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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Corrosion resistance of materials is predominately dependent on their surface roughness. Therefore, surface finishing techniques can effectively improve the corrosion resistance of the components. Ultrasonic-assisted burnishing (UAB) process is a newly developed surface finishing technique capable of flattening the surface of components without material removal. This research experimentally investigated the effects of amplitude in the UAB process on surface roughness and corrosion performance of AA7075-T6 aluminum alloys. Turned sample (control) was treated by conventional burnishing (CB), followed by UAB with an amplitude of 10, 20, and 30 µm. Then, the surface roughness, microstructure, microhardness, and corrosion resistance of the treated samples were assessed. The surface roughness showed an improvement upon burnishing of the samples, where the best surface was achieved by UAB with an amplitude of 10 µm. UAB process also led to grain refinement such that finer grains could be achieved by increasing the amplitude. Microhardness also increased after the UAB process which got intensified by increasing the amplitude. The turned sample showed the least corrosion resistance, while the UAB-treated specimens (amplitude of 10 µm) exhibited minimal corrosion rate. Furthermore, the enhancement of UAB amplitude increased the surface roughness, causing a decline in corrosion resistance.

Alumina is one of the most important ceramic materials in the industry due to its advantageous properties, such as electrical resistivity and high hardness. The machining of this material encounters several difficulties, and it is usually machined using non-traditional machining processes. Among these processes, ultrasonic-assisted electrochemical discharge machining has been widely used for glass machining. However, this machining process is expected to be accompanied by acceptable results in the case of alumina. Therefore, in this study, the effects of process parameters such as ultrasonic vibration amplitude, voltage, pulse-on time, and pulse-off time on material removal rate, depth, overcut, and taper angle have been experimentally studied in the machining of alumina. The results revealed that hole depth increased up to 52% using ultrasonic vibrations with an amplitude of 34 µm.