{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T21:37:39Z","timestamp":1790804259163,"version":"4.1.0"},"reference-count":62,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2023,1,3]],"date-time":"2023-01-03T00:00:00Z","timestamp":1672704000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation (DFG)","doi-asserted-by":"publisher","award":["SCHM 746\/248-1"],"award-info":[{"award-number":["SCHM 746\/248-1"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Materials"],"abstract":"<jats:p>A comprehensive approach to understand the mechanical behavior of materials involves costly and time-consuming experiments. Recent advances in machine learning and in the field of computational material science could significantly reduce the need for experiments by enabling the prediction of a material\u2019s mechanical behavior. In this paper, a reliable data pipeline consisting of experimentally validated phase field simulations and finite element analysis was created to generate a dataset of dual-phase steel microstructures and mechanical behaviors under different heat treatment conditions. Afterwards, a deep learning-based method was presented, which was the hybridization of two well-known transfer-learning approaches, ResNet50 and VGG16. Hyper parameter optimization (HPO) and fine-tuning were also implemented to train and boost both methods for the hybrid network. By fusing the hybrid model and the feature extractor, the dual-phase steels\u2019 yield stress, ultimate stress, and fracture strain under new treatment conditions were predicted with an error of less than 1%.<\/jats:p>","DOI":"10.3390\/ma16010447","type":"journal-article","created":{"date-parts":[[2023,1,4]],"date-time":"2023-01-04T02:54:55Z","timestamp":1672800895000},"page":"447","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Hybrid Data-Driven Deep Learning Framework for Material Mechanical Properties Prediction with the Focus on Dual-Phase Steel Microstructures"],"prefix":"10.3390","volume":"16","author":[{"given":"Ali Cheloee","family":"Cheloee Darabi","sequence":"first","affiliation":[{"name":"Institute for Materials Testing, Materials Science and Strength of Materials, University of Stuttgart, Pfaffenwaldring 32, 70569 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5104-4597","authenticated-orcid":false,"given":"Shima","family":"Rastgordani","sequence":"additional","affiliation":[{"name":"Institute for Materials Testing, Materials Science and Strength of Materials, University of Stuttgart, Pfaffenwaldring 32, 70569 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2239-1706","authenticated-orcid":false,"given":"Mohammadreza","family":"Khoshbin","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Shahid Rajaee Teacher Training University, Lavizan, Tehran 1678815811, Iran"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9475-2750","authenticated-orcid":false,"given":"Vinzenz","family":"Guski","sequence":"additional","affiliation":[{"name":"Institute for Materials Testing, Materials Science and Strength of Materials, University of Stuttgart, Pfaffenwaldring 32, 70569 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6022-3853","authenticated-orcid":false,"given":"Siegfried","family":"Schmauder","sequence":"additional","affiliation":[{"name":"Institute for Materials Testing, Materials Science and Strength of Materials, University of Stuttgart, Pfaffenwaldring 32, 70569 Stuttgart, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,3]]},"reference":[{"key":"ref_1","unstructured":"Rana, R., and Singh, S.B. (2017). Automotive Steels: Design, Metallurgy, Processing and Applications, Elsevier\/Woodhead Publishing."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"107520","DOI":"10.1016\/j.engfracmech.2020.107520","article-title":"Micromechanical Modeling of Damage Mechanisms in Dual-Phase Steel under Different Stress States","volume":"243","author":"Kadkhodapour","year":"2021","journal-title":"Eng. Fract. Mech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"2516","DOI":"10.1007\/s11661-011-0626-y","article-title":"Phase-Field Modeling of Austenite Formation from a Ferrite plus Pearlite Microstructure during Annealing of Cold-Rolled Dual-Phase Steel","volume":"42","author":"Rudnizki","year":"2011","journal-title":"Metall. Mater. Trans. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1073","DOI":"10.1007\/s11661-014-2698-y","article-title":"Phase-Field Modeling for Intercritical Annealing of a Dual-Phase Steel","volume":"46","author":"Zhu","year":"2015","journal-title":"Metall. Mater. Trans. A"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"045016","DOI":"10.1088\/2051-672X\/abc0f8","article-title":"Damage Characterization of Heat-Treated Titanium Bio-Alloy (Ti\u20136Al\u20134V) Based on Micromechanical Modeling","volume":"8","author":"Rastgordani","year":"2020","journal-title":"Surf. Topogr. Metrol. Prop."