Skip to main content

Advertisement

SpringerLink
Log in

Machine Learning Pathway for Harnessing Knowledge and Data in Material Processing

  • Published:
International Journal of Metalcasting Aims and scope Submit manuscript

Abstract

Artificial intelligence (AI) is integral to Industry 4.0 and the evolution of smart factories. To realize this future, material processing industries are embarking on adopting AI technologies into their enterprise and plants; however, like all new technologies, there is always the potential for misuse or the false belief that the outcomes are reliable. The goal of this paper is to provide context for the application of machine learning to materials processing. The general landscapes of data science and materials processing are presented, using the foundry and the metal casting industry as an exemplar. The challenges that exist with typical foundry data are that the data are unbalanced, semi-supervised, heterogeneous, and limited in sample size. Data science methods to address these issues are presented and discussed. The elements of a data science project are outlined and illustrated by a case study using sand cast foundry data. Finally, a prospective view of the application of data science to materials processing and the impact this will have in the field are given.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6

Similar content being viewed by others

References

  1. “Industry 4.0: the fourth industrial revolution- guide to Industrie 4.0.” https://www.i-scoop.eu/industry-4–0/. Accessed May 26, 2020.

  2. K.-D. Thoben, S. Wiesner, T. Wuest, BIBA – Bremer Institut für Produktion und Logistik GmbH, the University of Bremen, Faculty of Production Engineering, University of Bremen, Bremen, Germany, and Industrial and Management Systems Engineering, “‘Industrie 4.0’ and Smart Manufacturing—A Review of Research Issues and Application Examples. Int. J. Autom. Technol. 11(1), 4–16 (2017). https://doi.org/10.20965/ijat.2017.p0004

    Article  Google Scholar 

  3. Capgemini Consulting Group, Industry_4.0_-The_Capgemini_Consulting_V.pdf. Capgemini, 2014, [Online]. Available: https://www.capgemini.com/consulting/wp-content/uploads/sites/30/2017/07/capgemini-consulting-industrie-4.0_0_0.pdf.

  4. T. Prucha, From the Editor - Big Data. Int. J. Met. 9(3), 5 (2015)

    Google Scholar 

  5. J. Friedman, R. Tibshirani, T. Hastie, The Elements of Statistical Learning: Data Mining, Inference, and Prediction (Springer, Berlin, 2001)

    Google Scholar 

  6. L. Hauser, Internet encyclopedia of philosophy, Artificial Intelligence. https://www.iep.utm.edu/art-inte/. Accessed 26 May 2020.

  7. A. M. Turing, I.—COMPUTING MACHINERY AND INTELLIGENCE, Mind, vol. LIX, no. 236, pp. 433–460, 1950, https://doi.org/10.1093/mind/LIX.236.433.

  8. C. Bernhardt, Turing’s Vision—The Birth of Computer Science (MIT Press, Cambridge, 2016)

    Google Scholar 

  9. J. McCarthy, M. Minsky, N. Rochester, C.E. Shannon, A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence. Aug. 31, 1955, Accessed 17 Feb 17 2020. [Online]. https://wvvw.aaai.org/ojs/index.php/aimagazine/article/view/1904.

  10. K.P. Murphy, Machine Learning: A Probabilistic Perspective (MIT Press, Cambridge, MA, 2012)

    Google Scholar 

  11. Y. Zhu, Y. Zhang, The study on some problems of support vector classifier, Comput. Eng. Appl., 2003, [Online]. Available: https://en.cnki.com.cn/Article_en/CJFDTotal-JSGG200313011.htm.

  12. M.W. Craven, J.W. Shavlik, Using neural networks for data mining. Data Min. 13(2), 211–229 (1997). https://doi.org/10.1016/S0167-739X(97)00022-8

    Article  Google Scholar 

  13. J.D. Rodriguez, A. Perez, J.A. Lozano, Sensitivity analysis of k-Fold cross validation in prediction error estimation. IEEE Trans. Pattern Anal. Mach. Intell. 32(3), 569–575 (2010). https://doi.org/10.1109/TPAMI.2009.187

    Article  Google Scholar 

  14. C. Reid Turner, A. Fuggetta, L. Lavazza, A.L. Wolf, A conceptual basis for feature engineering. J. Syst. Softw. 49(1), 3–15 (1999). https://doi.org/10.1016/S0164-1212(99)00062-X

