Skip to main content
SpringerLink
Log in

Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing

  • Technical Article
  • Published:
Integrating Materials and Manufacturing Innovation Aims and scope Submit manuscript

Abstract

Integrated computational materials engineering (ICME) methods combining CALPHAD with process-based simulations can produce rich, high-dimensional data for alloy and process design. In ICME methods for metallurgical applications, the visualization and interpretation of such high-dimensional data has previously been through heat maps represented in 2 or 3 dimensions. While such an approach is ideal when one variable is varied at a time, in the case of high-dimensional data with multiple variables varied simultaneously, as is the case in additive manufacturing, interpreting the trends through two- or three-dimensional heat maps becomes challenging. Here, we propose a strategy of mixed visual data mining and quantitative analysis for high-dimensional metallurgical and process data using high-throughput thermodynamic calculations. Two case studies show the application of the proposed approach. The first case study investigated the effects of feedstock chemistry on the \(\delta\) ferrite formation in 316L stainless steel powders used for binder jet additive manufacturing. The second case study linked Scheil–Gulliver calculations to a process model for dissimilar joining of aluminum alloys 5356 and 6111 during laser hot-wire additive manufacturing. Both cases contained thousands of calculated data points, showcasing the utility of visual data analysis through parallel coordinate plotting, Pearson correlation coefficient matrices, and scatter matrices compared to traditional process maps. These visualization techniques can be extended to many additive manufacturing problems to capture process–structure–property relationships for additively manufactured components.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Tan C, Wang D, Ma W, Chen Y, Chen S, Yang Y, Zhou K (2020) Design and additive manufacturing of novel conformal cooling molds. Mater Des 196:109147

    Article  Google Scholar 

  2. Mazur M, Leary M, McMillan M, Elambasseril J, Brandt M (2016) Slm additive manufacture of h13 tool steel with conformal cooling and structural lattices. Rapid Prototyp J 22(3):504–518

    Article  Google Scholar 

  3. Bajaj P, Hariharan A, Kini A, Kürnsteiner P, Raabe D, Jägle EA (2020) Steels in additive manufacturing: a review of their microstructure and properties. Mater Sci Eng, A 772:138633

    Article  CAS  Google Scholar 

  4. Zuback JS, Moradifar P, Khayat Z, Alem N, Palmer TA (2019) Impact of chemical composition on precipitate morphology in an additively manufactured nickel base superalloy. J Alloy Compd 798:446–457

    Article  CAS  Google Scholar 

  5. Nandwana P, Elliott AM, Siddel D, Merriman A, Peter WH, Babu SS (2017) Powder bed binder jet 3D printing of Inconel 718: densification, microstructural evolution and challenges. Curr Opin Solid State Mater Sci 21(4):207–218

    Article  CAS  Google Scholar 

  6. Yang S, Lu J, Xing F, Zhang L, Zhong Yu (2020) Revisit the VEC rule in high entropy alloys (HEAs) with high-throughput CALPHAD approach and its applications for material design-A case study with Al-Co-Cr-Fe-Ni system. Acta Mater 192:11–19

    Article  CAS  Google Scholar 

  7. Xiong W, Olson GB (2015) Integrated computational materials design for high-performance alloys. MRS Bull 40(12):1035–1043

    Article  Google Scholar 

  8. Gorsse S, Tancret F (2018) Current and emerging practices of CALPHAD toward the development of high entropy alloys and complex concentrated alloys. J Mater Res 33(19):2899–2923

    Article  CAS  Google Scholar 

  9. Van De Walle A, Asta M (2019) High-throughput calculations in the context of alloy design. MRS Bull 44(4):252–256

    Article  Google Scholar 

  10. Shi R (2020) Applications of CALPHAD (CALculation of PHAse diagram) modeling in organic orientationally disordered phase change materials for thermal energy storage. Thermochimica Acta 683:178461

    Article  CAS  Google Scholar 

  11. Wang X, Sridar S, Xiong W (2020) Thermodynamic investigation of new high-strength low-alloy steels with heusler phase strengthening for welding and additive manufacturing: high-throughput CALPHAD calculations and key experiments for database verification. J Phase Equilib Diffus 41(6):804–818

    Article  Google Scholar 

  12. Zhang C, Jiang X, Zhang R, Wang X, Yin H, Xuanhui Q, Liu Z-K (2019) High-throughput thermodynamic calculations of phase equilibria in solidified 6016 al-alloys. Comput Mater Sci 167:19–24

    Article  CAS  Google Scholar 

  13. Yang S, Jun L, Xing F, Zhang L, Zhong Yu (2020) Revisit the vec rule in high entropy alloys (heas) with high-throughput calphad approach and its applications for material design-a case study with al-co-cr-fe-ni system. Acta Mater 192:11–19

