Explaining hardness modeling with XAI of C45 steel spur-gear induction hardening
Source abstract
Abstract
This work presents an interpretability study with XAI tools to explain an XGBoost model for hardness prediction in the simultaneous double-frequency induction hardening. Experiments were carried out on C45 steel spur-gear. In order to explain the model, firstly, the built-in tool of the XGBoost library was used to interpret the feature importance. Then, a more advanced approach with the SHAP library was employed to highlight local and global explanations. Finally, the implementation of an interpretable surrogate model allowed to illustrate rules for prediction, making the explanation, although approximate, clear. This study proposes a relevant approach of AI to explain the results obtained by black box models which is currently a major element for the industry allowing to justify the quality of the results in a clear way. It is concluded that the model is consistent with physical principles.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Hydrogen embrittlement and corrosion behaviors in metals
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (37)
Show source-provided references
1. Rokicki P (2017) Induction hardening of tool steel for heavily loaded aircraft engine components. Arch Metall Mate 62:315–32010.1515/amm-2017-0047 ↗
2. Candeo A, Ducassy C, Bocher P, Dughiero F (2011) modeling of induction hardening of ring gears for the aerospace industry. IEEE Trans Magn 47(5):918–92110.1109/TMAG.2010.2073682 ↗
3. Rudnev, V, Loveless, D and Cook, R (2017) Handbook of Induction Heating. CRC Press, 2nd ed10.1201/9781315117485 ↗
4. Qiu G, Zhan D, Li C, Yang Qi, M, Jiang, Z and Zhang, H, (2020) Effects of yttrium and heat treatment on the microstructure and mechanical properties of clam steel. J of Materi Eng and Perform 29(1):42–5210.1007/s11665-020-04574-7 ↗
5. Celada-Casero C, Huang B, Yang J-R, San-Martin D (2019) Microstructural mechanisms controlling the mechanical behaviour of ultrafine grained martensite/austenite microstructures in a metastable stainless steel. Materials & Design 181:10792210.1016/j.matdes.2019.107922 ↗
6. Hömberg D, Liu Q, Montalvo-Urquizo J, Nadolski D, Petzold T, Schmidt A, Schulz A (2016) Simulation of multi-frequency-induction-hardening including phase transitions and mechanical effects. Finite Elem Anal Des 121:86–10010.1016/j.finel.2016.07.012 ↗
7. Maresca F, Kouznetsova V, Geers M, Curtin W (2018) Contribution of austenite-martensite transformation to deformability of advanced high strength steels: from atomistic mechanisms to microstructural response. Acta Mater 156:463–47810.1016/j.actamat.2018.06.028 ↗
8. Xu S, Li J, Cui Y, Zhang Y, Sun L, Li J, Luan J, Jiao Z, Wang X-L, Liu C et al (2020) Mechanical properties and deformation mechanisms of a novel austenite-martensite dual phase steel. International Journal of Plasticity 128:10267710.1016/j.ijplas.2020.102677 ↗
9. Wuppermann, C and Míček, E (2018) Importance of heat treatment for the variety of applications of modern materials. Prozesswarme, pp 95–101
10. Hömberg D (2004) A mathematical model for induction hardening including mechanical effects. NONLINEAR ANAL-REAL WORLD APP 5:55–9010.1016/S1468-1218(03)00017-8 ↗
11. Spezzapria M, Dughiero F, Forzan M, Candeo A (2012) Multiphysics fem simulation of contour induction hardening process on aeronautical gears. J Iron Steel Res 19:95–98
12. Wang K, Chandrasekar S, Yang H (1992) Finite-element simulation of induction heat treatment. J of Materi Eng and Perform 1:97–11210.1007/BF02650041 ↗
13. Samiuddin M, Muzamil M (2016) Experimental investigation and optimization of process parameters for through induction hardening using factorial design of experiments. Journal of Engineering Research 5:174–185
14. Langer M, Oster D, Speith T, Hermanns H, Kästner L, Schmidt E, Sesing A, Baum K (2021) What do we want from explainable artificial intelligence (xai)?-a stakeholder perspective on xai and a conceptual model guiding interdisciplinary xai research. Artif Intell 296:10347310.1016/j.artint.2021.103473 ↗
