Optimization of Grinding Parameters of Tool Steel by the Soft Computing Technique.

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 µm), wheel speed (15, 20, 25 m/s), feed rate (100, 300, 500 mm/s), and incidence angle (0, 30, 45 de grees) 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 µm, wheel speed of 15.02 mm/s, feed rate of 369.45 mm/s, and incidence angle of 44.98 de grees, 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.

Experimental and numerical investigation of heat generation and surface integrity of ZrO2 bioceramics in grinding process under MQL condition.

Zirconia bioceramics has tremendous potential in medical applications owing to biocompatibility and high mechanical properties. Meanwhile, thermal damage and surface defects limit the grindability to achieve the desired properties. Therefore, this research provides an investigation of various grinding parameters on heat generation and surface morphology using a diamond wheel. In addition, a triangular and parabolic moving heat flux is used for heat distribution analysis based on FEM-model. The parabolic model more corresponds to experimental compared to the triangular heat flux, with an average deviation of <5% and 6.5% under dry and MQL, respectively. The response surface methodology is applied to extract a statistical representation of inputs and outputs. Dry grinding temperature obtained in range of 200-540 °C, which by applying MQL, it decreased by 16-35%. Increasing cutting depth would worsen the MQL efficiency in force and temperature. Results indicate the impact of cutting depth on temperature and force is greatest, followed by the effect of feed-rate, and that of wheel speed is the least. Thus, the increasing feed-rate should be utilized to preserve the high removal rate. SEM images indicate material removal mechanism is accomplished by plastic and brittle mode. Furthermore, MQL and a combination of low depth of cut could effectively decline the surface roughness and defects formation by decreasing the brittle material removal mechanism in one step. MQL reduced surface roughness by 46% compared with dry grinding, so that its performance increase in higher cutting depth. Because at higher cutting depths, the MQL changes the prevailing chip removal mechanism from brittle to ductile-regime grinding.