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Six-sigma Quality Management of Additive Manufacturing

Published

Author(s)

Yan Lu, Hui Yang, Paul W. Witherell

Abstract

Quality is a key determinant in deploying new processes, products or services, and influences the adoption of emerging manufacturing technologies. The advent of additive manufacturing (AM) as a manufacturing process has the potential to revolutionize a host of enterprise-related functions from production to supply chain. The unprecedented level of design flexibility and expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave the way for mass customization. However, widespread application of AM is currently hampered by technical challenges in process repeatability and quality management. The breakthrough effect of Six Sigma has been demonstrated in traditional manufacturing industries (e.g., semiconductor and automotive industries) in the context of quality planning, control, and improvement through the intensive use of data, statistics and optimization. Six sigma entails a data-driven DMAIC methodology of five steps - Define, Measure, Analyze, Improve, and Control. Notwithstanding the sustained successes of Six-Sigma knowledge body in a variety of established industries ranging from manufacturing, healthcare, logistics and beyond, there is a dearth of concentrated application of Six-Sigma quality management approaches in the context of AM. In this paper, we propose to design, develop, and implement the new DMAIC methodology for Six-Sigma quality management of AM. First, we define the specific quality challenges arising from AM layer-wise fabrication and mass customization (even one-of-a-kind production). Second, we present a review of AM metrology and sensing techniques, from materials through design, process, environment, to post-build inspection. Third, we contextualize a framework for realizing the full potential of data from AM systems, and emphasize the need for analytical methods and tools.
Citation
Proceedings of the IEEE

Keywords

Additive manufacturing, sensor systems, data analytics, quality management, engineering design, simulation modeling, artificial intelligence
Created November 25, 2020, Updated December 2, 2020