International Journal of Contents

The Korea Contents Association (한국콘텐츠학회)
권호 표지
DOI QR코드

DOI QR Code

A Visualization Scheme with a Calendar Heat Map for Abnormal Pattern Analysis in the Manufacturing Process

  • Received : 2017.02.13
  • Accepted : 2017.03.22
  • Published : 2017.06.28
Download PDF
⟨ Previous Next ⟩

Abstract

Abnormal data in the manufacturing process makes it difficult to find useful information that can be applied in data management for the manufacturing industry. It causes various problems in the daily process of production. An issue from the abnormal data can be handled by our method that uses big data and visualization. Visualization is a new technology that transforms data representation into a two-dimensional representation. Nowadays, many newly developed technologies provide data analysis, algorithm, optimization, and high efficiency, and they meet user requirements. We propose combined production of the data visualization approach that uses integrative visualization of sources of abnormal pattern analysis results. The perceived idea of the proposed approach can solve the problem as it also works for big data. It can also improve the performance and understanding by using visualization and solving issues that occur in the manufacturing process with a calendar heat map.

Keywords

References

  1. J. Lee, E. Lapira, B. Bagheri, and H. Kao, "Recent advances and trends in predictive manufacturing system in a big data environment," Manufacturing Letter, 2013, pp. 38-41.
  2. J. Lee, Behrad Bagheri, and Hung-An Kao, "A Cyper- Physical Systems architecture for Industry 4.0-based manufacturing systems," Manufacturing Letter, vol. 3, 2015, pp. 18-23. https://doi.org/10.1016/j.mfglet.2014.12.001
  3. KSCI, Manufacture Symbol, KS A 3002, 2014
  4. SIEMENSE, 'Plant Simulation', http://www.plm.automation.siemens.com/en_us.products/tecnomatix/manufacturing-simulation/material-flow/plantsimulation.html , May, 2015
  5. Arena, 'Arena Simulation Software', https://www.arenasimulation.com/industrysolutions/manuf acturing-simulation-software , May, 2015
  6. Y. Zhong and B. Shirinzadeh, "Virtual factory for manufacturing process visualization," in Complexity International, vol. 12, 2008, pp. 1-22.
  7. Helga Thorvaldsdottir, James T. Robinson, and Jill P. Mesirov, "Integrative Genomics Viewer (IGV): highperformance genomics data visualization and exploration," vol. 14, 2012, pp.178-192.
  8. D. A. Keim, "Information Visualization and Visual Data Mining," In IEEE transactions on visualization and computer graphics, vol. 7, no. 1, January-march 2002.
  9. G. Koksal, I. Batmaz, and M. C. Testik, "A review of data mining applications for quality improvement in the manufacturing industry," in Expert Systems with Applications, vol. 38, 2011, pp. 13448-13467. https://doi.org/10.1016/j.eswa.2011.04.063
  10. F. Bao and J. Chen, "Visual framework for big data in d3.js," IEEE Workshop on Electronics, Computer and Applications, 2014, pp. 44-50.
  11. D. Pantförder and Birgit V. Heuser, "Benefit and evaluation of interactive 3D process data visualization in operator training of plant manufacturing industry," Proceedings of the 2009 IEEE International Conference on Systems, 2009, pp. 840-845.
  12. M. D. Ma, D. S. H. Wong, S. S. Jang, and S. T. Tseng, "Fault Detection Based on Statistical Multivariate Analysis and Microarray Visualization," IEEE Transactions on industrial informatics, vol. 6, 2010. pp. 18- 24. https://doi.org/10.1109/TII.2009.2030793
  13. M. M. Najafabadi, F. Villanustre, T. M. Khoshgoftaar, N. Seliya, R. Wald, and E. Muharemagic, "Deep learning applications and challenges in big data analytics," Journal of Big Data, vol. 2, no. 1, 2015, pp. 1-21.
  14. Q. Yang and X. Wu, "10 Challenging Problems in Data Mining Research," International Journal of Information Technology & Decision Making, vol. 5, no. 4, 2006, pp. 597-604. https://doi.org/10.1142/S0219622006002258
  15. L. J. Wells, F. M. Megahed, J. A. Camelio, and W. H. Woodall, "A framework for variation visualization and understanding in complex manufacturing systems," Journal of Intelligent Manufacturing, vol. 23, 2012, pp. 2025-2036. https://doi.org/10.1007/s10845-011-0529-1
  16. Zhiqiang Ge and Zhihuan Song, "Semiconductor Manufacturing Process Monitoring Based on Adaptive Substatistical PCA," IEEE Transactions on Semiconductor Manufacturing, vol. 23, 2010, pp. 99-108. https://doi.org/10.1109/TSM.2009.2039188
  17. J. Jo, J. Huh, J. Park, B. Kim, and J. Seo, "LiveGantt: Interactively Visualizing a Large Manufacturing Schedule," IEEE Transaction on Visualization and Computer Graphics, vol. 20, no. 12, 2014, pp. 2329-2337. https://doi.org/10.1109/TVCG.2014.2346454
  18. F. Doil, W. Schreiber, T. Alt, and C. Patron, "Augmented reality for manufacturing planning," in Proceedings of the Workshop on Virtual Environments (EGVE), 2003, pp. 71-76.
  19. A. Tang, C. Owen, F. Biocca, and W. Mou, "Comparative Effectiveness of augmented reality in object assembly," Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 2003, pp.73-80.
  20. D. Chankhihort, S. S. Choi, G. J. Lee, B. M. Im, A. Nasridinov, S.O. Kwon, S. H. Lee, J. T. Kang, and K. H. Yoo, "Integrative Manufacturing Data Visualization using Calendar View Map," 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN), 2016, pp. 114-116.
  21. D. Chankhihort, S. S. Choi, G. J. Lee, B. M. Im, A. Nasridinov, S. O. Kwon, S. H. Lee, J. T. Kang, and K. H. Yoo, "Integrative visualization of resources abnormal analysis results," The 3rd international conference on big data applications and services (Bigdas-L), vol. 3, 2016, pp. 196-200.
  22. John F. Magee, Decision trees for decision making, Harvard Business Review, 1964.
  23. T. P. Yang, C. Beazley, S. B. Montgomery, A. S. Dimas, M.G.Arcelus, B, E. Stranger, P. Deloukas, and E. T. Dermitzakis, "Genevar: a database and Java application for the analysis and visualization of SNP-gene associations in eQTL studies," Bioinformatics, vol. 26, 2010, pp. 2474-2476. https://doi.org/10.1093/bioinformatics/btq452