Six-Sigma Quality Management of Additive Manufacturing

Quality is a key determinant in deploying new processes, products, or services and influences the adoption of emerging manufacturing technologies. The advent additive (AM) as process has potential to revolutionize host enterprise-related functions from production supply chain. unprecedented level design flexibility expanded functionality offered by AM, coupled with greatly reduced lead times, can potentially pave way for mass customization. However, widespread application AM currently hampered technical challenges repeatability quality management. breakthrough effect six sigma (6S) been demonstrated traditional industries (e.g., semiconductor automotive industries) context planning, control, improvement through intensive use data, statistics, optimization. 6S entails data-driven DMAIC methodology five steps-define, measure, analyze, improve, control. Notwithstanding sustained successes knowledge body variety established ranging manufacturing, healthcare, logistics, beyond, there dearth concentrated management approaches AM. In this article, we propose design, develop, implement First, define specific arising layerwise fabrication customization (even one-of-a-kind production). Second, present review metrology sensing techniques, materials process, environment, postbuild inspection. Third, contextualize framework realizing full data systems emphasize need analytical methods tools. We delineate utility methods, including deep learning, machine network science, characterize model interrelationships between engineering setting, variability, final build quality. Fourth, methodologies ontology analytics, experiments (DOE), simulation analysis system improvements. closing, control are discussed optimize action plans, once an anomaly detected, consideration time energy consumption. posit that work will catalyze more in-depth investigations multidisciplinary research efforts accelerate