A study on friction induced tribological characteristics of steel 316 L against 100 cr6 alloy under different lubricating conditions with machine learning model

The material steadily wears away from touching surfaces when two solid entities are constantly moving against one other. When more parameters and extreme materials are involved in tribological testing, then it is very difficult to analyze and observe the working phenomena. With this aim, this study uses the gaussian process regression (GPR) approach to estimate friction forces when testing SS 316L against 100 Cr6 alloy under cryogenic and cryo + minimum amount lubrication conditions. The friction forces from ball-on test experiments were used to develop the prediction models. Then, the wear surfaces and surface morphology are analyzed under cryo and cryo+MQL conditions. The results demonstrated that the combination of MQL and CRYO cooling reduced the friction forces more than 10 times for sliding distances above ~30m and loads below ~25n. Hence, the cryo+MQL conditions are beneficial in enhancing the tribological features due to the dual cooling and lubricating effects.