Abstract
While experimental high-throughput and computational methods exist for the development of functional materials, structural materials are still being developed on the base of experience, stepwise prediction and punctual support of computational models. As a result, many major breakthroughs have been and still are achieved by coincidence under non-intuitive conditions. Experimental high throughput methods allow to explore large process windows where no prediction is possible due to lack of existent data. This work proposes the high throughput method “Farbige Zustände” as a novel approach for the experimental exploration of structural materials. New methods for sample synthesis, treatment and characterization are developed as well as computational methods for ad-hoc data analysis, search and experiment planning.
Kurzfassung
Während neue Funktionswerkstoffe heutzutage mit experimentellen Hochdurchsatz- und Berechnungsmethoden entwickelt werden, findet die Suche nach neuen Konstruktionswerkstoffen auch heute noch auf der Basis von Erfahrung, schrittweiser Prädiktion und lokal unterstützenden Berechnungsmodellen statt. Folglich waren und sind immer noch viele Durchbrüche in der Werkstoffentwicklung Zufallsentdeckungen unter nicht-intuitiven Bedingungen. Experimentelle Hochdurchsatzmethoden erlauben die Exploration weiter Prozessfenster, in denen aufgrund fehlenden Wissens noch keine Vorhersagen für eine schrittweise Vorgehensweise möglich sind. Diese Arbeit schlägt die neuartige Methode „Farbige Zustände“ für die experimentelle Hochdurchsatz-Exploration von Konstruktionswerkstoffen vor, die ein spezifisches Anforderungsprofil erfüllen. Neue Methoden für die Probensynthese, deren thermische und mechanische Behandlung sowie deren Charakterisierung werden ebenso entwickelt wie Methoden zur Ad-hoc-Datenanalyse, Suche und Versuchsplanung.
References
1. Crane, F. A. A.; Charles, J. A.; Furness, J.: Selection and use of engineering materials. Butterworth-Heinemann, Oxford, UK, 1997Search in Google Scholar
2. Thelning, K.-E.: Steel and its heat treatment. Butterworth-Heinemann, Oxford, UK, 2013Search in Google Scholar
3. Hoddeson, L.; Braun, E.; Teichmann, J.; Weart, S.: Out of the crystal maze: chapters from the history of solid state physics. Oxford University Press, Oxford, UK, 199210.1119/1.17165Search in Google Scholar
4. Cobb, H. M.: The history of stainless steel. ASM Int., Materials Park, Ohio, USA, 201010.31399/asm.tb.hss.9781627083560Search in Google Scholar
5. Hornbogen, E.: Hundred years of precipitation hardening. J. Light Metals1 (2001), pp. 127–132, 10.1016/s1471-5317(01)00006-2Search in Google Scholar
6. García, J. M.; Jones, G. O.;Virwani, K.; McCloskey, B. D.; Boday, D. J.; ter Huurne, G. M.; Horn, H. W.; Coady, D. J.; Bintaleb, A. M.; Alabdulrahman, A. M.: Recyclable, strong thermosets and organogels via paraformaldehyde condensation with diamines. Science344 (2014), pp. 732–735, 10.1126/science.1251484Search in Google Scholar PubMed
7. Zhu, H.; Hautier, G.; Aydemir, U.; Gibbs, Z. M.; Li, G.; Bajaj, S.; Pöhls, J.-H.; Broberg, D.; Chen, W.; Jain, A.: Correction: Computational and experimental investigation of TmAgTe 2 and XYZ 2 compounds, a new group of thermoelectric materials identified by first-principles high-throughput screening. J. Mater. Chem. C4 (2016), pp. 4331–4331, 10.1039/c6tc90077aSearch in Google Scholar
8. Urban, A.; Seo, D.-H.; Ceder, G.: Computational understanding of Li-ion batteries. Npj Comp. Mater. 2 (2016) 16002, 10.1038/npjcompumats.2016.2Search in Google Scholar
9. Jain, A.; Hautier, G.; Moore, C. J.; Ong, S. P.; Fischer, C. C.; Mueller, T.; Persson, K. A.; Ceder, G.: A high-throughput infrastructure for density functional theory calculations. Comp. Mater. Sci. 50 (2011), pp. 2295–2310, 10.1016/j.commatsci.2011.02.023Search in Google Scholar
