Showing 5,521 - 5,540 results of 7,394 for search 'parameter machine', query time: 0.17s Refine Results
  1. 5521

    Sustainable Mineral Processing Technologies Using Hybrid Intelligent Algorithms by Olga Shiryayeva, Batyrbek Suleimenov, Yelena Kulakova

    Published 2025-06-01
    “…A hybrid intelligent control system was developed to beneficiate fine chromite ore in a jigging machine. The objective is to enhance separation efficiency and reduce chromium losses through real-time optimization of process parameters under variable feed conditions. …”
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    Article
  2. 5522
  3. 5523

    Simulation of vertical vibrations of the operator’s seat with a given force characteristic by Korytov M.S., Sherbakov V.S., Galdin N.S., Kashapova I.E.

    Published 2025-03-01
    “…Operators of construction and road building machines are subjected to significant vibration and shock loads. …”
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    Article
  4. 5524

    Analysis of Surface Roughness of Diamond-Burnished Surfaces Using Kraljic Matrices and Experimental Design by Szilárd Smolnicki, Gyula Varga

    Published 2025-07-01
    “…The aim of this study is to analyze the roughness of a surface machined by diamond burnishing using Kraljic matrices. …”
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    Article
  5. 5525

    Modeling turning performance of Inconel 718 with hybrid nanofluid under MQL using ANN and ANFIS by Paresh Kulkarni, Satish Chinchanikar

    Published 2024-10-01
    “…This shows that ANFIS could be a better option for forecasting the machining performance while turning Inconel 718. However, this study suggests further investigation into ANFIS modeling, with a focus on membership function parameter optimization through hybrid optimization techniques.…”
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    Article
  6. 5526

    Numerical simulation of heat-and-mass transfer processes in artificial burning CO^ gases cleanup systems by A. P. Nesenchuk, V. M. Kopko, T. V. Ryzhova, M. G. Pshonik, N. G. Malkevich

    Published 2001-02-01
    “…On the basis of numerical simulation of heat-and- mass transfer dependencies of mass-transfer cha­racteristics of αϒ and  αΩ sorption system at varying machine parameters are received.…”
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    Article
  7. 5527

    Excellent vibration isolation properties of a curved Euler beam QZS isolator: Simulation and experiment by Xiaoqiang Chong, Zhijing Wu

    Published 2024-11-01
    “…The static and dynamic properties and parameter influences are investigated to identify the isolation mechanism of the novel isolator through numerical simulation using the finite element analysis. …”
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    Article
  8. 5528

    Research on Soft-Sensing Methods for Measuring Diene Yields Using Deep Belief Networks by Xiangwu Deng, Zhiping Peng, Delong Cui

    Published 2022-01-01
    “…The diene yield is an important production quality index parameter of ethylene units, and it is very important to detect and control them in real time. …”
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    Article
  9. 5529

    Optimization on Material Removal Rate and Surface Roughness of Stainless Steel 304 Wire Cut EDM by Response Surface Methodology by Sathishkumar Seshaiah, Deepak Sampathkumar, Mathanbabu Mariappan, Ashokkumar Mohankumar, Guruprasad Balachandran, Murugan Kaliyamoorthy, Barathiraja Rajendran, Rajendiran Gopal

    Published 2022-01-01
    “…In this paper, the selected complex geometry of the metal sample was eroded away from the wire during the WEDM process, which eliminates mechanical tensions during machining. The effect of different WEDM operation variables set as wire speed, wire tension, discharge current, dielectric flow rate, and pulse on and off time on the parameter, stainless steel 304 material removing rate (MRR) using RSM, has been studied. …”
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    Article
  10. 5530

    Prediction of vibration in milling of thin-walled aluminum alloy parts using neural network model by Junming Hou, Baosheng Wang, Dongsheng Lv, Changhong Xu

    Published 2024-12-01
    “…In this study, a method for establishing a particle swarm optimization-back propagation (PSO-BP) neural network model is proposed to predict the modal parameters of thin-walled parts and the surface vibration of machined parts. …”
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    Article
  11. 5531
  12. 5532

