Researchers from Penn State University, the University of Nebraska-Lincoln and the National Institute of Standards and Technology (NIST) in the US have joined forces to improve quality control in additive manufacturing (AM).
According to Yang, the researchers analyzed data and were able to identify the critical challenges of AM and where quality standards are lacking.
‘Like an ecosystem, we have people working in isolated efforts in different areas of additive manufacturing, and systems engineers can help connect the dots to provide a framework for quality management,’ said Hui Yang, professor of industrial engineering, who is leading the initiative. ‘Quality is indispensable, and if we design a system-level framework of quality management from the start, then we have higher quality and better productivity at less cost. 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.’
‘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,’ he said.
‘Quality control processes and methods are established for mass production, where you make hundreds to millions of things,’ added Tim Simpson, interim head of the school of engineering design, technology, and professional programs. ‘Additive manufacturing enables customization, and the current quality control methods and accepted practices do not readily apply when you are only making one or a few of an item. We have to think differently to ensure highly quality parts.’
The research was published in the Proceedings of Institute of Electrical and Electronics Engineers (IEEE).
This story uses material from Penn State University, with editorial changes made by Materials Today. The views expressed in this article do not necessarily represent those of Elsevier.
