worked together to analyze various academic papers to deduce a six-sigma framework of quality control for additive manufacturing, which lead to their proposed systems engineering approach.

“Via the research we analyzed, we identified the critical challenges of additive manufacturing and where quality standards are lacking,” Yang said. “For each step in the process, you need to identify the sticking points, which is where methods such as machine learning can come into play and help show an engineer or designer how to control the process to avoid defects.”

The authors of the study include Yang; Prahalad Rao, associate professor of mechanical and materials engineering at the University of Nebraska-Lincoln; Yan Lu, NIST; Paul Witherell, NIST; Abdalla R. Nassar, associate research professor of engineering science and mechanics and mechanical engineering at Penn State; and Edward Reutzel, associate research professor engineering science and mechanics at Penn State.

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