    Article  Google Scholar 

  15. A. Zheng, A. Casari, Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (O-Reilly, Beijing, 2018)

    Google Scholar 

  16. I. Gibson, C. Amies, Data normalization techniques, 6259456, 10 Jul 2001.

  17. Z-Transform, Wolfram MathWorld. https://mathworld.wolfram.com/Z-Transform.html. Accessed 26 May 2020.

  18. W.M.P. van der Aalst, V. Rubin, H.M.W. Verbeek, B.F. van Dongen, E. Kindler, C.W. Günther, Process mining: a two-step approach to balance between underfitting and overfitting. Softw. Syst. Model. 9(1), 87 (2008). https://doi.org/10.1007/s10270-008-0106-z

    Article  Google Scholar 

  19. J.K. Kittur, G.C. ManjunathPatel, M.B. Parappagoudar, Modeling of pressure die casting process: an artificial intelligence approach. Int. J. Met. 10(1), 70–87 (2016). https://doi.org/10.1007/s40962-015-0001-7

    Article  Google Scholar 

  20. E. Kocaman, S. Şirin, D. Dispinar, Artificial neural network modeling of grain refinement performance in AlSi10Mg alloy. Int. J. Met. 20, 20 (2020). https://doi.org/10.1007/s40962-020-00472-9

    Article  CAS  Google Scholar 

  21. P.K.D.V. Yarlagadda, E. Cheng Wei Chiang, A neural network system for the prediction of process parameters in pressure die casting. J. Mater. Process. Technol. 89–90, 583–590 (1999). https://doi.org/10.1016/S0924-0136(99)00071-0

    Article  Google Scholar 

  22. J.K. Rai, A.M. Lajimi, P. Xirouchakis, An intelligent system for predicting HPDC process variables in interactive environment. J. Mater. Process. Technol. 203(1–3), 72–79 (2008). https://doi.org/10.1016/j.jmatprotec.2007.10.011

    Article  CAS  Google Scholar 

  23. A. Krimpenis, P.G. Benardos, G.-C. Vosniakos, A. Koukouvitaki, Simulation-based selection of optimum pressure die-casting process parameters using neural nets and genetic algorithms. Int. J. Adv. Manuf. Technol. 27(5–6), 509–517 (2006). https://doi.org/10.1007/s00170-004-2218-0

    Article  Google Scholar 

  24. J. Zheng, Q. Wang, P. Zhao, C. Wu, Optimization of high-pressure die-casting process parameters using artificial neural network. Int. J. Adv. Manuf. Technol. 44(7–8), 667–674 (2009). https://doi.org/10.1007/s00170-008-1886-6

    Article  Google Scholar 

  25. D. Blondheim, Artificial intelligence, machine learning, and data analytics: understanding the concepts to find value in die casting data, presented at the 2020 NADCA Executive Conference, Clearwater Beach, FL, 25 Feb 2020.

  26. T. Prucha, From the editor: AI needs CSI: common sense input. Int. J. Met. 12(3), 425–426 (2018). https://doi.org/10.1007/s40962-018-0235-2

    Article  Google Scholar 

  27. R. Blagus, L. Lusa, SMOTE for high-dimensional class-imbalanced data. BMC Bioinform. 14(1), 106 (2013). https://doi.org/10.1186/1471-2105-14-106

    Article  Google Scholar 

  28. H. Han, W.-Y. Wang, B.-H. Mao, Borderline-SMOTE: A new over-sampling method in imbalanced data sets learning, in Advances in Intelligent Computing, vol. 3644, D.-S. Huang, X.-P. Zhang, and G.-B. Huang, Eds. Berlin: Springer, 2005, pp. 878–887.

  29. A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A.A. Bharath, Generative adversarial networks: an overview. IEEE Signal Process. Mag. 35(1), 53–65 (2018). https://doi.org/10.1109/MSP.2017.2765202

    Article  Google Scholar 

  30. I. Goodfellow, NIPS 2016 Tutorial: Generative Adversarial Networks, ArXiv170100160 Cs, Apr. 2017. Accessed 27 May 2020. [Online]. Available: https://arxiv.org/abs/1701.00160.