10. Cui, J.; Chu, Y. S.; Famodu, O. O.; Furuya, Y.; Hattrick-Simpers, J.; James, R. D.; Ludwig, A.; Thienhaus, S.; Wuttig, M.; Zhang, Z.: Combinatorial search of thermoelastic shape-memory alloys with extremely small hysteresis width. Nature mater. 5 (2006), pp. 286–290, 10.1038/nmat1593Search in Google Scholar PubMed
11. Ding, S.; Liu, Y.; Li, Y.; Liu, Z.; Sohn, S.; Walker, F. J.; Schroers, J.: Combinatorial development of bulk metallic glasses. Nature mater. 13 (2014), pp. 494–500, 10.1038/nmat3939Search in Google Scholar PubMed
12. Otani, M.; Itaka, K.; Wong-Ng, W.; Schenck, P.; Koinuma, H.: Development of a high-throughput thermoelectric screening tool for combinatorial thin film libraries. Appl. Surf. Sci. 254 (2007), pp. 765–767, 10.1016/j.apsusc.2007.05.091Search in Google Scholar
13. Zhao, J.-C.: The diffusion-multiple approach to designing alloys. Annu. Rev. Mater. Res. 35 (2005), pp. 51–73, 10.1146/annurev.matsci.35.100303.111314Search in Google Scholar
14. Zhao, J.-C.: Combinatorial approaches as effective tools in the study of phase diagrams and composition–structure–property relationships. Progr. Mater. Sci. 51 (2006), pp. 557–631, 10.1016/j.pmatsci.2005.10.001Search in Google Scholar
15. Zhao, J.-C.; Zheng, X.; Cahill, D. G.: High-throughput measurements of materials properties, JOM63 (2011), pp. 40–44, 10.1007/s11837-011-0044-zSearch in Google Scholar
16. Springer, H.; Raabe, D.: Rapid alloy prototyping: Compositional and thermo-mechanical high throughput bulk combinatorial design of structural materials based on the example of 30Mn–1.2 C–xAl triplex steels. Acta Mat. 60 (2012), pp. 4950–4959, 10.1016/j.actamat.2012.05.017Search in Google Scholar
17. Ciftci, N.; Ellendt, N.; von Bargen, R.; Henein, H.; Mädler, L.; Uhlenwinkel, V.: Atomization and characterization of a glass forming alloy {(Fe0.6Co0.4)0.75B0.2Si0.05}96 Nb4. J. Non-Cryst. Solids394 (2014), pp. 36–42, 10.1016/j.jnoncrysol.2014.03.023Search in Google Scholar
18. Ellendt, N.; Ciftci, N.; Goodreau, C.; Uhlenwinkel, V.; Mädler, L.: Solidification of single droplets under combined cooling conditions. IOP Conf. Ser.: Mat. Sci. Eng. 117 (2016), 012057, 10.1088/1757-899X/117/1/012057Search in Google Scholar
19. Cheng, S.; Chandra, S.: A pneumatic droplet-on-demand generator. Experiments in Fluids34 (2003), pp. 755–762, 10.1007/s00348-003-0629-6Search in Google Scholar
20. Xia, T.; Malasarn, D.; Lin, S.; Ji, Z.; Zhang, H.; Miller, R. J.; Keller, A. A.; Nisbet, R. M.; Harthorn, B. H.; Godwin, H. A.; Lenihan, H. S.; Liu, R.; Gardea-Torresdey, J.; Cohen, Y.; Mädler, L.; Holden, P. A.; Zink, J. I.; Nel, A. E.: Implementation of a multidisciplinary approach to solve complex nano EHS problems by the UC Center for the Environmental Implications of Nanotechnology. Small9 (2013), pp. 1428–1443, 10.1002/smll.201201700Search in Google Scholar PubMed
21. Ellendt, N.; Uhlenwinkel, V.; Mädler, L.: High yield spray forming of small diameter tubes using pressure-gas-atomization. Mat.-Wiss. u. Werkstofftech. 45 (2014), pp. 699–707, 10.1002/mawe.201400306Search in Google Scholar
22. Pintaúde, G.; di V. Cuppari, M. G; Schön C. G.; Sinatora, A.; Souza, R. M.: A review on the reverse analysis for the extraction of mechanical properties using instrumented Vickers indentation. Z. f. Metallkd. 96 (2005), pp. 1252–1255, 10.3139/146.101170Search in Google Scholar
23. Epp, J.; Surm, H.; Hirsch, T.; Hoffmann, F.: Residual stress relaxation during heating of bearing rings produced in two different manufacturing chains. J. Mater. Process. Technol. 211 (2011), pp. 637–643, 10.1016/j.jmatprotec.2010.11.022Search in Google Scholar