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"570","DOI":"10.4028\/www.scientific.net\/KEM.651-653.570","article-title":"Prediction of 3D Microstructure and Plastic Deformation Behavior in Dual-Phase Steel Using Multi-Phase Field and Crystal Plasticity FFT Methods","volume":"651\u2013653","author":"Yamanaka","year":"2015","journal-title":"Key Eng. Mater."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"637","DOI":"10.1002\/srin.201000077","article-title":"Thermo-Elastic Homogenization of 3-D Steel Microstructure Simulated by the Phase-Field Method","volume":"81","author":"Laschet","year":"2010","journal-title":"Steel Res. Int."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.1039\/D0MH01451F","article-title":"Artificial Intelligence and Machine Learning in Design of Mechanical Materials","volume":"8","author":"Guo","year":"2021","journal-title":"Mater. Horiz."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1186\/s40192-015-0042-z","article-title":"Machine Learning Approaches for Elastic Localization Linkages in High-Contrast Composite Materials","volume":"4","author":"Liu","year":"2015","journal-title":"Integr. Mater. Manuf. Innov."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"100591","DOI":"10.1016\/j.softx.2020.100591","article-title":"Sherpa: Robust Hyperparameter Optimization for Machine Learning","volume":"12","author":"Hertel","year":"2020","journal-title":"SoftwareX"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.commatsci.2016.05.034","article-title":"Image Driven Machine Learning Methods for Microstructure Recognition","volume":"123","author":"Chowdhury","year":"2016","journal-title":"Comput. Mater. Sci."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Khorrami, M.S., Mianroodi, J.R., Siboni, N.H., Goyal, P., Svendsen, B., Benner, P., and Raabe, D. (2022). An Artificial Neural Network for Surrogate Modeling of Stress Fields in Viscoplastic Polycrystalline Materials. arXiv.","DOI":"10.1038\/s41524-023-00991-z"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1016\/j.commatsci.2018.05.014","article-title":"Deep Learning Approaches for Mining Structure-Property Linkages in High Contrast Composites from Simulation Datasets","volume":"151","author":"Yang","year":"2018","journal-title":"Comput. Mater. Sci."},{"key":"ref_14","unstructured":"Peivaste, I., Siboni, N.H., Alahyarizadeh, G., Ghaderi, R., Svendsen, B., Raabe, D., and Mianroodi, J.R. (2022). Accelerating Phase-Field-Based Simulation via Machine Learning. arXiv."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.cma.2019.01.005","article-title":"Predicting the Effective Mechanical Property of Heterogeneous Materials by Image Based Modeling and Deep Learning","volume":"347","author":"Li","year":"2019","journal-title":"Comput. Methods Appl. Mech. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1038\/s41524-017-0018-y","article-title":"A Review: Applications of the Phase Field Method in Predicting Microstructure and Property Evolution of Irradiated Nuclear Materials","volume":"3","author":"Li","year":"2017","journal-title":"npj Comput. Mater."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.eml.2017.10.001","article-title":"De Novo Composite Design Based on Machine Learning Algorithm","volume":"18","author":"Gu","year":"2018","journal-title":"Extreme Mech. Lett."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"111750","DOI":"10.1016\/j.commatsci.2022.111750","article-title":"Machine-Learning-Based Surrogate Modeling of Microstructure Evolution Using Phase-Field","volume":"214","author":"Peivaste","year":"2022","journal-title":"Comput. Mater. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1038\/s41524-021-00568-8","article-title":"Super-Resolving Material Microstructure Image via Deep Learning for Microstructure Characterization and Mechanical Behavior Analysis","volume":"7","author":"Jung","year":"2021","journal-title":"Npj Comput. Mater."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"103787","DOI":"10.1016\/j.advwatres.2020.103787","article-title":"DeePore: A Deep Learning Workflow for Rapid and Comprehensive Characterization of Porous Materials","volume":"146","author":"Rabbani","year":"2020","journal-title":"Adv. Water Resour."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1017\/S1431927621007674","article-title":"Image-Driven Discriminative and Generative Methods for Establishing Microstructure-Processing Relationships Relevant to Nuclear Fuel Processing Pipelines","volume":"27","author":"Kautz","year":"2021","journal-title":"Microsc. Microanal."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"103774","DOI":"10.1016\/j.isci.2022.103774","article-title":"Comparing Transfer Learning to Feature Optimization in Microstructure Classification","volume":"25","author":"Banerjee","year":"2022","journal-title":"iScience"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"104062","DOI":"10.1016\/j.mtcomm.2022.104062","article-title":"Mixing Effects of SEM Imaging Conditions on Convolutional Neural Network-Based Low-Carbon Steel Classification","volume":"32","author":"Tsutsui","year":"2022","journal-title":"Mater. Today Commun."