A fuzzy data-driven and rule-based reasoning system for setting the nano- particle milling process parameters

In this research, an integration of adaptive resonance theory (ART-2) neural networks, fuzzy set theory, variable precision rough set (VPRS), decision tree C5.0 algorithm and fuzzy rule-based control is proposed to develop a fuzzy rule-based reasoning system to set a nano- particle milling process. The characteristics of the proposed system are to use data-driven to do fuzzification and rule extraction instead of directly using domain experts. In addition, the regulation scheme of the parameter setting system is based on adjusting the process parameters by using its current deviation from the optimal parameters. The verified results show that the proposed process parameter setting system indeed can be applied to guide engineers to set the process parameters when a nano-particle milling process is shifted. It can be applied to adjust the milling process from current shift condition back to near-optimal condition.