Chemical reaction optimization algorithm for machining parameter of abrasive water jet cutting

Abrasive water jet cutting is one of the most prominent technique for the cutting of wide range of materials. Selection of the input process parameter with optimized condition determines the productivity and process applicability. Present paper describes the nature inspired meta-heuristic chemical reaction optimization (CRO) algorithm for the selection of input process parameter for the most favorable material removal rate (MRR). In the present paper ductile material model for the MRR is considered by CRO for the solution approach. Five input variables namely water jet pressure, diameter of nozzle, feed rate of nozzle, mass flow rate of abrasive and mass flow rate of water were considered for the material removal rate in abrasive water jet machining. It was found that CRO algorithms delivers improved performance compare to different algorithms such as genetic algorithm, cuckoo search, teaching learning-based optimization and teaching learning based cuckoo search algorithm. The predicted results can be used for the identification of the input process parameter to enhance outcome at the acceptable range for machining.