𝔖 Bobbio Scriptorium
✦   LIBER   ✦

Prediction of wear and surface roughness in electro-discharge diamond grinding

✍ Scribed by Sanjeev Kumar; S.K. Choudhury


Publisher
Elsevier Science
Year
2007
Tongue
English
Weight
299 KB
Volume
191
Category
Article
ISSN
0924-0136

No coin nor oath required. For personal study only.

✦ Synopsis


The present work focuses on prediction of wheel wear and surface roughness using two techniques, namely design of experiments and neural network. Effect of process parameters, such as pulse current, duty ratio, wheel speed and grain size on output responses, namely, wheel wear and surface roughness of high speed steel (HSS) were investigated experimentally.


πŸ“œ SIMILAR VOLUMES


Ground surface integrity of granite by u
✍ J. Xie; Y.J. Liu; Y. Tang; A. Kubo; J. Tamaki πŸ“‚ Article πŸ“… 2009 πŸ› Elsevier Science 🌐 English βš– 912 KB

Dry electro-contact discharge (ECD) dressing of metal-bonded #600 diamond grinding wheel is proposed for grinding of various granites. As compared to mechanical GC dressing, Dry ECD dressing can not only protrude fine diamond grains from wheel metal-bond without any damage, but also eliminate bond-t

The use of neural networks for the predi
✍ HΓΌlya KaΓ§ar Durmuş; Erdoğan Γ–zkaya; Cevdet MeriΒ·Γ§ πŸ“‚ Article πŸ“… 2006 πŸ› Elsevier Science βš– 365 KB

Artificial neural networks (ANNs) are a new type of information processing system based on modeling the neural system of human brain. Effects of ageing conditions at various temperatures, load, sliding speed, abrasive grit diameter in 6351 aluminum alloy have been investigated by using artificial ne