Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
International Journal of Material Forming
Published in: International Journal of Material Forming 5/2023

01-09-2023 | Original Research

Explaining hardness modeling with XAI of C45 steel spur-gear induction hardening

Authors: Sevan Garois, Monzer Daoud, Francisco Chinesta

Published in: International Journal of Material Forming | Issue 5/2023

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.
previous article 2S-ML: A simulation-based classification and regression approach for drawability assessment in deep drawing
next article Matching the mechanical system of metal forming equipment to reduce life cycle carbon emissions

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now
Appendix
Available only for authorised users
Literature
1.
Rokicki P (2017) Induction hardening of tool steel for heavily loaded aircraft engine components. Arch Metall Mate 62:315–320CrossRef
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):918–921CrossRef
3.
Rudnev, V, Loveless, D and Cook, R (2017) Handbook of Induction Heating. CRC Press, 2nd ed
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):42–52
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 181:107922CrossRef
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 121:86–100MathSciNetCrossRef
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 156:463–478CrossRef
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 128:102677CrossRef
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 5:55–90MathSciNetCrossRefMATH
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 1:97–112CrossRef
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 296:103473MathSciNetCrossRef
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 7:154096–154113CrossRef
18.
Adadi A, Berrada M (2018) Peeking inside the black-box: a survey on explainable artificial intelligence (xai). IEEE Access 6:52138–52160CrossRef
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 51:1–42CrossRef
20.
García MV, Aznarte JL (2020) Shapley additive explanations for no2 forecasting. Ecological Informatics 56:101039CrossRef
21.
Abdollahi A, Pradhan B (2021) Urban vegetation mapping from aerial imagery using explainable ai (xai). Sensors 21(14):4738CrossRef
22.
Dikshit A, Pradhan B (2021) Interpretable and explainable ai (xai) model for spatial drought prediction. Sci Total Environ 801:149797CrossRef
23.
Yoo S, Kang N (2021) Explainable artificial intelligence for manufacturing cost estimation and machining feature visualization. Expert Syst Appl 183:115430CrossRef
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 Technology
25.
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)1168
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. Springer
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, ACM
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.
34.
Azodi CB, Tang J, Shiu S-H (2020) Opening the black box: interpretable machine learning for geneticists. Trends Genet 36(6):442–455CrossRef
35.
Quinlan JR (1996) Learning decision tree classifiers. ACM Computing Surveys (CSUR) 28(1):71–72CrossRef
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 92:2000540CrossRef
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):738CrossRef
Metadata
Title
Explaining hardness modeling with XAI of C45 steel spur-gear induction hardening
Authors
Sevan Garois
Monzer Daoud
Francisco Chinesta
Publication date
01-09-2023
Publisher
Springer Paris
Published in
International Journal of Material Forming / Issue 5/2023
Print ISSN: 1960-6206
Electronic ISSN: 1960-6214
DOI
https://doi.org/10.1007/s12289-023-01780-1

Other articles of this Issue 5/2023

International Journal of Material Forming 5/2023 Go to the issue

Forming processes as a lever a for boosting the ecological transition

Matching the mechanical system of metal forming equipment to reduce life cycle carbon emissions

Original Research

Experimental and numerical investigation on the effect of rotational speed on exit-hole-free friction stir spot welding with consumable pin

Original Research

Characterising the shear resistance of a unidirectional non-crimp glass fabric using modified picture frame and uniaxial bias extension test methods

Review

Recent advances in 4D printing hydrogel for biological interfaces

Original Research

2S-ML: A simulation-based classification and regression approach for drawability assessment in deep drawing

Original Research

Effect of bending radius on deformation behavior of H62 brass tubes in a less constrained free bending process

Premium Partners

    Image Credits