  31. F. Pedregosa et al., Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011). https://doi.org/10.1016/j.patcog.2011.04.006

    Article  Google Scholar 

  32. A. Geron, Hands-On Machine Learning with Scikit-Learn and Tensor Flow, 1st edn. (O’Reilly, Beijing, 2017)

    Google Scholar 

  33. J.D. Hunter, Matplotlib: a 2D graphics environment. Comput. Sci. Eng. 9(3), 90–95 (2007). https://doi.org/10.1109/MCSE.2007.55

    Article  Google Scholar 

  34. The pandas development team, pandas-dev/pandas: Pandas. Zenodo, 2020.

  35. W. McKinney, Data structures for statistical computing in Python, in Proceedings of the 9th Python in Science Conference, 2010, pp. 51–56, Accessed 09 Jan 2020. [Online]. Available: https://conference.scipy.org/proceedings/scipy2010/pdfs/mckinney.pdf.

  36. G. Van Rossum, F.L. Drake, Python 3 Reference Manual (CreateSpace, Scotts Valley, CA, 2009)

    Google Scholar 

  37. T.E. Oliphant, Python for scientific computing. Comput. Sci. Eng. 9(3), 10–20 (2007). https://doi.org/10.1109/MCSE.2007.58

    Article  CAS  Google Scholar 

  38. T. Wuest, D. Weimer, C. Irgens, K.-D. Thoben, Machine learning in manufacturing: advantages, challenges, and applications. Prod. Manuf. Res. 4(1), 23–45 (2016). https://doi.org/10.1080/21693277.2016.1192517

    Article  Google Scholar 

  39. C. Eckart, G. Young, The approximation of one matrix by another of lower rank. Psychometrika 1(3), 211–218 (1936). https://doi.org/10.1007/BF02288367

    Article  Google Scholar 

  40. H. Abdi, L.J. Williams, Principal component analysis: principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2(4), 433–459 (2010). https://doi.org/10.1002/wics.101

    Article  Google Scholar 

  41. S. Wold, K. Esbensen, P. Geladi, Principal component analysis. Chemom. Intell. Lab. Syst. 2, 37–52 (1987). https://doi.org/10.1016/0169-7439(87)80084-9

    Article  CAS  Google Scholar 

  42. M. Pal, Random forest classifier for remote sensing classification. Int. J. Remote Sens. 26(1), 217–222 (2005). https://doi.org/10.1080/01431160412331269698

    Article  Google Scholar 

  43. R.E. Wright, Logistic regression, in Reading and Understanding Multivariate Statistics, Washington, DC, US: American Psychological Association, 1995, pp. 217–244.

  44. D. Dietrich, B. Heller, B. Yang, Data Science and Big Data Analytics: Discovering, Analyzing, Visualizing and Presenting Data, 1st edn. (Wiley, Hoboken, 2015)

    Google Scholar 

  45. A. Altmann, L. Toloşi, O. Sander, T. Lengauer, Permutation importance: a corrected feature importance measure. Bioinformatics 26(10), 1340–1347 (2010). https://doi.org/10.1093/bioinformatics/btq134

    Article  CAS  Google Scholar 

  46. T. Chen, C. Guestrin, XGBoost: a scalable tree boosting system. in Proc. 22nd ACM SIGKDD Int. Conf. Knowl. Discov. Data Min., pp. 785–794, Aug. 2016, https://doi.org/10.1145/2939672.2939785.

  47. National Research Council, Frontiers in Massive Data Analysis (National Academies Press, Washington, D.C., 2013)

    Google Scholar 

  48. C.P. Snow, The Two Cultures (Cambridge University Press, London, 1959)

    Google Scholar 

Download references

Acknowledgements

The authors would like to thank the ACRC consortium members for their support and data for this project.

Author information

Authors and Affiliations

  1. Metso, Shrewsbury, MA, 01545, USA

    Ning Sun

  2. Mercury Marine, Fond du Lac, WI, 54935, USA

    Adam Kopper

  3. Data Science, WPI, Worcester, MA, 01609, USA

    Rasika Karkare

  4. Mathematical Sciences, Computer Science, Data Science, WPI, Worcester, MA, 01609, USA

    Randy C. Paffenroth

  5. Materials Science and Engineering, UCI, Irvine, CA, 92967, USA

    Diran Apelian

Authors
  1. Ning Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Adam Kopper
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rasika Karkare
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Randy C. Paffenroth
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Diran Apelian
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Diran Apelian.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sun, N., Kopper, A., Karkare, R. et al. Machine Learning Pathway for Harnessing Knowledge and Data in Material Processing. Inter Metalcast 15, 398–410 (2021). https://doi.org/10.1007/s40962-020-00506-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40962-020-00506-2

Keywords

Search

Navigation