Image Texture Description, Surface-Grains Structure Morphology and Prediction of Wear Parameters for Mg/B4C Composites Using Response Surface Methodology

Murugesan, K.; Suresh, S.; Vettivel, S.C.; Prema, C. Emmy; Arun, J.; Thirunamakodi, P.

doi:10.1007/s12540-021-01042-2

Cited by 2 publications

(1 citation statement)

References 44 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Response surface analysis is a fitting method combining experimental design and mathematical modeling [27][28][29][30]. The mathematical relationship between variables and results in the global range is regressed and fitted by testing representative local points, and the optimal value corresponding to the factors is obtained.…”

Section: Design Factors and Levels Of Response Surface Testmentioning

confidence: 99%

Comparative study on single-objective optimization and multi-objective optimization of convex textured friction couple performance

Yu¹,

Shi²,

Zhao³

et al. 2023

Surf. Topogr.: Metrol. Prop.

View full text Add to dashboard Cite

To improve the friction performance of the friction pair of the hydrostatic thrust bearing, the surface with convex textures is designed at the bottom of the oil cavity. The result shows that the oil cavity with the convex structures can significantly improve the friction characteristics of the hydrostatic thrust bearing. To further study the effect of texture parameters (texture radius, texture spacing and texture quantity) on its performance, the method based on the full factor test is used for single objective optimization, and the methods based on the BBD response surface and the NSGA-II algorithm are used for multiple target collaborative optimization. The full factor test with the main effect shows that the best combination with the bearing capacity is 0.5 mm, 7 mm, 7 and 4.91%, and the best combination with the rigidity is 0.3 mm, 5 mm, 5 and 3.61%. The influence of textures on the maximum temperature of oil film is basically unchanged and the bearing capacity is inversely proportional to the friction factor. When considering the interaction between texture parameters, the BBD response surface method is used for multi-objective optimization. The result shows that when the texture radius is 0.7 mm, the texture spacing is 7 mm, the quantity of textures is 6.2, and the area ratio is 8.73%, the optimal response values can be obtained, which the bearing capacity is 8519.31 N and the rigidity is 1.31868 E+12 N/m. The multi-objective optimization analysis of the NSGA-II algorithm based on the BBD response surface model shows that the algorithm can obtain a Pareto solution set. Each set of solutions obtained by the NSGA-II algorithm is the optimal solution. Both the BBD response surface method and the NSGA-II algorithm can be used to solve multi-objective problems.

show abstract

Section: Design Factors and Levels Of Response Surface Testmentioning

confidence: 99%