A hybrid model for evaluating the sawability of stones through the performance of frame sawing machine

Abstract Modeling of stone processing, especially the sawability of stones (SS) has been studied extensively over the last decade. Artificial neural network (ANN) is an effective method to deal with complex engineering problems. However, ANN has some limitations such as trapped in local minima and cannot analyze the sensitivity of parameters. To fill the gaps, the primary objectives of this paper are to optimize ANN by genetic algorithm (GA) and introduce mean impact value (MIV) algorithm for parametric sensitivity analysis. Therefore, a hybrid model was proposed for predicting SS based on BP neural network (BPNN), MIV algorithm, and GA. Further, the main factors that affect SS were analyzed based on the grinding mechanism and fracture mechanics. The proposed model is not only meaningful to analyze SS, but provides a novel foundation for studying other stone procedures in future.