    Study on design of crop residue choppers for hard stalks by SONG Hui-zhi, SHENG Kui-chuan, Qian Xiang-qun

    Published 2003-03-01
    “…The main purpose of this project is to investigate the main structure and main design parameters of crop chopper for chopping hard stalks, such as cotton stalk, mulberry branch and so on. …”
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    Article
  13. 5533

    Enhanced mastitis severity classification in dairy cows using DNN and RF: A study on PCA and correlation-based feature selection by Manar Lashin, Ayman Samir Farid, Abdullah T. Elgammal

    Published 2024-12-01
    “…Key parameters in the RF and DNN models were carefully selected to ensure robust performance across diverse datasets. …”
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    Article
  14. 5534

    A Deep Learning Approach for Extracting Cyanobacterial Blooms in Eutrophic Lakes From Satellite Imagery by Nan Wang, Zhenyu Tan, Chen Yang, Jinge Ma, Hongtao Duan

    Published 2025-01-01
    “…Experiments showed that MBAUNet achieved over 90% precision and recall, with an F1 score of 94.01%, outperforming vanilla UNet, DeepLabV3+, random forest, and support vector machine, while halving the number of parameters and training time compared to UNet. …”
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  15. 5535

    Prediction of the SYM‐H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification by Yasser Abduallah, Khalid A. Alobaid, Jason T. L. Wang, Haimin Wang, Vania K. Jordanova, Vasyl Yurchyshyn, Huseyin Cavus, Ju Jing

    Published 2024-02-01
    “…The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM‐H indices (1 hr in advance) in a large storm (SYM‐H = −393 nT) using 5‐min resolution data. …”
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  16. 5536

    Three-dimensional creep constitutive model of sandstone based on damage statistics by Wang Liukai, Cai Guojun, Zhao Weiping, Hu Xing

    Published 2025-01-01
    “…The STAC600-600 rock rheology testing machine was used to carry out the rock classification rheological test, and the fitting results of the creep model were analyzed, and the model parameters were optimized to improve the fitting accuracy. …”
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  17. 5537

    翻板机主减速器齿轮修形 by 成沛祥, 王聪

    Published 2008-01-01
    “…By mending tooth shape of the tooth profile and tooth direction of gear pair,the impact of meshing which is caused by manufacture error and deformation of loaded tooth can be lessen,so increasing the ability of loading and capability of meshing.The main parameters of the main reducer of panel turnover machine are given,and the elastic deformation of the tooth profile and tooth direction of the first order gear pair is calculated,the profiling quantity of the tooth profile and tooth direction is determined.By detecting the live practical application datum of the reducer,the good effect of the gear modification is verified.…”
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  18. 5538

    茄子采摘机器人机械传动系统设计与开发 by 宋健, 孙学岩, 张铁中, 张宾

    Published 2009-01-01
    “…The optimal design is made on the parameters of the robot framework and structure according to the eggplant’s growth distribution space. …”
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  19. 5539

    基于COSMOS/Works的数控机床主轴箱模态分析及优化设计 by 邱海飞, 王增强

    Published 2012-01-01
    “…The finite element model of NC Machine spindle box is set up by COSMOS/Works,and modal analysis of the spindle box is done,then the natural frequencies and the vibration modes are calculated.On that basis,the dynamic characteristic of spindle box is optimized through defining multi structure sizes as design variable.Fundamental frequency of the spindle box is increased obviously after optimizing;as a result,damping property of it is enhanced effectively.A group of structural parameters with fine dynamic characteristic are calculated,which offer significant referents to structure design of spindle box.…”
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  20. 5540

    Investigation of Effects of Feed Rate and Cutting-Edge Angle Variation on Surface Roughness in External Cylindrical Turning Process of Ms58 Brass Material by Ergun Ateş, Nefise Serbest

    Published 2024-12-01
    “…For each surface roughness value measured from parts machined with other parameters, measurements were made five times, the largest and smallest values were discarded, and the average of the remaining 3 values was taken. …”
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