    Article  CAS  Google Scholar 

  14. Inselberg A (2009) Parallel coordinates: visual multidimensional geometry and its applications, vol 20. Springer Science and Business Media, Berlin

    Book  Google Scholar 

  15. Steed CA , Swan JE, Jankun-Kelly TJ, Fitzpatrick PJ (2009) Guided analysis of hurricane trends using statistical processes integrated with interactive parallel coordinates. In 2009 IEEE symposium on visual analytics science and technology, pp 19–26. IEEE

  16. Steed CA, Ricciuto DM, Shipman G, Smith B, Thornton PE, Wang D, Shi X, Williams DN (2013) Big data visual analytics for exploratory earth system simulation analysis. Comput Geosci 61:71–82

    Article  Google Scholar 

  17. Rickman JM (2018) Data analytics and parallel-coordinate materials property charts. npj Comput Mater 4(1):1–8

    Article  CAS  Google Scholar 

  18. Rickman JM, Lookman T, Kalinin SV (2019) Materials informatics: from the atomic-level to the continuum. Acta Mater 168:473–510

    Article  CAS  Google Scholar 

  19. Dehoff RR, Kirka MM, Ellis E, Paquit VC, Nandwana P, Plotkowski AJ (2019) Electron beam melting technology improvements. Technical report, Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

  20. Kamath C, El-Dasher B, Gallegos GF, King WE, Sisto A (2014) Density of additively-manufactured, 316L SS parts using laser powder-bed fusion at powers up to 400 W. Int J Adv Manuf Technol 74(1):65–78

    Article  Google Scholar 

  21. German RM (1997) Supersolidus liquid-phase sintering of prealloyed powders. Metall Mater Trans A Phys Metall Mater Sci 28(7):1553–1567

    Article  Google Scholar 

  22. German RM (2003) An update on the theory of supersolidus liquid phase sintering. Proc Sinter

  23. Liu J, German RM (1999) Densification and shape distortion in liquid-phase sintering. Metall Mater Trans A 30(12):3211–3217

    Article  Google Scholar 

  24. Liu ZY, Loh NH, Khor KA, Tor SB (2000) Sintering of injection molded M2 high-speed steel. Mater Lett 45(1):32–38

    Article  CAS  Google Scholar 

  25. Levasseur D, Brochu M (2016) Supersolidus liquid phase sintering modeling of inconel 718 superalloy. Metall Mater Trans A 47(2):869–876

    Article  CAS  Google Scholar 

  26. Nandwana P, Kannan R, Siddel D (2020) Microstructure evolution during binder jet additive manufacturing of H13 tool steel. Addit Manuf 36:101534

    CAS  Google Scholar 

  27. Muterlle PV, Zendron M, Perina M, Molinari A (2009) Influence of delta ferrite on mechanical properties of stainless steel produced by MIM. 20th international congress of mechanical engineering, pp 1–6

  28. Frykholm R, Takeda Y, Andersson BG, Carlstrom R (2016) Solid state sintered 3-D printing component by using inkjet (binder) method. J Jpn Soc Powder Powder Metall 63(7):421–426

    Article  CAS  Google Scholar 

  29. Luo C, Lai Z, Zhang Y (2020) Improvement of mechanical properties of dissimilar spot-welded joints of additively manufactured stainless steels. J Manuf Process 54:210–220

    Article  Google Scholar 

  30. Mirzababaei S, Pasebani S (2019) A review on binder jet additive manufacturing of 316L stainless steel. J Manuf Mater Process 3(3):82

    CAS  Google Scholar 

  31. Do T, Bauder TJ, Suen H, Rego K, Yeom J, Kwon P (2018) Additively manufactured full-density stainless steel 316L with binder jet printing. In ASME 2018 13th international manufacturing science and engineering conference, MSEC 2018, vol 1. American Society of Mechanical Engineers (ASME)

  32. Schäfer L (1998) Influence of delta ferrite and dendritic carbides on the impact and tensile properties of a martensitic chromium steel. J Nucl Mater 258–263(PART 2 B):1336–1339

    Article  Google Scholar 

  33. ASTM 240A. ASTM A 240/A 240M - 04a (2004) Standard specification for chromium and chromium-nickel stainless steel plate, sheet, and strip for pressure vessels and for general applications 1. ASTM, i

