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Using Taguchi design of experiments (DoE), experiments were conducted with 3 factors and 3 levels. The factors were the depth of cut, spindle speed, and feed. The responses were surface roughness, flank wear, material removal rate, and spindle vibration along $x$ ($V_x$), $y$ ($V_y$), and $z$ ($V_z$) axis. To convert the multi-response optimization problem into a single response optimization problem, the technique for order of preference by similarity to ideal solution (TOPSIS) was applied. The S/N of the closeness coefficients from TOPSIS was calculated and optimum machining conditions were obtained. Further, analysis of variance (ANOVA) was performed to verify which input parameter significantly affects the output responses. From TOPSIS optimization, the responses like surface roughness and flank wear were decreased by 0.99% and 2.55%. The vibration in $x$, $y$, and $z$-axis decreased by 3.84%, 16.87% and 12.48% respectively. This optimization significantly enhances the machining characteristics.

This work aims to perform the multi-response optimization for abrasive waterjet machining (AWJM) of glass fiber reinforced plastics (GFRP). The experiments were conducted with AWJM factors like pressure (P), traverse speed (TS), and standoff distance (SOD) at three levels. Taguchi’s L₉ orthogonal array (OA) was used to design the experiments. The influence of control factors was evaluated by measuring the surface roughness and taper angle while cutting GFRP. The optimum parameter for an individual response was obtained through Taguchi’s $S$/$N$ and multi-response optimization was performed with TOPSIS. From TOPSIS, the optimal parameter of the pressure of 200 MPa, standoff distance (SOD) of 1.5mm, and traverse speed (TS) of 25mm/min were found. After optimization, the taper angle was decreased by 1.41%. The influence of cutting variables on the responses was statistically analyzed through analysis of variance. It was observed that the pressure has a significant effect on multi-response characteristics and a contribution of 85.90%. After, AWJM, the surface was examined using SEM analysis and found the deformation and pull-out of fibers.

In this work, initially, the raw AISI 52100 bearing steel was heat-treated to obtain 40 HRC and 45 HRC workpiece hardness. Further, dry hard turning tests were carried out to study the impact of workpiece hardness ($H$), cutting speed ($v$), feed ($f$), and depth of cut ($a$) on cutting force (Fy), surface roughness (Ra), and sound intensity (SI). An economically viable PVD-coated carbide turning tool was implemented for the experiments. The Taguchi L${}_{18}$ (2–3 mixed level) design of experiments was employed to establish the experimental plan in order to save the experimental time, energy, and cost of manufacturing. The results disclosed that the feed has the prevailing consequence on surface roughness with a 96.3% contribution, while it also significantly affects the cutting force with a contribution of 13.8%. The contribution of cutting speed and workpiece hardness on the cutting force was reported as 48.3% and 35.1%, respectively. Higher workpiece hardness required more energy for plastic deformation as a result the cutting force increases with leading hardness. The sound intensity was dominantly influenced by depth of cut (53.3%) and cutting speed (40%). Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) was performed to determine the optimum machining parameters. According to the TOPSIS, the optimum level of cutting parameters was predicted as 40 HRC hardness ($H$), 150m/min cutting speed ($V$), 0.15mm/rev feed ($f$), and 0.1mm depth of cut ($a$) while the optimal result of Fy, SI, and Ra were noted as 27.66N, 70.7dB, and 0.86$\mu$m individually.

Laser cutting is a one of the efficient manufacturing processes in industry to cut the hard materials by vaporizing. Stainless steel (SS347) is the most popular material for many applications due its unique characteristics such as efficiency to retain good strength with no inter-granular corrosion even at elevated temperatures. However, the cutting or machining of this material is very difficult. On the other side, the machining cost of laser process is high when compared with other processes. In this work, GRA and TOPSIS techniques are used to study the laser cutting process parameters of SS347. The obtained results were compared with the data mining approach. The input parameters are power, speed, pressure and stand-off distance (SOD) and the output responses of surface roughness, machining time and HAZ are considered. The set of experiments were constructed by using the Taguchi’s L9 method. The predicted closeness value of TOPSIS is greater than the GRA technique and the predominant factor observed is SOD followed by pressure, speed and power. In this work, C4.5-decision tree algorithm is applied to find the most influential parameter. It also represents the low-level knowledge of data set into high level knowledge (If-Then rules form). This investigation reveals that both TOPSIS and data mining suggested the SOD as predominant factor. This result of the optimized process parameters supports the laser assisted manufacturing industries by providing optimized output. Better results were obtained using the optimized set of parameters with the machining time, HAZ and surface roughness being 7.83 s, 0.09 mm and 0.86 $\mu$m, respectively. The results of this work would be very useful for automobiles and aircrafts industries where SS347 is highly employed.

In the current scenario, many researchers aspire to develop biodegradable material for biomedical implant applications. Magnesium (Mg)-based alloys are most promising materials since they have mechanical properties similar to human bone. In this study, Mg alloy AZ31 matrix was reinforced with a seashell powder (2wt.%) and zirconium dioxide (10wt.%) using bottom pouring stir casting furnace. Scanning electron microscope (SEM) with energy dispersive spectroscopy (EDS) images confirms the proper distribution of reinforcement throughout the matrix. This study analyzed the influence of WEDM process parameters for the material removal rate (MRR) and surface roughness (SR) of the proposed composite. According to Taguchi’s L₉ (3${}^3)$ orthogonal array the machining was performed to investigate the ideal machining parameters with a range of pulse current (I${}_p)$ 6–8 amps, pulse-on time ($T_{\mathrm{\text{on}}}$) 5–15$\mu$s and pulse-off time ($T_{\mathrm{\text{off}}})$ 10–30$\mu$s, respectively. Analysis of variance (ANOVA) result confirms that $I_p$ (45.86%) has the most influencing parameter affecting the MRR and SR, followed by $T_{\mathrm{\text{on}}}$ (25.10%) and $T_{\mathrm{\text{off}}}$ (17.19%), respectively. Furthermore, Technique for Order Preference by Similar Ideal Solution (TOPSIS) and desirability approach was employed to find the optimal parameter combinations to attain the best combined output responses.