In this paper, dry an electro-discharge machining (Dry EDM), one of the newest machining process which differs mainly from conventional EDM in using gaseous dielectric along with tool electrode rotation, has been studied. Gap voltage, discharge current, pulse-on-time, pulse-off time, dielectric gas pressure, and electrode rotational speed have been considered as effective input parameters. Response surface methodology (RSM) has been used to optimize the machining performance with respect to Material Removal Rate (MRR). Base on the results and analysis of running experiments, it can be concluded that MRR increases by increasing gap voltage, discharge current, the ratio of pulse-on time over pulse-off time, input gas pressure, and electrode rotational speed. There also exists an optimum amount of pulse-on time determined according to the machining circumstances. Also the Material Removal Rate in dry EDM has been improved compared with that in conventional EDM in identical conditions.

In this work, the influence of different EDM parameters (current, pulse on-time, pulse off-time, arc voltage) in finishing stage on the Material Removal Rate as a result of application copper electrode to a work piece (cold work steel DIN1.2379) has been investigated. Design of the experiments was chosen full factorial and statistical analysis was used for analysis of experimental data in this study. Finally, to select the optimal machining conditions presented a mathematical model to achieve suitable Material Removal Rate.

This paper presents a model to predict Material Removal Rate (MRR) in Micro Ultrasonic Machining (micro-USM). The proposed model is developed based on the ductile-mode of Material Removal in micro-USM process. The correlation between ductile Material Removal Rate and process parameters including frequency and amplitude of the ultrasonic vibration, particle size, and slurry concentration is presented. The proposed predictive model is verified by performing micromachining experiments using two types of workpiece Materials including silicon and quartz at various process parameters levels. The results show that the MRR increases with a rise in vibration amplitude for both silicon and quartz Materials. The experimental MRR values follow a trend similar to that of predicted MRR values. However, the predicted MRR values are higher than the measured MRR values for both silicon and quartz Materials. The measured MRR values for ductile Removal mode were found to have a considerable increase at vibration amplitudes of 2  m and 2. 4  m for silicon and quartz, respectively, which is in favour of increasing the accuracy of the model prediction.

Magnetorheological polishing is an optimized mechanism for accuRate surface polishing. By using this structure change, the common problems of payment, such as creating the aggregate structure of abrasives, have been solved. In this regard, studying and checking process parameters will be a solution to increase efficiency and best performance. In this research, the parameters of magnetic pole rotation speed, workpiece rotation speed, turning radius, gap, and machining time have been investigated. Using the regression equation of the chipping Rate, the parameters have been analyzed by a statistical sensitivity analysis using the Sobol method. The obtained results state that the rotation speed of the magnetic pole at 37% and the rotation speed of the workpiece at about 30% are considered the most effective parameters, and the turning radius at 15% and the machining gap at 16% are considered as the next parameters. Machining time is known to be the least influential parameter in this process by 1%. Based on this, the effect of time parameters on this process can be ignored.

Today, various military, aerospace, automotive, etc. industries need Materials with a high strength-to-weight ratio. The use of metal-based composite Materials, especially aluminum-based composites, has increased greatly. Machining is needed to achieve high dimensional accuracy in products made with aluminum-based composites. Due to the presence of reinforcing Material such as silicon carbide, machining of this type of Material is difficult. Therefore, it is important to study the parameters affecting the machining of aluminum-based composites. In this study, the effect of spindle speed, feed Rate, depth of cut and percentage of reinforcing particles were discussed using experimental and statistical test methods. The responses of surface roughness and Material Removal Rate were investigated. The behavior of the input parameters on the responses of the process has been carefully investigated quantitatively and qualitatively. Answers have also been optimized. According to the obtained results, the spindle speed has the greatest effect on the surface roughness. Also, feed Rate 33%, spindle speed 28%, depth of cut 26% and the percentage of reinforcing particles 13% have an effect on the chipping Rate.

