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Related Concept Videos

Quality Control01:05

Quality Control

504
Quality control is one of the three cyclical quality assurance activities that help keep a system under statistical control. Typical quality control activities include creating quality control charts, conducting proficiency testing, and documenting and archiving results.
Quality control helps track data, visualize trends, and identify variations, making it easier to detect deviations that may affect the accuracy of an analysis. One way to do this is by generating a quality control chart, which...
504

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Additive Manufacturing of Functionally Graded Ceramic Materials by Stereolithography
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Six-Sigma Quality Management of Additive Manufacturing.

Hui Yang1, Prahalad Rao2, Timothy Simpson3

  • 1Harold and Inge Marcus Department of Industrial and Manufacturing Engineering, The Pennsylvania State University, University Park, PA 16802 USA.

Proceedings of the IEEE. Institute of Electrical and Electronics Engineers
|July 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new data-driven DMAIC methodology for Six Sigma (6S) quality management in additive manufacturing (AM). It addresses AM

Keywords:
Additive manufacturing (AM)artificial intelligence (AI)data analyticsengineering designquality managementsensor systemssimulation modeling

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Area of Science:

  • Manufacturing Engineering
  • Quality Management
  • Industrial Engineering

Background:

  • Additive Manufacturing (AM) offers design flexibility and mass customization potential but faces challenges in process repeatability and quality.
  • Six Sigma (6S) has proven effective in traditional manufacturing for quality improvement using a data-driven DMAIC methodology.
  • There is a gap in applying 6S quality management principles specifically to the unique challenges of AM.

Purpose of the Study:

  • To design, develop, and implement a novel DMAIC methodology tailored for Six Sigma quality management in Additive Manufacturing.
  • To address specific quality challenges in AM, including layerwise fabrication and mass customization.
  • To accelerate the adoption of robust quality management practices in the AM sector.

Main Methods:

  • Defining AM-specific quality challenges related to layerwise fabrication and customization.
  • Reviewing AM metrology, sensing techniques, and data analytics frameworks.
  • Proposing data-driven analytical methods like deep learning, machine learning, and network science.
  • Utilizing ontology analytics, Design of Experiments (DOE), and simulation for AM system improvements.
  • Discussing new process control approaches for optimizing action plans, considering lead time and energy consumption.

Main Results:

  • A comprehensive framework for leveraging AM data through advanced analytical methods is presented.
  • Interrelationships between design, machine settings, process variability, and build quality are modeled.
  • Methodologies for enhancing AM system performance and quality are delineated.
  • New process control strategies are proposed to optimize anomaly detection and response.

Conclusions:

  • The proposed DMAIC methodology provides a structured approach to Six Sigma quality management for Additive Manufacturing.
  • This work aims to bridge the gap in applying established quality frameworks to emerging AM technologies.
  • It is expected to stimulate further research and multidisciplinary efforts to enhance AM quality and adoption.