SearchMachine Learning and Deep Learning (2020–2024)
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Explainable machine learning model for predicting molten steel temperature in the LF refining process
2024, vol. 31, no. 12, pp. 2657-2669. doi: 10.1007/s12613-024-2950-4
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Machine learning design of 400 MPa grade biodegradable Zn–Mn based alloys with appropriate corrosion rates
2024, vol. 31, no. 12, pp. 2727-2736. doi: 10.1007/s12613-024-2995-4
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Rapid prediction of flow and concentration fields in solid–liquid suspensions of slurry electrolysis tanks
2024, vol. 31, no. 9, pp. 2006-2016. doi: 10.1007/s12613-024-2826-7
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Developing an atmospheric aging evaluation model of acrylic coatings: A semi-supervised machine learning algorithm
2024, vol. 31, no. 7, pp. 1617-1627. doi: 10.1007/s12613-024-2921-9
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Description of martensitic transformation kinetics in Fe–C–X (X = Ni, Cr, Mn, Si) system by a modified model
2024, vol. 31, no. 5, pp. 1026-1036. doi: 10.1007/s12613-023-2780-9
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Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature
2024, vol. 31, no. 4, pp. 773-785. doi: 10.1007/s12613-023-2767-6
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Prediction model for corrosion rate of low-alloy steels under atmospheric conditions using machine learning algorithms
2024, vol. 31, no. 2, pp. 337-350. doi: 10.1007/s12613-023-2679-5
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Lattice Boltzmann simulation study of anode degradation in solid oxide fuel cells during the initial aging process
2024, vol. 31, no. 2, pp. 405-411. doi: 10.1007/s12613-023-2692-8
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Prediction of the thermal conductivity of Mg–Al–La alloys by CALPHAD method
2024, vol. 31, no. 1, pp. 129-137. doi: 10.1007/s12613-023-2759-6
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State of the art in applications of machine learning in steelmaking process modeling
2023, vol. 30, no. 11, pp. 2055-2075. doi: 10.1007/s12613-023-2646-1
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Intelligent method to experimentally identify the fracture mechanism of red sandstone
2023, vol. 30, no. 11, pp. 2134-2146. doi: 10.1007/s12613-023-2668-8
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Effect of traveling-wave magnetic field on dendrite growth of high-strength steel slab: Industrial trials and numerical simulation
2023, vol. 30, no. 9, pp. 1716-1728. doi: 10.1007/s12613-023-2629-2
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Advances in machine learning- and artificial intelligence-assisted material design of steels
2023, vol. 30, no. 6, pp. 1003-1024. doi: 10.1007/s12613-022-2595-0
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Double-face intelligent hole position planning method for precision blasting in roadways using a computer-controlled drill jumbo
2023, vol. 30, no. 6, pp. 1025-1037. doi: 10.1007/s12613-022-2575-4
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Prediction of mechanical properties for deep drawing steel by deep learning
2023, vol. 30, no. 1, pp. 156-165. doi: 10.1007/s12613-022-2547-8
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Rapid design of secondary deformation-aging parameters for ultra-low Co content Cu–Ni–Co–Si–X alloy via Bayesian optimization machine learning
2022, vol. 29, no. 6, pp. 1197-1205. doi: 10.1007/s12613-022-2479-3
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Recent progress in the machine learning-assisted rational design of alloys
2022, vol. 29, no. 4, pp. 635-644. doi: 10.1007/s12613-022-2458-8
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Data-mining and atmospheric corrosion resistance evaluation of Sn- and Sb-additional low alloy steel based on big data technology
2022, vol. 29, no. 4, pp. 825-835. doi: 10.1007/s12613-022-2457-9
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Evaluating data-driven algorithms for predicting mechanical properties with small datasets: A case study on gear steel hardenability
2022, vol. 29, no. 4, pp. 836-847. doi: 10.1007/s12613-022-2437-0
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Prediction of the Charpy V-notch impact energy of low carbon steel using a shallow neural network and deep learning
2021, vol. 28, no. 8, pp. 1309-1320. doi: 10.1007/s12613-020-2168-z
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Intelligent logistics system of steel bar warehouse based on ubiquitous information
2021, vol. 28, no. 8, pp. 1367-1377. doi: 10.1007/s12613-021-2325-z
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Machine-learning-assisted prediction of the mechanical properties of Cu–Al alloy
2020, vol. 27, no. 3, pp. 362-373. doi: 10.1007/s12613-019-1894-6
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