Experimental prediction model for the running-in state of a friction system based on chaotic characteristics and BP neural network
暂无分享,去创建一个
[1] C. Yue,et al. Tool wear identification and prediction method based on stack sparse self-coding network , 2023, Journal of Manufacturing Systems.
[2] Yuankai Zhou,et al. Improving running-in quality of Babbitt alloy using h-BN as oil additives , 2023, Wear.
[3] Jiabing Wang,et al. Multi-objective optimization of printed circuit heat exchanger with airfoil fins based on the improved PSO-BP neural network and the NSGA-II algorithm , 2023, Nuclear Engineering and Technology.
[4] Chunling Wei,et al. Study on the boundedness, stability and dynamic characteristics of friction system based on fractal and chaotic theory , 2023, Tribology International.
[5] Zhi Yang,et al. A novel and quantitative determination method for the running-in process through dimensionless real contact area , 2022, Wear.
[6] Yuankai Zhou,et al. Influence of noise on wear fault diagnosis based on recurrence plot , 2022, Measurement.
[7] Jun Wang,et al. Multiaxial fatigue prediction and uncertainty quantification based on back propagation neural network and Gaussian process regression , 2022, International Journal of Fatigue.
[8] Jiawei Chen,et al. Research on friction performance and wear rate prediction of high-speed train brake pads , 2022, Wear.
[9] Yajie Dou,et al. High-end equipment: An improved two-sided based S&M matching and a novel Pareto refining method considering consistency , 2022, Expert Syst. Appl..
[10] Xinliang Yang,et al. Modeling and experimental investigation of wear and roughness for honed cylinder liner during running-in process , 2022, Tribology International.
[11] M. Khonsari,et al. Experimentally Verified Prediction of Friction Coefficient and Wear Rate During Running-In in Dry Contact , 2022, Tribology International.
[12] T. Pham,et al. EfficientNet convolutional neural networks-based Android malware detection , 2022, Comput. Secur..
[13] Tonghai Wu,et al. Running-in real-time wear generation under vary working condition based on Gaussian process regression approximation , 2021 .
[14] M. Khonsari,et al. On the running-in nature of metallic tribo-components: A review , 2021 .
[15] Junyi Zhang,et al. Reliability prediction-based improved dynamic weight particle swarm optimization and back propagation neural network in engineering systems , 2021, Expert Syst. Appl..
[16] Yuankai Zhou,et al. Prediction model of fractal dimensions in steady state through a multi-stage running in of Sn11Sb6Cu and AISI 1045 steel , 2021, Wear.
[17] Guodong Sun,et al. Investigation of the Optimum Surface Roughness of AISI 5120 Steel by Using a Running-in Attractor , 2020 .
[18] C. Ding. Property and evolution of the running-in attractor in an actual dynamic system , 2020, Nonlinear Dynamics.
[19] Guobin Li,et al. Experimental investigation on identifying friction state in lubricated tribosystem based on friction-induced vibration signals , 2020 .
[20] A. Mäntylä,et al. Prediction of contact condition and surface damage by simulating variable friction coefficient and wear , 2020 .
[21] Gang Chen,et al. Morphing control of a new bionic morphing UAV with deep reinforcement learning , 2019, Aerospace Science and Technology.
[22] Wei Pu,et al. Surface topography and friction coefficient evolution during sliding wear in a mixed lubricated rolling-sliding contact , 2019, Tribology International.
[23] Hua Zhu,et al. Characteristic Parameters and Evolution of the Running-in Attractor , 2019, Int. J. Bifurc. Chaos.
[24] Yuankai Zhou,et al. Application of chaos theory to optimize the running-in parameters by using a running-in attractor , 2019, Wear.
[25] K. Lu,et al. Friction mechanism in the running-in stage of copper: From plastic deformation to delamination and oxidation , 2017 .
[26] Jianbin Luo,et al. Investigation of running-in process in water-based lubrication aimed at achieving super-low friction , 2016 .
[27] Jinyan Fan,et al. Accelerating the modified Levenberg-Marquardt method for nonlinear equations , 2013, Math. Comput..
[28] B. Rolfe,et al. The effect of temperature on sliding wear of steel-tool steel pairs , 2012 .
[29] P. Blau. On the nature of running-in , 2005 .
[30] J. Klafter,et al. The nonlinear nature of friction , 2004, Nature.
[31] P. Grassberger,et al. Characterization of Strange Attractors , 1983 .
[32] F. Takens. Detecting strange attractors in turbulence , 1981 .