Comparative determination of unit wear in circular stone cutting with conventional statistical methods and data mining techniques

Natural stones are subjected to some processes in stone processing plants for producing stone products, such as cladding, paving, tiling, and decorative materials. Cutting is one of the most critical processes in stone processing. Diamond segments are still widely used in cutting and other processing steps. The parameter that affects the usage of segments is diamond segment wear. Nowadays, unit wear (UW) on the diamond segment should be kept at a minimum in cutting operations for economic production. Thus, the prediction of UW has become a vital issue in stone processing. In this study, UW on diamond segments after stone cutting was evaluated in terms of stone characteristics, operating parameters of the stone cutting machine, vibration amplitude, and sound level measured during cutting. Conventional statistical (CS) methods and data mining (DM) techniques were used to predict unit wear, and these methods were compared. Performed assessments showed that almost all DM techniques give more reliable results than CS methods. Artificial neural networks (ANN) and k-nearest neighbor (k-NN) techniques significantly predicted the UW values more accurately than the other assessment techniques. The results showed a high coefficient of determination with ANN and k-NN obtained as R2= 0.936 and 0.919, respectively. DM techniques can be more efficient in evaluating complex cutting data.