Shengli Jin
(Former)
Research output
- Published
Simulation of refractory fracture as a tool for advanced material testing
Harmuth, H., Gruber, D. & Jin, S., 2014, Advances in Science and Technology . Vol. 92. p. 232-241Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
A Method for Steel Ladle Lining Optimization Applying Thermomechanical Modeling and Taguchi Approaches
Hou, A., Jin, S., Harmuth, H. & Gruber, D., 9 Aug 2018, In: JOM. 70, 11, p. 2449 2456 p.Research output: Contribution to journal › Article › Research › peer-review
- Published
Thermal and Thermomechanical Responses Prediction of a Steel Ladle Using a Back-Propagation Artificial Neural Network Combining Multiple Orthogonal Arrays
Hou, A., Jin, S., Harmuth, H. & Gruber, D., 29 Apr 2019, In: Steel research international. 2019, 8 p., 1900116.Research output: Contribution to journal › Article › Research › peer-review
- Published
Modelling of a steel ladle and prediction of its thermomechanical behavior by finite element simulation together with artificial neural network approaches
Hou, A., Jin, S., Gruber, D. & Harmuth, H., 4 Jul 2019, p. 1109 - 1116. 7 p.Research output: Contribution to conference › Paper
- Published
Influence of Variation/Response Space Complexity and Variable Completeness on BP-ANN Model Establishment: Case Study of Steel Ladle Lining
Hou, A., Jin, S., Gruber, D. & Harmuth, H., 16 Jul 2019, In: Applied Sciences : open access journal. 9.2019, 14, 12 p., 2835.Research output: Contribution to journal › Article › Research › peer-review
- E-pub ahead of print
Multi-Response Optimization of the Thermal and Thermomechanical Behavior of a Steel Ladle Lining using Grey Relational Analysis and Technique for Order Preference by Similarity to Ideal Solution
Hou, A., Gruber, D. & Jin, S., 23 Sept 2022, (E-pub ahead of print) In: Steel research international. 94.2023, 1, 10 p., 2200407.Research output: Contribution to journal › Article › Research › peer-review
- Published
Application of Artificial Neural Network to Predict the Thermal and Thermomechanical Behavior of Refractory Linings
Hou, A., Jin, S., Gruber, D. & Harmuth, H., 2022, 2022 International Joint Conference on Neural Networks, IJCNN 2022 - Proceedings. Institute of Electrical and Electronics Engineers, (Proceedings of the International Joint Conference on Neural Networks; vol. 2022-July).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
- Published
Classification of thermomechnical impact factors and prediction model for ladle preheating
Jin, S., Harmuth, H., Gruber, D., Auer, T. & Li, Y., 2011, In: Journal of Wuhan University of Technology. 34, 1, p. 28-31Research output: Contribution to journal › Article › Research › peer-review
- Published
Thermomechanical Steel Ladle Simulation Including a Mohr-Coulomb Plasticity Failure Model
Jin, S., Gruber, D., Harmuth, H. & Fréchette, M.-H., 2012, In: RHI bulletin : the journal of refractory innovations. 1, p. 39-43Research output: Contribution to journal › Article › Research › peer-review
- Published
Thermo-mechanical Modeling of a Complete Steel Ladle Process
Jin, S., Auer, T., Gruber, D., Harmuth, H., Fréchette, M. H. & Li, Y., 2011, Refractories-Technology to Sustain the Global Environment. p. 10-10Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
