Optimizing prediction of stainless steel mechanical properties with random forest: a comparison of feature selection methods
Abstract
Keywords
Full Text:
PDFReferences
. [1] Momeni, M. M., &Motalebian, M. (2021). Chromium-doped titanium oxide nanotubes grown via one-step anodization for efficient photocathodic protection of stainless steel. Surface and Coatings Technology, 420, 127304.
. Rabi, MM (2020). Analysis and design of stainless steel reinforced concrete structural elements (Doctoral dissertation, Brunel University London).
. Tabrizikahou, A., Kuczma, M., Łasecka-Plura, M., Farsangi, E.N., Noori, M., Gardoni, P., & Li, S. (2022). Application and modeling of Shape-Memory Alloys for structural vibration control: State-of-the-art review. Construction and Building Materials, 342, 127975.
. Kumar, P., Jayaraj, R., Suryawanshi, J., Satwik, U.R., McKinnell, J., &Ramamurty, U. (2020). Fatigue strength of additively manufactured 316L austenitic stainless steel. ActaMaterialia, 199, 225-239.
. Morini, A. A., Ribeiro, M. J., &Hotza, D. (2019). Early-stage materials selection based on embodied energy and carbon footprint. Materials & Design, 178, 107861.
. Wang, C., Zhu, P., Lu, Y. H., & Shoji, T. (2022). Effect of heat treatment temperature on microstructure and tensile properties of austenitic stainless 316L using wire and arc additive manufacturing. Materials Science and Engineering: A, 832, 142446.
. Pan, M., Zhang, X., Chen, P., Su, X., &Misra, RDK (2020). The effect of chemical composition and annealing conditions on the microstructure and tensile properties of a resource-saving duplex stainless steel. Materials Science and Engineering: A, 788, 139540.
. Leni, D. (2023). Prediction Modeling of Low Alloy Steel Based on Chemical Composition and Heat Treatment Using Artificial Neural Network. Polymachinery Journal, 21(5), 54-61.
. Leni, D., Karudin, A., Abbas, M. R., Sharma, J. K., &Adriansyah, A. (2024). Optimizing stainless steel tensile strength analysis: through data exploration and machine learning design with Streamlit. EUREKA: Physics and Engineering, (5), 73-88.
. Laleh, M., Sadeghi, E., Revilla, R.I., Chao, Q., Haghdadi, N., Hughes, A.E., ...& Tan, M.Y. (2023). Heat treatment for metal additive manufacturing. Progress in Materials Science, 133, 101051.
. Vorontsov, A., Astafurov, S., Melnikov, E., Moskvina, V., Kolubaev, E., &Astafurova, E. (2021). The microstructure, phase composition and tensile properties of austenitic stainless steel in a wire-feed electron beam melting combined with ultrasonic vibration. Materials Science and Engineering: A, 820, 141519.
. Luo, M., Zhou, G.Y., Shen, H., Wang, X.T., Li, M.C., Zhang, Z.H., & Cao, G.H. (2022). Effect of Tempering Temperature on Microstructure and Sulfide Stress Cracking of 125 Ksi Grade Casing Steel. Materials, 15(7), 2589.
. Packwood, D., Nguyen, LTH, Cesana, P., Zhang, G., Staykov, A., Fukumoto, Y., & Nguyen, D.H. (2022). Machine learning in materials chemistry: An invitation. Machine learning with applications, 8, 100265.
. Leni, D., Sumiati, R., Angelia, N., & Nofriyanti, E. (2023). The Influence of Heatmap Correlation-based Feature Selection on Predictive Modeling of Low Alloy Steel Mechanical Properties Using Artificial Neural Network (ANN) Algorithm. Journal of Energy, Materials, and Instrumentation Technology, 4(4), 152-162.
. Liu, W., & Wang, J. (2021). Recursive elimination–election algorithms for wrapper feature selection. Applied Soft Computing, 113, 107956.
. Jiang, X., Jia, B., Zhang, G., Zhang, C., Wang, X., Zhang, R., ...& Ma, H. (2020). A strategy combining machine learning and multiscale calculation to predict tensile strength for pearlitic steel wires with industrial data. ScriptaMaterialia, 186, 272-277.
. Agrawal, A., &Choudhary, A. (2018). An online tool for predicting fatigue strength of steel alloys based on ensemble data mining. International Journal of Fatigue, 113, 389-400.
. Ruiz, E., Ferreño, D., Cuartas, M., López, A., Arroyo, V., & Gutiérrez-Solana, F. (2020). Machine learning algorithms for the prediction of the strength of steel rods: an example of data-driven manufacturing in steelmaking. International Journal of Computer Integrated Manufacturing, 33(9), 880-894.
. Narayana, PL, Lee, SW, Park, CH, Yeom, JT, Hong, JK, Maurya, AK, & Reddy, NS (2020). Modeling high-temperature mechanical properties of austenitic stainless steels by neural networks. Computational Materials Science, 179, 109617.
. The British Steelmakers Creep Committee: BSCC High Temperature Data; The Iron and Steel Institute: London, UK, 1973.
. Materials Algorithms Project. Available online: https://www.phase-trans.msm.cam.ac.uk/map (accessed on 11 April 2022).