  34. Schaeffler AL (1949) Constitution diagram for stainless steel weld metal. Metal Prog 56(11):680-680B

    CAS  Google Scholar 

  35. DeLong WT (1974) Ferrite in austenitic stainless steel weld metal. Weld J 53(7):273–286

    Google Scholar 

  36. Kotecki DJ, Siewert TA (1992) Wrc-1992 constitution diagram for stainless steel weld metals: a modification of the wrc-1988 diagram. Weld J 71(5):171–178

    Google Scholar 

  37. Plotly Technologies Inc. Collaborative data science, 2015

  38. Saboori A, Aversa A, Marchese G, Biamino S, Lombardi M, Fino P (2019) Application of directed energy deposition-based additive manufacturing in repair. Appl Sci 9(16):3316

    Article  CAS  Google Scholar 

  39. Li S, Xu W, Xiao G, Chen B (2018) Weld formation in laser hot-wire welding of 7075 aluminum alloy. Metals 8(11):909

    Article  CAS  Google Scholar 

  40. Lin C, Chunming W, Lingda X, Xiong Z, Gaoyang M (2020) Microstructural, porosity and mechanical properties of lap joint laser welding for 5182 and 6061 dissimilar aluminum alloys under different place configurations. Mater Des 191:108625

    Article  Google Scholar 

  41. Gan Z, Gang Yu, He X, Li S (2017) Surface-active element transport and its effect on liquid metal flow in laser-assisted additive manufacturing. Int Commun Heat Mass Transfer 86:206–214

    Article  CAS  Google Scholar 

  42. The Aluminum Association (2018) International alloy designations and chemical composition limits for wrought aluminum and wrought aluminum alloys. Report, 2018

  43. Kou S (2015) A criterion for cracking during solidification. Acta Mater 88:366–374

    Article  CAS  Google Scholar 

  44. Bocklund B, Bobbio LD, Otis RA, Beese AM, Liu Z-K (2020) Experimental validation of Scheil-Gulliver simulations for gradient path planning in additively manufactured functionally graded materials. Materialia 11:100689

    Article  CAS  Google Scholar 

  45. Rosenthal D (1941) Mathematical theory of heat distribution during welding and cutting. Weld J 20:220–234

    Google Scholar 

  46. Rappaz M, Drezet J-M, Gremaud M (1999) A new hot-tearing criterion. Metall and Mater Trans A 30(2):449–455

    Article  Google Scholar 

  47. Waskom ML (2021) Seaborn: statistical data visualization. J Open Source Softw 6(60):3021

    Article  Google Scholar 

  48. Hunter JD (2007) Matplotlib: A 2D graphics environment. Comput Sci Eng 9(3):90–95

    Article  Google Scholar 

  49. Berube P (2018) Weldability of aluminum alloys, vol 2A, Aluminum Science and Technology

  50. Dausinger F (2000) Laser welding of aluminum alloys: from fundamental investigation to industrial application. In: High-power lasers in manufacturing. vol 3888, pp 367–379. International Society for Optics and Photonics

  51. Lobanov L (1997) Welded structures. Taylor and Francis, Oxfordshire

    Google Scholar 

Download references

Acknowledgements

Research was performed at the U.S. Department of Energy’s Manufacturing Demonstration Facility, located at Oak Ridge National Laboratory. Research was co-sponsored by the U.S. Department of Energy, Office of Energy Efficiency and Renewable Energy, Advanced Manufacturing Office, Vehicle Technologies Office. This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy.

Author information

Authors and Affiliations

  1. Materials Science and Technology Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Rangasayee Kannan, Gerald L. Knapp, Peeyush Nandwana, Alex Plotkowski, Benjamin Stump & Ying Yang

  2. Manufacturing Science Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Ryan Dehoff

  3. Energy Systems Analytics Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA

    Vincent Paquit

Authors
  1. Rangasayee Kannan
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gerald L. Knapp
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peeyush Nandwana
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ryan Dehoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Alex Plotkowski
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Benjamin Stump
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ying Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Vincent Paquit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Rangasayee Kannan or Peeyush Nandwana.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher’s Note

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

Notice of Copyright: This manuscript has been authored by UT-Battelle, LLC under Contract No. DE-AC05-00OR22725 with the U.S. Department of Energy. The US Government retains and the publisher, by accepting the article for publication, acknowledges that the United States Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this manuscript, or allow others to do so, for United States Government purposes. The Department of Energy will provide public access to these results of federally sponsored research in accordance with the DOE Public Access Plan (http://energy.gov/downloads/doe-public-access-plan).

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kannan, R., Knapp, G.L., Nandwana, P. et al. Data Mining and Visualization of High-Dimensional ICME Data for Additive Manufacturing. Integr Mater Manuf Innov 11, 57–70 (2022). https://doi.org/10.1007/s40192-021-00243-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40192-021-00243-2

Keywords

Search

Navigation