Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data-Reference-Cited by-同舟云学术

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Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data

Published:2019-01-02 Issue:1 Volume:51 Page:3-20
ISSN:0022-4065
Container-title:Journal of Quality Technology
language:en
Short-container-title:Journal of Quality Technology

Author:

Shi Zhenyu¹,Apley Daniel W.²,Runger George C.³

Affiliation:

1. True[X], Los Angeles, California;

2. Department of Industrial Engineering & Management Sciences, Northwestern University, Evanston, Illinois;

3. School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, Arizona

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Management Science and Operations Research,Strategy and Management,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/00224065.2018.1541380

Reference25 articles.

1. Identifying Spatial Variation Patterns in Multivariate Manufacturing Processes

2. Simultaneous Identification of Premodeled and Unmodeled Variation Patterns

3. A Factor-Analysis Method for Diagnosing Variability in Mulitvariate Manufacturing Processes

4. Identifying and visualizing nonlinear variation patterns in multivariate manufacturing data

5. Geodesic Gaussian Processes for the Parametric Reconstruction of a Free-Form Surface

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2. Variation analysis for custom manufacturing processes;Quality Engineering;2024-04-04

3. Root cause analysis of manufacturing variation from optical scanning data;Annals of Operations Research;2022-12-21

4. Complex geometries in additive manufacturing: A new solution for lattice structure modeling and monitoring;Journal of Quality Technology;2021-06-30

5. Analyzing Nonparametric Part-to-Part Variation in Surface Point Cloud Data;Technometrics;2021-03-16

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