A Penn State-led team of scientists has developed a new data-driven methodology for quality control in additive manufacturing.
According to the team from Penn State, University of Nebraska-Lincoln and the National Institute of Standards and Technology (NIST), the new method hinges on six sigma, a popular approach that uses data-driven tactics to eliminate defects, drive profits and improve quality of products.
They opine that this five-step approach of defining, measuring, analyzing, improving and controlling could further quality management when applied to additive manufacturing.
According to Penn State professor Hui Yang, additive manufacturing offers an unprecedented level of design flexibility and expanded functionality, and this is why the aerospace, health care and automotive industries are becoming more reliant on 3D printing.
However, as the quality and process varies from one industry’s machines to another, there is a need to ensure that mass customizable additive manufacturing meets certain standards.
“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,” says Yang.
“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.”
“Via the research we analyzed, we identified the critical challenges of additive manufacturing and where quality standards are lacking,” says Yang.
“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.”
Additive manufacturing defects can become massive liabilities when considered in the context of mass-produced products, notes Penn State professor Tim Simpson:
“If your goal is to use additive manufacturing to make parts for a car or a plane, then that part better not fail.”
And then there’s the cost of failed parts: Simpson says that a failed metal build could easily cost 10 to 20 thousand dollars and requires multiple iterations along the way.
By looking for the quality gaps in the standards for mass-produced parts, the team’s proposed methodology could help ensure quality production with additive manufacturing for both high volume and custom products.
Image and content: ZMorph3D-Pixabay/Penn State