The surface roughness of the ground parts is an essential factor in the assessment of the grinding process, and a crucial criterion in choosing the dressing and grinding tools and parameters. Additionally, the surface roughness directly influences the functionality of the workpiece. The application of artificial intelligence in the prediction of complex results of machining processes, such as surface roughness and cutting forces has increasingly become popular. This paper deals with the design of the appropriate artificial neural network for the prediction of the ground surface roughness and grinding forces, through an individual integrated acoustic emission (AE) sensor in the machine tool. Two models were trained and tested. Once using only the grinding parameters, and another with both acoustic emission signals and grinding parameters as input data. The recorded AE-signal was pre-processed, amplified and denoised. The feedforward neural network was chosen for the modeling with Bayesian backpropagation, and the model was tested by various experiments with different grinding and neural network parameters. It was found that the predictions presented by the achieved network parameters model agreed well with the experimental results with a superb accuracy of 99 percent. The results also showed that the AE signals act as an additional input parameter in addition to the grinding parameters, and could significantly increase the efficiency of the neural network in predicting the grinding forces and the surface roughness.

Grinding is a promising machining method for finishing workpieces that need a smooth surface with tight tolerances. Due to the high thermal energy generated in the grinding zone, an accurate prediction of workpiece temperature plays a crucial role in the design and optimization of the grinding process. Finite difference method (FDM) is used for simulating the temperature distribution in a workpiece subjected to shallow grinding using a DuFort–Frankel explicit scheme. Moreover, two simple methods, one for modeling the effect of material removal in shallow grinding and the other for calculating the heat partition, are presented. A semi-empirical correlation of cooling jet is applied to calculate the convection heat transfer coefficient (CHTC) over the grinding surface. Experiments were carried out to verify the simulation results, and a good agreement was observed between the simulation and experimental data. An analysis of the results indicated that the misestimation of workpiece temperature could occur when the effect of the material removal rate is not considered in the simulation. The simulation results showed that the heat flux flow is one-dimensional for a high Peclet number, while a two-dimensional heat flux flow prevails for a low Peclet number. The results revealed that reducing the Peclet number and extending the depth of cut increase the heat partition. The study of wet grinding demonstrated that, for efficient cooling, the coolant should be applied directly to the contact zone. Moreover, using water-based emulsion as a coolant was more effective than palm and sunflower oils.