Prediction of surface treatment effects on the tribological performance of tool steels using artificial neural networks
The present paper discussed the development of a reliable and robust artificial neural
network (ANN) capable of predicting the tribological performance of three highly alloyed tool …
network (ANN) capable of predicting the tribological performance of three highly alloyed tool …
Hybridizing five neural-metaheuristic paradigms to predict the pillar stress in bord and pillar method
Pillar stability is an important condition for safe work in room-and-pillar mines. The instability
of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced …
of pillars will lead to large-scale collapse hazards, and the accurate estimation of induced …
Surface roughness modeling using machine learning approaches for wire electro-spark machining of titanium alloy
Purpose In the present study, wire electro-spark machining of Titanium alloy is performed
with the machining parameter such as spark-on time, spark-off time, current and servo …
with the machining parameter such as spark-on time, spark-off time, current and servo …
Surface roughness prediction of AISI D2 tool steel during powder mixed EDM using supervised machine learning
Surface integrity is one of the key elements used to judge the quality of machined surfaces,
and surface roughness is one such quality parameter that determines the pass level of the …
and surface roughness is one such quality parameter that determines the pass level of the …