24. Vashista, M.; Yusufzai, M. Z. K.: Correlation between full width at half maximum (FWHM) of XRD peak with mechanical properties. IJMA1 (2015), pp. 15–23Search in Google Scholar
25. Zhang, P.; Li, S. X.; Zhang, Z. F.: General relationship between strength and hardness. Materials Science and Engineering a-Structural Materials Properties Microstruct. Process. 529 (2011), pp. 62–73, 10.1016/j.msea.2011.08.061Search in Google Scholar
26. Kramer, H. S.; Starke, P.; Klein, M.; Eifler, D.: Cyclic hardness test PHYBALCHT – Short-time procedure to evaluate fatigue properties of metallic materials. Int. J. Fatigue63 (2014), pp. 78–84, 10.1016/j.ijfatigue.2014.01.009Search in Google Scholar
27. Vollertsen, F.; Niehoff, H. S.; Wielage, H.: On the acting pressure in laser deep drawing. Prod. Eng. 3 (2009), pp. 1–8, 10.1007/s11740-008-0135-zSearch in Google Scholar
28. Arrazola, P. J.; Özel, T.; Umbrello, D.; Davies, M.; Jawahir, I. S.: Recent advances in modelling of metal machining processes. CIRP Ann. 62 (2013), pp. 695–718, 10.1016/j.cirp.2013.05.006Search in Google Scholar
29. Brinksmeier, E.; Riemer, O.; Stern, R.: Machining of precision parts and microstructures. In: Initiatives of Precision Engineering at the Beginning of a Millennium, Inasaki, I. (eds), 2002, pp. 3–11, 10.1007/0-306-47000-4_1Search in Google Scholar
30. Dobmann, G.; Kröning, M.; Theiner, W.; Willems, H.; Fiedler, U.: Nondestructive characterization of materials (ultrasonic and micromagnetic techniques) for strength and toughness prediction and the detection of early creep damage. Nucl. Eng. Design157 (1995), pp. 137–158, 10.1016/0029-5493(95)00992-LSearch in Google Scholar
31. Punckt, C.; Bölscher, M.; Rotermund, H. H.; Mikhailov, A. S.; Organ, L.; Budiansky, N.; Scully, J. R.; Hudson, J. L.: Sudden onset of pitting corrosion on stainless steel as a critical phenomenon. Science305 (2004), pp. 1133–1136, 10.1126/science.1101358Search in Google Scholar PubMed
32. Munktell, S.; Nyholm, L.; Björefors, F.: Towards high throughput corrosion screening using arrays of bipolar electrodes. J. Electroanal. Chem. 747 (2015), pp. 77–82, 10.1016/j.jelechem.2015.04.008Search in Google Scholar
33. Ong, S. P.; Richards, W. D.; Jain, A.; Hautier, G.; Kocher, M.; Cholia, S.; Gunter, D.; Chevrier, V. L.; Persson, K. A.; Ceder, G.: Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comp. Mater. Sci. 68 (2013), pp. 314–319, 10.1016/j.commatsci.2012.10.028Search in Google Scholar
34. Drechsler, R.; Eggersglüß, S.; Ellendt, N.; Huhn, S.; Mädler, L.: Exploring superior structural materials using multi-objective optimization and formal techniques. Proc. 6th IEEE Int. Symp. on Embedded Computing & System Design (ISED), 15–17.12.16, Patna, India, IEEE (eds.), 2016, 10.1109/ISED.2016.7977046Search in Google Scholar
35. Kusne, A. G.; Gao, T.; Mehta, A.; Ke, L.; Nguyen, M. C.; Ho, K.-M.; Antropov, V.; Wang, C.-Z.; Kramer, M. J.; Long, C.; Takeuchi, I.: On-the-fly machine-learning for high-throughput experiments: search for rare-earth-free permanent magnets. Scientific Reports4 (2014), 6367, 10.1038/srep06367Search in Google Scholar PubMed PubMed Central
36. Onken, A.-K.; Bader, A.; Tracht, K.: Logistical Control of Flexible Processes in High-throughput Systems by Order Release and Sequence Planning. Proc. CIRP52 (2016), pp. 245–250, 10.1016/j.procir.2016.07.060Search in Google Scholar
37. Meiners, F.; Hogreve, S.; Tracht, K.: Boundary Conditions in Handling of Microspheres Induced by Shape Deviation Constraints. Proc. 2nd Congr. Montage Handhabung Industrieroboter, Berlin, T. Schüppstuhl, J. Franke, K. Tracht (eds.), Springer, Heidelberg, 2017, pp. 125–13310.1007/978-3-662-54441-9_13Search in Google Scholar
© 2018, Carl Hanser Verlag, München
This work is licensed under the Creative Commons Attribution 4.0 International License.