As a successful solution applied to hard machining, the minimum quantity lubricant (MQL) has already been established as an alternative to flood coolant processing. The optimization of MQL parameters and cutting parameters under MQL condition are essential and pressing. The study was divided into two parts. In the first part of this study, the Taguchi method was applied to find the optimal values of MQL condition in the hard milling of AISI H13 with consideration of reduced surface roughness. The L9 orthogonal array, the signal-to-noise (S/N) ratio and analysis of variance (ANOVA) were employed to analyze the effect of the performance characteristics of MQL parameters (i.e., cutting fluid type, pressure, and fluid flow) on good surface finish. In the results section, lubricant and pressure of MQL condition are determined to be the most influential factors which give a statistically significant effect on machined surfaces. A verifiable experiment was conducted to demonstrate the reliability of the results. In the second section, the optimized MQL parameters were applied in a series of experiments to find out cutting parameters of hard milling. The Taguchi method was also used to optimize the cutting parameters in order to obtain the best surface roughness. The design of the experiment (DOE) was implemented by using the L27 orthogonal array. Based on an analysis of the signal-to-noise response and ANOVA, the optimal values of cutting parameters (i.e., cutting speed, feed rate, depth-of-cut and hardness of workpiece) were introduced. The results of the present work indicate feed rate is the factor having the most effect on surface roughness.

As a successful solution applied to electrical discharge machining (EDM), powder-mixed electrical discharge machining (PMEDM) has been proposed as an upgrade of the EDM process. The optimization of the process parameters of PMEDM is essential and pressing. In this study, Taguchi methods and analysis of variance (ANOVA) were used to find the main parameters affecting surface roughness in the EDM process with SiC powder-mixed-dielectric of hardened 90CrSi steel. The PMEDM parameters selected were the powder concentration, the pulse-on-time, the pulse-off-time, the pulse current, and the server voltage. It was found that SiC powder exhibits positive effects on reducing surface roughness. The roughness obtained with the optimum powder concentration of 4 g/L was reduced by 30.02% compared to that when processed by conventional EDM. Furthermore, the pulse-off-time was found to be the most influential factor that gave an important effect on surface roughness followed by the powder concentration. The EDM condition including a powder concentration of 4 g/L, a pulse-on-time of 6 µs, a pulse-off-time of 21 µs, a pulse current of 8 A, and a server voltage of 4 V resulted in the best surface roughness.

In metal cutting, surface roughness plays an important role in assessing the quality of processed products. The roughness depends greatly on the selection of machining parameters such as cooling conditions and cutting parameters. For this purpose, cooling conditions including dry, MQL, and Silica-based nanofluid MQL as well as cutting parameters including cutting speed, depth-of-cut and feed-rate were investigated to determine their influence on machining roughness during hard milling of AISI H13 steel. The DOE method developed by G. Taguchi was used to design the experiments. An analysis of the signal-to-noise response and ANOVA were carried to obtain the optimal values of cutting parameters for minimizing surface roughness. The results of the present study show that Silica-based nanofluid MQL, minimum feed-rate, minimum depth-of-cut, and maximum cutting speed is an optimal cutting condition for reducing machining roughness.

This paper presents an experimental study of the positive effects of vibration-assisted deep drilling of aluminum alloy Al-6061. The four most important evaluation criteria in drilling—machinability, workpiece temperature, torque, and material removal rate—were chosen to be investigated. Holes with a depth-to-diameter ratio of 13 were drilled by high speed steel (HSS) twist drill bits of 3 mm diameter, using both methods of conventional drilling (CD) and ultrasonic-assisted drilling (UAD). Three levels of axial force of 6 kgf, 9 kgf, and 12 kgf were kept constant for each pair of comparison experiments. It was found that workpiece temperature and torque not only changed from one drill to the other, but were also dependent on the hole depth being drilled. Comparisons were made in-pair between CD and UAD under the same axial force and at the same order of hole numbers. The result shows that the material removal rate with UAD was up to 3.5 times higher than that with CD and the average workpiece temperature and torque in UAD were reduced by 3.5 and 6 times, respectively. Moreover, tool life in UAD was observed to increase from 2.5 to 5 times, in terms of number of holes drilled, compared to that in CD.

Productivity and quality are always two goals in the production process. In metal cutting, two prominent representatives of quality and productivity are roughness and material removal rate (MRR). In this study, the Response Surface method was used to perform single-objective and multiobjective optimizations during the hard milling of SKD11 steel. From there, comparative analyzes are carried out to give effective advice for different approaches in actual production. The selected inputs are the nanoparticle concentration in the cutting oil and three typical cutting parameters including cutting velocity, depth of cut, and feed rate. Each input will have three levels including low, high and average. The L27 orthogonal array developed by Taguchi was applied to the experimental design. In addition, ANOVA was also used to evaluate the statistical indicators of the study. The results of single-objective optimization show that the feed rate is the main influencing factor for the roughness followed by the nanoparticle concentration. They contribute 51.2 and 21.12 of the total roughness effect, respectively. On the other hand, the main factors affecting the material removal rate are the depth of cut and feed rate. In multiobjective optimization, a compromise solution has also been proposed to achieve small roughness and high material removal rate. The minimum roughness was 0.1956μm and the maximum material removal rate was 1479.8688mm³/min when applying the multiobjective optimal machining condition.