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1016\/S0167-2789(99)00129-3","article-title":"A Generalized Field Method for Multiphase Transformations Using Interface Fields","volume":"134","author":"Steinbach","year":"1999","journal-title":"Phys. Nonlinear Phenom."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"066122","DOI":"10.1103\/PhysRevE.73.066122","article-title":"Multiphase-Field Approach for Multicomponent Alloys with Extrapolation Scheme for Numerical Application","volume":"73","author":"Eiken","year":"2006","journal-title":"Phys. Rev. E"},{"key":"ref_26","unstructured":"(2022, November 21). ACCESS, e.V. MICRESS Microstructure Simulation Software Manual, Version 7.0. Available online: https:\/\/micress.rwth-aachen.de\/download.html#manuals."},{"key":"ref_27","unstructured":"Kozeschnik, E. (MatCalc Software, 2020). MatCalc Software, version 6.03 (rel 1.000)."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Krauss, G. (2014). Quench and Tempered Martensitic Steels. Comprehensive Materials Processing, Elsevier.","DOI":"10.1016\/B978-0-08-096532-1.01212-7"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/0167-2789(95)00298-7","article-title":"A Phase Field Concept for Multiphase Systems","volume":"94","author":"Steinbach","year":"1996","journal-title":"Phys. Nonlinear Phenom."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Br\u00e9chet., Y. (2011). Solid-Solid Phase Transformations in Inorganic Materials, Trans Tech Publications. Solid State Phenomena.","DOI":"10.4028\/b-2Lqlx1"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"534","DOI":"10.3139\/146.110307","article-title":"Phase Field Modelling of Austenite Formation from Ultrafine Ferrite\u2013Carbide Aggregates in Fe\u2013C","volume":"101","author":"Militzer","year":"2010","journal-title":"Int. J. Mater. Res."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4817","DOI":"10.1016\/j.actamat.2007.05.013","article-title":"The Influence of Lattice Strain on Pearlite Formation in Fe\u2013C","volume":"55","author":"Steinbach","year":"2007","journal-title":"Acta Mater."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1016\/j.actamat.2014.04.015","article-title":"The Influence of Microstructure and Composition on the Plastic Behaviour of Dual-Phase Steels","volume":"73","author":"Pierman","year":"2014","journal-title":"Acta Mater."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"2683","DOI":"10.1007\/s11665-017-2687-6","article-title":"Modification of Rule of Mixtures for Estimation of the Mechanical Properties of Dual-Phase Steels","volume":"26","author":"Alibeyki","year":"2017","journal-title":"J. Mater. Eng. Perform."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"68","DOI":"10.1016\/j.mechmat.2017.04.009","article-title":"Micromechanical Analysis of Two Heat-Treated Dual Phase Steels: DP800 and DP980","volume":"110","author":"Chamani","year":"2017","journal-title":"Mech. Mater."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1038\/s41524-017-0056-5","article-title":"Machine Learning in Materials Informatics: Recent Applications and Prospects","volume":"3","author":"Ramprasad","year":"2017","journal-title":"Npj Comput. Mater."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"2128","DOI":"10.1038\/s41598-018-20037-5","article-title":"Advanced Steel Microstructural Classification by Deep Learning Methods","volume":"8","author":"Azimi","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Smokvina Hanza, S., Marohni\u0107, T., Iljki\u0107, D., and Basan, R. (2021). Artificial Neural Networks-Based Prediction of Hardness of Low-Alloy Steels Using Specific Jominy Distance. Metals, 11.","DOI":"10.3390\/met11050714"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Agrawal, A., Gopalakrishnan, K., and Choudhary, A. (2020). Materials Image Informatics Using Deep Learning. Handbook on Big Data and Machine Learning in the Physical Sciences, World Scientific Publishing Co.","DOI":"10.1142\/9789811204555_0006"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"520","DOI":"10.1016\/j.jmrt.2022.02.108","article-title":"Machine Learning Prediction of the Mechanical Properties of \u03b3-TiAl Alloys Produced Using Random Forest Regression Model","volume":"18","author":"Kwak","year":"2022","journal-title":"J. Mater. Res. Technol."