摩擦学
材料科学
酒窝
润滑
摩擦学
纹理(宇宙学)
沟槽(工程)
复合材料
冶金
计算机科学
人工智能
图像(数学)
作者
Shubrajit Bhaumik,Viorel Paleu,Dhrubajyoti Chowdhury,Adarsh Batham,Udit Sehgal,Basudev Bhattacharya,Chiradeep Ghosh,Shubhabrata Datta
出处
期刊:Materials
[Multidisciplinary Digital Publishing Institute]
日期:2022-11-27
卷期号:15 (23): 8445-8445
被引量:10
摘要
The present work investigates the friction reduction capability of two types of micro-textures (grooves and dimples) created on steel surfaces using a vertical milling machine. The wear studies were conducted using a pin-on-disc tribometer, with the results indicating a better friction reduction capacity in the case of the dimple texture as compared to the grooved texture. The microscopic images of the pin surface revealed deep furrows and significant damage on the pin surfaces of the groove-textured disc. An optimization of the textured surfaces was performed using an artificial neural network (ANN) model, predicting the influence of the surface texture as a function of the load, depth of cut and distance between the micro-textures.
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