. Leni, D., Kesuma, D. S., Maimuzar, Haris, &Afriyani, S. (2024). Prediction of Mechanical Properties of Austenitic Stainless Steels with the Use of Synthetic Data via Generative Adversarial Networks. Engineering Proceedings, 63(1), 4.
. AGRAWAL, Ankit, et al. Exploration of data science techniques to predict fatigue strength of steel from composition and processing parameters. Integrating Materials and Manufacturing Innovation, 2014, 3: 90-108.
. Deepa, B., & Ramesh, K. (2022). Epileptic seizure detection using deep learning through min max scaler normalization. Int. J. Health Sci, 6, 10981-10996.
. Jeon, H., & Oh, S. (2020). Hybrid-recursive feature elimination for efficient feature selection. Applied Sciences, 10(9), 3211.
. Lin, X., Yang, F., Zhou, L., Yin, P., Kong, H., Xing, W., ...&Xu, G. (2012). A support vector machine-recursive feature elimination feature selection method based on artificial contrast variables and mutual information. Journal of chromatography B, 910, 149-155.
. Pham, BT, Jaafari, A., Avand, M., Al-Ansari, N., Dinh Du, T., Yen, HPH, ...&Tuyen, TT (2020). Performance evaluation of machine learning methods for forest fire modeling and prediction. Symmetry, 12(6), 1022.
. Hu, M., Tan, Q., Knibbe, R., Wang, S., Li, X., Wu, T., ...& Zhang, MX (2021). Prediction of mechanical properties of wrought aluminum alloys using feature engineering assisted machine learning approach. Metallurgical and Materials Transactions A, 52(7), 2873-2884.
. Xiong, J., Zhang, T., & Shi, S. (2020). Machine learning of mechanical properties of steels. Science China Technological Sciences, 63(7), 1247-1255.
. Zhang, S., Jiang, Z., Li, H., Zhang, B., Fan, S., Li, Z., ...& Zhu, H. (2018). Precipitation behavior and phase transformation mechanism of super austenitic stainless steel S32654 during isothermal aging. Materials characterization, 137, 244-255.
. Moniruzzaman, F.M., Shakil, S.I., Shaha, S.K., Kacher, J., Nasiri, A., Haghshenas, M., &Hadadzadeh, A. (2023). Study of direct aging heat treatment of additively manufactured PH13–8Mo stainless steel: role of the manufacturing process, phase transformation kinetics, and microstructure evolution. Journal of Materials Research and Technology, 24, 3772-3787.
. Niu, M. C., Yang, K., Luan, J. H., Wang, W., & Jiao, Z. B. (2022). Cu-assisted austenite reversion and enhanced TRIP effect in maraging stainless steels. Journal of Materials Science & Technology, 104, 52-58.
. Bleck, W., Guo, X., & Ma, Y. (2017). The TRIP effect and its application in cold formable sheet steels. Steel Research International, 88(10), 1700218.
. Cronemberger, MER, Mariano, NA, Coelho, MF, Pereira, JN, Ramos, É. C., de Mendonça, R., ...&Maestrelli, S.C. (2014, December). Study of cooling rate influence on SAF 2205 duplex stainless steel solution
. Probst, P., &Boulesteix, A.L. (2018). To tune or not to tune the number of trees in random forest. Journal of Machine Learning Research, 18(181), 1-18.
. Probst, P., Wright, M.N., &Boulesteix, A.L. (2019). Hyperparameters and tuning strategies for random forests. Wiley Interdisciplinary Reviews: data mining and knowledge discovery, 9(3), e1301.
. Probst, P., &Boulesteix, A.L. (2018). To tune or not to tune the number of trees in random forest. Journal of Machine Learning Research, 18(181), 1-18.
. Lin, S., Zheng, H., Han, B., Li, Y., Han, C., & Li, W. (2022). Comparative performance of eight ensemble learning approaches for the development of models of slope stability prediction. ActaGeotechnica, 17(4), 1477-1502.
. Ibrahim, M. (2022). Evolution of Random Forest from Decision Trees and Bagging: A Bias-Variance Perspective. Dhaka University Journal of Applied Science and Engineering, 7(1), 66-71.
. Lamba, R., Gulati, T., & Jain, A. (2022). A hybrid feature selection approach for Parkinson's detection based on mutual information gain and recursive feature elimination. Arabian Journal for Science and Engineering, 47(8), 10263-10276.
. Wang, X., Guo, B., Shen, Y., Zhou, C., &Duan, X. (2019). Input feature selection method based on feature set equivalence and mutual information gain maximization. IEEE Access, 7, 151525-151538.
. Raschka, S. (2018). Model evaluation, model selection, and algorithm selection in machine learning. arXiv preprint arXiv:1811.12808.
DOI: http://dx.doi.org/10.30811/jpl.v22i5.5381
Refbacks
- There are currently no refbacks.
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Ciptaan disebarluaskan di bawah Lisensi Creative Commons Atribusi-BerbagiSerupa 4.0 Internasional .
Alamat Surat :
Politeknik Negeri Lhokseumawe
Jl. Banda Aceh-Medan Km 280
Buketrata, Lhokseumawe, 24301, Aceh, Indonesia