Grinding optimization using nondestructive testing (NDT) and empirical models

Abstract

Proper choice of grinding parameters is important because productivity is limited by the possibility of grinding burns. Nondestructive testing (NDT), as Barkhausen noise (BN), can provide a tool for determining parameter limits. However, BN requires a threshold value for approval and requires multiple experiments to provide grinding parameters corresponding to high productivity. Other limiting process outputs, such as surface roughness, need to be fulfilled. In this study, process modeling, experiments and NDT are combined to produce optimized grinding parameters that fulfill the process output requirements and reduces the number of experiments for a new component. Design of experiments (DOE) grinding tests were performed with cutting power measurements. The outcome was verified with BN and surface roughness outputs. Then, using polynomial fitting, regression models were fitted into the BN, surface roughness and cutting power data. Malkin’s temperature model was utilized to analyze the temperature rise in the grinding zone. Combining all models, a grinding productivity optimization problem was defined from a specific material removal rate perspective. The polynomial fits of BN results and cutting power showed good prediction capability, as expressed by their R2 values. The study provides a novel method to choose grinding parameters to produce quality parts with minimal loss of productivity.