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Gajewski, J., Golewski, P., and Sadowski, T. (2021). The Use of Neural Networks in the Analysis of Dual Adhesive Single Lap Joints Subjected to Uniaxial Tensile Test. Materials, 14.","DOI":"10.3390\/ma14020419"},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Kosarac, A., Cep, R., Trochta, M., Knezev, M., Zivkovic, A., Mladjenovic, C., and Antic, A. (2022). Thermal Behavior Modeling Based on BP Neural Network in Keras Framework for Motorized Machine Tool Spindles. Materials, 15.","DOI":"10.3390\/ma15217782"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Valen\u00e7a, J., Mukhandi, H., Ara\u00fajo, A.G., Couceiro, M.S., and J\u00falio, E. (2022). Benchmarking for Strain Evaluation in CFRP Laminates Using Computer Vision: Machine Learning versus Deep Learning. Materials, 15.","DOI":"10.3390\/ma15186310"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Azarafza, M., Hajialilue Bonab, M., and Derakhshani, R. (2022). A Deep Learning Method for the Prediction of the Index Mechanical Properties and Strength Parameters of Marlstone. Materials, 15.","DOI":"10.3390\/ma15196899"},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Liu, H., and Motoda, H. (1998). Feature Selection for Knowledge Discovery and Data Mining, Springer.","DOI":"10.1007\/978-1-4615-5689-3"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Rasool, M., Ismail, N.A., Boulila, W., Ammar, A., Samma, H., Yafooz, W.M.S., and Emara, A.-H.M. (2022). A Hybrid Deep Learning Model for Brain Tumour Classification. Entropy, 24.","DOI":"10.3390\/e24060799"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Zou, F., Shen, L., Jie, Z., Zhang, W., and Liu, W. (2018). A Sufficient Condition for Convergences of Adam and RMSProp. arxiv.","DOI":"10.1109\/CVPR.2019.01138"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/s11242-011-9877-8","article-title":"Relative Permeability Calculations from Two-Phase Flow Simulations Directly on Digital Images of Porous Rocks","volume":"94","author":"Ramstad","year":"2012","journal-title":"Transp. Porous Media"},{"key":"ref_49","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arxiv."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Patel, A., Cheung, L., Khatod, N., Matijosaitiene, I., Arteaga, A., and Gilkey, J.W. (2020). Revealing the Unknown: Real-Time Recognition of Gal\u00e1pagos Snake Species Using Deep Learning. Animals, 10.","DOI":"10.3390\/ani10050806"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"012012","DOI":"10.1088\/1742-6596\/1653\/1\/012012","article-title":"Based on Adam Optimization Algorithm: Neural Network Model for Auto Steel Performance Prediction","volume":"1653","author":"Pengtao","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2015). Deep Residual Learning for Image Recognition. arxiv.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Xu, M., Wang, S., Guo, J., and Li, Y. (2020). Robust Structural Damage Detection Using Analysis of the CMSE Residual\u2019s Sensitivity to Damage. Appl. Sci., 10.","DOI":"10.3390\/app10082826"},{"key":"ref_54","unstructured":"Pedersen, M.E.H. (2020, April 02). Available online: https:\/\/github.com\/Hvass-Labs\/TensorFlow-Tutorials\/blob\/master\/11_Adversarial_Examples.ipynb."},{"key":"ref_55","unstructured":"Brownlee, J. (2022, November 21). Loss and Loss Functions for Training Deep Learning Neural Networks. Machine Learning Mastery, Available online: https:\/\/machinelearningmastery.com\/loss-and-loss-functions-for-training-deep-learning-neural-networks\/."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1847","DOI":"10.1007\/s11548-019-02010-3","article-title":"Hyperparameter Optimization for Image Analysis: Application to Prostate Tissue Images and Live Cell Data of Virus-Infected Cells","volume":"14","author":"Ritter","year":"2019","journal-title":"Int. J. Comput. Assist. Radiol. Surg."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ijforecast.2006.03.001","article-title":"Another Look at Measures of Forecast Accuracy","volume":"22","author":"Hyndman","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"4420","DOI":"10.1007\/s11837-020-04404-0","article-title":"Damage Analysis in Dual-Phase Steel Using Deep Learning: Transfer from Uniaxial to Biaxial Straining Conditions by Image Data Augmentation","volume":"72","author":"Medghalchi","year":"2020","journal-title":"JOM"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Bengio, Y. (2012). Practical Recommendations for Gradient-Based Training of Deep Architectures. arxiv.","DOI":"10.1007\/978-3-642-35289-8_26"},{"key":"ref_60","unstructured":"Ibrahim, M.M. (2022, November 21). The Design of an Innovative Automatic Computational Method for Generating Geometric Islamic Visual Art with Aesthetic Beauty. Available online: https:\/\/uobrep.openrepository.com\/handle\/10547\/625007."},{"key":"ref_61","unstructured":"Brownlee, J. (2020). Data Preparation for Machine Learning: Data Cleaning, Feature Selection, and Data Transforms in Python, Machine Learning Mastery. Available online: https:\/\/machinelearningmastery.com\/data-preparation-for-machine-learning\/."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s41529-021-00151-y","article-title":"StressNet\u2014Deep Learning to Predict Stress with Fracture Propagation in Brittle Materials","volume":"5","author":"Wang","year":"2021","journal-title":"npj Mater. 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