Drilling machining processes is one of the most widespread applications in manufacturing of various mechanical parts and equipment. One of the interesting technologies in drilling, micro-drilling process that is critical in industries such as precision manufacturing nozzle for refueling vehicles, parts and camera clock is used. The drilling operations of metals, for having holes with desired quality and accuracy, it is required that various factors such as spindle rotation speed, feed Rate and drill diameter evaluated. In drilling, increase the speed of spindle rotation speed machining and Material Removal Rate is also increasing, and the other side is the erodes faster tool. Therefore, careful selection of various parameters in the drilling operation is necessary. In this paper, by doing experiments, a linear regression model to predict the Rate of Material Removal Rate during the second drilling operation based brass spindle rotation speed, feed Rate, tool diameter and effective interactions are presented. Then, using statistical sensitivity analysis Sobol, the impact of parameters on Material Removal Rate is obtained. The results of the sensitivity analysis show that the Rate of Material Removal Rate has the greatest impact on advancing the tool diameter is the least effect on the Rate of Material Removal.

Nickel-based alloys are largely used in aerospace, power generation and petrochemical industries. Machining of these alloys is usually accompanied with high cutting force and temperature and low surface quality. In order to avoid these limitations, small values of cutting parameters are used in machining of nickel-based alloys that significantly decrease the productivity. In this paper, the effects of cutting speed, feed Rate and depth of cut on the surface roughness in dry turning of nickel-based super alloy Inconel600 by using carbide tool in finishing range have been experimentally investigated. Full factorial design of experiments was used and totally 48 tests were done. Results of the analysis of variance showed that feed Rate 69%, depth of cut 6% and cutting speed 2% affects on surface roughness of Inconel 600. In addition to measuring the surface roughness, chip Removal Rate was calculated for each test and then, for achieving to minimum surface roughness and maximum chip Removal Rate at the same time, the optimal machining parameters have been achieved by using neural network and imperialist competitive optimization algorithm. By using this algorithm, several levels of optimum cutting parameters are set and a process planner according to the required surface roughness and Material Removal Rate can use these cutting parameters.

In this research, the effects of cutting parameters on Material Removal Rate and surface roughness, are investigated. Therefore, after that the comprehensive model of low‐immersion milling is developed, the optimum cutting conditions has to be found for optimizing all of them. The stability criterion is considered as the optimization constraint which is calculated by TFEA. On the other hand, instead of using explicit equation for calculating surface roughness, such as previous works, surface roughness is calculated by TFEA for all of the cases that are needed. Finally, the ability of Genetic algorithm, Particle Swarm Optimization and Imperialist Competitive Algorithm for searching optimum cutting parameters are compared and the results are reported. By comparing the results of the three algorithms it is shown that the ICA is more powerful to deal with nonlinearity aspects of the problem and to tackle sticking in local minimums. Also it is demonstRated that the convergence Rate of the ICA is faster than the other two methods. Finally, experiments to confirm the changes of the objective function toward optimal point are done and error percentage of objective function at obtained optimal point compared with experimental result is determined.

In recent years, the oscillations use of ultrasonic waves has been strongly increased in various industries. Therefore, respecting to the application type of these waves in different industrial fields, extensive and wide researches have been started which can be helpful and competent in optimizing the effective parameters in methods based on the ultrasonic waves application. One of the special uses of them is in machining. The ultrasonic machining is a mechanical Material Removal which is applied for creating various holes and cavities on the brittle and fragile Materials. In this study, in order to ensure the practical behavior of the machine, be able to investigate the resulted findings and also obtain the optimized machining parameters, the drilling operation was performed on the glass workpiece of super clear crystal type by designing the experiment. The results in different conditions show that the Material Removal has been increased by increasing the frequency, the static load and the machining power. The highest Material Removal has been achieved by choosing a frequency of 40 kilohertz, static load of 1080 grams and power of 130 watts. Also, the created holes have a desirable quality of roundness tolerance.