15. Das, A and Rad, P (2020) Opportunities and challenges in explainable artificial intelligence (xai): A survey. arXiv preprint arXiv:2006.11371
16. Tjoa, E and Guan, C (2019) A survey on explainable artificial intelligence (XAI): towards Medical XAI, pp 21
17. Loyola-Gonzalez O (2019) Black-box vs. white-box: Understanding their advantages and weaknesses from a practical point of view. IEEE Access 7:154096–15411310.1109/ACCESS.2019.2949286 ↗
18. Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6:52138–5216010.1109/ACCESS.2018.2870052 ↗
19. Guidotti R, Monreale A, Ruggieri S, Turini F, Giannotti F, Pedreschi D (2019) A survey of methods for explaining black box models. ACM Comput Surv 51:1–4210.1145/3236009 ↗
20. García MV, Aznarte JL (2020) Shapley additive explanations for no2 forecasting. Ecological Informatics 56:10103910.1016/j.ecoinf.2019.101039 ↗
21. Abdollahi A, Pradhan B (2021) Urban vegetation mapping from aerial imagery using explainable ai (xai). Sensors 21(14):473810.3390/s21144738 ↗
22. Dikshit A, Pradhan B (2021) Interpretable and explainable ai (xai) model for spatial drought prediction. Sci Total Environ 801:14979710.1016/j.scitotenv.2021.149797 ↗
23. Yoo S, Kang N (2021) Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization. Expert Syst Appl 183:11543010.1016/j.eswa.2021.115430 ↗
24. Jeon, J, Seo, N, Jung, J-G, Son, SB and Lee, S-J (2022) Prediction and mechanism explain of austenite-grain growth during reheating of alloy steel using xai. Journal of Materials Research and Technology10.1016/j.jmrt.2022.09.119 ↗
25. Garois S, Daoud M, Traidi K, et al (2023) Artificial intelligence modeling of induction contour hardening of 300M steel bar and C45 steel spur-gear. Int J Mater Form 16:26. https://doi.org/10.1007/s12289-023-01748-110.1007/s12289-023-01748-1 ↗
26. Chen, T, He, T, Benesty, M, Khotilovich, V, Tang, Y, Cho, H, Chen, K et al (2015) Xgboost: extreme gradient boosting. R package version 0.4-2, 1(4):1–4
27. ISO, E, (2018) 6507–1: 2018-metallic materials-vickers hardness test-part 1: Test method. Geneva, Switzerland, ISO
28. Zheng, H, Yuan, J and Chen, L (2017) Short-term load forecasting using emd-lstm neural networks with a xgboost algorithm for feature importance evaluation. Energies, 10(8)116810.3390/en10081168 ↗
29. Goutte, C and Gaussier, E (2005) A probabilistic interpretation of precision, recall and f-score, with implication for evaluation. In: European conference on information retrieval, pp 345–359. Springer10.1007/978-3-540-31865-1_25 ↗
30. Ribeiro, MT, Singh, S and Guestrin, C (2016) Why Should I Trust You?: Explaining thepredictions of any classifier. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, (San Francisco California USA), pp 1135–1144, ACM10.1145/2939672.2939778 ↗
31. Guidotti, R, Monreale, A, Ruggieri, S, Pedreschi, D, Turini, F and Giannotti, F (2018) Local rule-based explanations of black box decision systems. arXiv:1805.10820
32. Lundberg, SM and Lee, S-I (2017) A Unified Approach to Interpreting Model Predictions. In: Advances in neural information processing systems, 30, Curran Associates, Inc
33. Lundberg, SM, Erion, GG and Lee, S-I (2019) Consistent Individualized Feature Attribution for Tree Ensembles. arXiv:1802.03888
34. Azodi CB, Tang J, Shiu S-H (2020) Opening the black box: interpretable machine learning for geneticists. Trends Genet 36(6):442–45510.1016/j.tig.2020.03.005 ↗
35. Quinlan JR (1996) Learning decision tree classifiers. ACM Computing Surveys (CSUR) 28(1):71–7210.1145/234313.234346 ↗
36. Li J, Cao Z, Liu L, Liu X, Peng J (2021) Effect of Microstructure on Hardness and Wear Properties of 45 Steel after Induction Hardening. Steel Research International 92:200054010.1002/srin.202000540 ↗
37. Derouiche K, Garois S, Champaney V, Daoud M, Traidi K, Chinesta F (2021) Data-driven modeling for multiphysics parametrized problems-application to induction hardening process. Metals 11(5):73810.3390/met11050738 ↗