PMID- 34248180
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20210714
IS  - 0018-9219 (Print)
IS  - 0018-9219 (Linking)
VI  - 109
IP  - 4
DP  - 2021 Apr
TI  - Six-Sigma Quality Management of Additive Manufacturing.
LID - 10.1109/JPROC.2020.3034519 [doi]
AB  - 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 the 
      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 (6S) 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. 6S 
      entails a data-driven DMAIC methodology of five steps-define, measure, analyze, 
      improve, and control. Notwithstanding the sustained successes of the 6S knowledge 
      body in a variety of established industries ranging from manufacturing, 
      healthcare, logistics, and beyond, there is a dearth of concentrated application 
      of 6S quality management approaches in the context of AM. In this article, we 
      propose to design, develop, and implement the new DMAIC methodology for the 6S 
      quality management of AM. First, we define the specific quality challenges 
      arising from AM layerwise 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, and environment, to postbuild 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. We propose 
      and delineate the utility of new data-driven analytical methods, including deep 
      learning, machine learning, and network science, to characterize and model the 
      interrelationships between engineering design, machine setting, process 
      variability, and final build quality. Fourth, we present the methodologies of 
      ontology analytics, design of experiments (DOE), and simulation analysis for AM 
      system improvements. In closing, new process control approaches are discussed to 
      optimize the action plans, once an anomaly is detected, with specific 
      consideration of lead time and energy consumption. We posit that this work will 
      catalyze more in-depth investigations and multidisciplinary research efforts to 
      accelerate the application of 6S quality management in AM.
FAU - Yang, Hui
AU  - Yang H
AUID- ORCID: 0000-0001-5997-6823
AD  - Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, 
      The Pennsylvania State University, University Park, PA 16802 USA.
FAU - Rao, Prahalad
AU  - Rao P
AUID- ORCID: 0000-0002-9642-622X
AD  - Department of Mechanical and Materials Engineering, University of 
      Nebraska-Lincoln, Lincoln, NE 68588 USA.
FAU - Simpson, Timothy
AU  - Simpson T
AD  - Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, 
      The Pennsylvania State University, University Park, PA 16801 USA.
FAU - Lu, Yan
AU  - Lu Y
AD  - National Institute of Standards and Technology, Gaithersburg, MD 20899 USA.
FAU - Witherell, Paul
AU  - Witherell P
AD  - National Institute of Standards and Technology, Gaithersburg, MD 20899 USA.
FAU - Nassar, Abdalla R
AU  - Nassar AR
AD  - Center for Innovative Materials Processing 3D (CIMP-3D), The Pennsylvania State 
      University, University Park, PA 16801 USA.
FAU - Reutzel, Edward
AU  - Reutzel E
AD  - Center for Innovative Materials Processing 3D (CIMP-3D), The Pennsylvania State 
      University, University Park, PA 16801 USA.
FAU - Kumara, Soundar
AU  - Kumara S
AD  - Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, 
      The Pennsylvania State University, University Park, PA 16802 USA.
LA  - eng
GR  - 9999-NIST/ImNIST/Intramural NIST DOC/United States
PT  - Journal Article
PL  - United States
TA  - Proc IEEE Inst Electr Electron Eng
JT  - Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
JID - 9879073
PMC - PMC8269016
MID - NIHMS1701342
OTO - NOTNLM
OT  - Additive manufacturing (AM)
OT  - artificial intelligence (AI)
OT  - data analytics
OT  - engineering design
OT  - quality management
OT  - sensor systems
OT  - simulation modeling
EDAT- 2021/07/13 06:00
MHDA- 2021/07/13 06:01
PMCR- 2021/07/09
CRDT- 2021/07/12 05:39
PHST- 2021/07/12 05:39 [entrez]
PHST- 2021/07/13 06:00 [pubmed]
PHST- 2021/07/13 06:01 [medline]
PHST- 2021/07/09 00:00 [pmc-release]
AID - 10.1109/JPROC.2020.3034519 [doi]
PST - ppublish
SO  - Proc IEEE Inst Electr Electron Eng. 2021 Apr;109(4):10.1109/JPROC.2020.3034519. 
      doi: 10.1109/JPROC.2020.3034519.