Component parameter optimization of strengthen waterjet grinding slurry with the orthogonal-experiment-design-based ANFIS

In this paper, the component parameter optimization of strengthen waterjet grinding slurry, by using the orthogonal-experiment-design-based adaptive neural fuzzy inference system (ANFIS), was conducted to obtain an optimal grinding performance for bearing ring. An improved ANFIS system based upon orthogonal experiment design was proposed to optimize the component parameters of multi-phase waterjet slurry in grinding practices and gives better grinding quality on objective ring surface. This approach involves the following steps: preparation of experimental environment, determination of measure indexes for surface grinded, orthogonal experiment design with fuzzy logic rules, establishment of ANFIS system, parameter optimization for waterjet slurry, and actual improvement verification of waterjet grinding on ring surface. The objective of this research is determining the optimal component parameters of waterjet slurry with fewer experimental iterations compared to other alternative approaches. Statistical analysis and result comparisons support its efficiency and reliability in practical machining operations; therefore, a more stable grinding process and better finished surface can be obtained. It was concluded that the proposed ANFIS system can be used as a suitable and effective tool to investigate the complicated influential correlations between slurry components and grinding effectiveness in bearing manufacturing conditions, which makes an optimal machining performance compared to other experimental practices in return.