Adaptive indirect neural network model for roughness in honing processes-Reference-Cited by-同舟云学术

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Adaptive indirect neural network model for roughness in honing processes

Published:2020-01 Issue: Volume:141 Page:105891
ISSN:0301-679X
Container-title:Tribology International
language:en
Short-container-title:Tribology International

Author:

Buj-Corral Irene,Sivatte-Adroer Maurici,Llanas-Parra Xavier

Funder

Spanish Ministry of Economy and Competitiveness

Publisher

Elsevier BV

Subject

Surfaces, Coatings and Films,Surfaces and Interfaces,Mechanical Engineering,Mechanics of Materials

Reference33 articles.

1. Automotive tribology overview of current advances and challenges for the future;Tung;Tribol Int,2004

2. Effects of honed cylinder liner surface texture on tribological properties of piston ring-liner assembly in short time tests;Grabon;Tribol Int,2017

3. Mutual influence of crosshatch angle and superficial roughness of honed surfaces on friction in ring-pack tribo-system;Mezghani;Tribol Int,2013

4. Evaluation of image based Abbott–Firestone curve parameters using machine vision for the characterization of cylinder liner surface topography;Deepak Lawrence;Measurement,2014

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Performance prediction and optimization of nanolubricants for hydrodynamic journal bearings: a data-driven approach for regulating volumetric fraction and aggregate particle size;Cogent Engineering;2024-07-12

2. A data-driven exploration of multi pad bidirectional adjustable bearings and deep learning model-based optimization;Tribology International;2024-06

3. Simulation of plateau-honed cylinder liner surface texture creation using superimposition approach;Precision Engineering;2023-07

4. Multi-objective optimization of tool wear, surface roughness, and material removal rate in finishing honing processes using adaptive neural fuzzy inference systems;Tribology International;2023-04

5. Modeling of Surface Roughness in Honing Processes by Using Fuzzy Artificial Neural Networks;Journal of Manufacturing and Materials Processing;2023-01-15

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