37
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Variation analysis for custom manufacturing processes

ORCID Icon & ORCID Icon

References

  • Abellán-Nebot, J. V. 2018. Derivation and application of the stream of variation model to the manufacture of ceramic floor tiles. Quality Engineering 30 (4):713–29. doi: 10.1080/08982112.2017.1385078.
  • Andreella, A., R. De Santis, A. Vesely, and L. Finos. 2023. Procrustes-based distances for exploring between-matrices similarity. Statistical Methods & Applications 32 (3):867–82. doi: 10.1007/s10260-023-00689-y.
  • Brock, A., J. Donahue, and K. Simonyan. 2019. Large Scale GAN Training for High Fidelity Natural Image Synthesis. ArXiv:1809.11096 [Cs, Stat]. http://arxiv.org/abs/1809.11096 (accessed March 10, 2022).
  • Bui, A. T. 2022. Root cause analysis of manufacturing variation from optical scanning data. Annals of Operations Research. doi: 10.1007/s10479-022-05077-5.
  • Bui, A. T., and D. W. Apley. 2019. An exploratory analysis approach for understanding variation in stochastic textured surfaces. Computational Statistics & Data Analysis 137:33–50. doi: 10.1016/j.csda.2019.01.019.
  • Bui, A. T., and D. W. Apley. 2022. Analyzing nonparametric part-to-part variation in surface point cloud data. Technometrics 64 (4):457–74. doi: 10.1080/00401706.2021.1883482.
  • Colosimo, B. M., M. Grasso, F. Garghetti, and B. Rossi. 2022. Complex geometries in additive manufacturing: A new solution for lattice structure modeling and monitoring. Journal of Quality Technology 54 (4):392–414. doi: 10.1080/00224065.2021.1926377.
  • Colosimo, B. M., and M. Pacella. 2011. Analyzing the effect of process parameters on the shape of 3D profiles. Journal of Quality Technology 43 (3):169–95. doi: 10.1080/00224065.2011.11917856.
  • Comon, P., and C. Jutten. 2010. Handbook of blind source separation: Independent component analysis and applications. Academic Press.
  • Gao, N., H. Xue, W. Shao, S. Zhao, K. K. Qin, A. Prabowo, M. Saiedur Rahaman, and F. D. Salim. 2022. Generative adversarial networks for spatio-temporal data: A survey. ACM Transactions on Intelligent Systems and Technology 13 (2):1–25. doi: 10.1145/3474838.
  • Goedhart, R., and W. H. Woodall. 2022. Monitoring proportions with two components of common cause variation. Journal of Quality Technology 54 (3):324–37. doi: 10.1080/00224065.2021.1903823.
  • Howard, P., D. W. Apley, and G. Runger. 2018a. Distinct variation pattern discovery using alternating nonlinear principal component analysis. IEEE Transactions on Neural Networks and Learning Systems 29 (1):156–66. doi: 10.1109/TNNLS.2016.2616145.
  • Howard, P., D. W. Apley, and G. Runger. 2018b. Identifying nonlinear variation patterns with deep autoencoders. IISE Transactions 50 (12):1089–103. doi: 10.1080/24725854.2018.1472407.
  • Hsu, C.-C., Y.-X. Zhuang, and C.-Y. Lee. 2020. Deep fake image detection based on pairwise learning. Applied Sciences 10 (1):370. doi: 10.3390/app10010370.
  • Li, Z., F. Liu, W. Yang, S. Peng, and J. Zhou. 2022. A survey of convolutional neural networks: Analysis, applications, and prospects. IEEE Transactions on Neural Networks and Learning Systems 33 (12):6999–7019. doi: 10.1109/TNNLS.2021.3084827.
  • Mirza, M., and O. Simon. 2014. Conditional generative adversarial nets. ArXiv:1411.1784 [Cs, Stat. http://arxiv.org/abs/1411.1784 (accessed March 7, 2022).
  • Moos, S., and E. Vezzetti. 2015. Resistance spot welding process simulation for variational analysis on compliant assemblies. Journal of Manufacturing Systems 37 (1):44–71. doi: 10.1016/j.jmsy.2015.09.006.
  • Panahi, M., S. H. Steiner, and J. De Mast. 2023. Verifying a dominant cause of output variation. Quality Engineering 0 (0):1–12. doi: 10.1080/08982112.2023.2253303.
  • Shan, X., and D. W. Apley. 2008. Blind identification of manufacturing variation patterns by combining source separation criteria. Technometrics 50 (3): 332–43. doi: 10.1198/004017008000000316.
  • Shi, Z., D. W. Apley, and G. C. Runger. 2016. Discovering the nature of variation in nonlinear profile data. Technometrics 58 (3):371–82. doi: 10.1080/00401706.2015.1049751.
  • Shi, Z., D. W. Apley, and G. C. Runger. 2019. Identifying and visualizing part-to-part variation with spatially dense optical dimensional metrology data. Journal of Quality Technology 51 (1):3–20. doi: 10.1080/00224065.2018.1541380.
  • Sohn, K., H. Lee, and X. Yan. 2015. Learning structured output representation using deep conditional generative models. Advances in Neural Information Processing Systems 28:3483–91. https://proceedings.neurips.cc/paper/2015/hash/8d55a249e6baa5c06772297520da2051-Abstract.html.
  • Steiner, S. H., and R. J. MacKay. 2014. Statistical engineering and variation reduction. Quality Engineering 26 (1):44–60. doi: 10.1080/08982112.2013.846069.
  • Verna, E., G. Genta, M. Galetto, and F. Franceschini. 2020. Planning offline inspection strategies in low-volume manufacturing processes. Quality Engineering 32 (4):705–20. doi: 10.1080/08982112.2020.1739309.
  • Wang, J., X. Zeng, S. Duan, Q. Zhou, and H. Peng. 2022. Image target recognition based on improved convolutional neural network. Mathematical Problems in Engineering 2022 (July):e2213295–11. doi: 10.1155/2022/2213295.
  • Wibawa, A. P., A. B. P. Utama, H. Elmunsyah, U. Pujianto, F. A. Dwiyanto, and L. Hernandez. 2022. Time-series analysis with smoothed convolutional neural network. Journal of Big Data 9 (1):44. doi: 10.1186/s40537-022-00599-y.
  • Zhang, Z., Y. Song, and H. Qi. 2017. Age Progression/Regression by Conditional Adversarial Autoencoder. arXiv. https://doi.org/10.48550/arXiv.1702.08423 (accessed March 31, 2024).

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.