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Grinding is one of the most complex and accurate machining processes, and the efficiency of the grinding wheel depends significantly on its surface properties. This work aims to propose an algorithmic manner that reduces the cost and time to conduct grinding of an optimized DIN 1.2080 tool steel (SPK) using a soft computing technique to obtain the best combination of input parameters including depth of cut (20, 40, 60  μ m ), wheel speed (15, 20, 25  m / s ), feed rate (100, 300, 500  m m / s ), and incidence angle (0, 30, 45 d e   g r e e s ) with respect to output parameters consisting of average surface roughness and specific grinding energy. According to the input parameters and their levels, an experiment using fractional factorial design of experiment (RFDOE) was designed. Later on, two parallel feed-forward backpropagation (FFBPNN) networks with similar topology made up of 4, 11, and 1 units in their input, hidden, and output layers are trained, respectively. After sensitivity analyses of networks for determination of the relative importance of input variables, a genetic algorithm (GA) adopting linear programming (LP) based on Euclidean distance is coupled to networks to seek out the best combinations of input parameters that result in minimum average surface roughness and minimum specific grinding energy. The findings revealed that RFDOE provides valid data for training FFBP networks with a total goodness value of more than 1.99 in both cases. The sensitivity analyses showed that feed rate (38.97%) and incidence angle (33.94%) contribute the most in the case of average surface roughness and specific grinding energy networks, respectively. Despite the similar surface quality based on scanning electron microscopy (SEM), the optimization resulted in an optimized condition of the depth of cut of 25.23  μ m , wheel speed of 15.02  m m / s , feed rate of 369.45  m m / s , and incidence angle of 44.98 d e   g r e e s , which had a lower cost value (0.0146) than the optimum one (0.0953). Thus, this study highlights that RFDOE with a hybrid optimization using FFBP networks-GA/LP can effectively minimize both average surface roughness and specific grinding energy of SPK.

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Grinding operation is one of the most complex and accurate machining processes whose performance is highly dependent on the surface conditions of the grinding wheel. As the friction between abrasive grains and the workpiece results in a large amount of heat, the use of coolant can prevent the workpiece surface from burning, microstructure alteration, subsurface damages, and hardness variation. To get better the cleaning of the abrasive grains on the surface of the wheel during the grinding process, this paper is focused on the performance of an auxiliary compressed air jet system during grinding of hard-to-grind materials. Moreover, the wheel loading of the grinding wheel will also be evaluated. Different incident angles of the compressed air nozzle (0˚, 30˚, 45˚, and 60˚) were examined. Furthermore, conventional flood coolant, as well as drying with and without a cleaning system, was applied for comparison purposes. Surface roughness, tangential forces, specific energy, wheel loading analysis, and SEM images were evaluated where the incident angle of 45˚ led to the best results. Wheel loading resulted in the accumulation of chips between abrasive grains to the top of cutting grains. Detection and identification of loading has been operated based on image processing technique.