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This paper deals with the development and comparison of prediction models established using response surface methodology (RSM) and artificial neural network (ANN) for a wire electrical discharge machining (WEDM) process. The WEDM experiments were designed using central composite design (CCD) for machining of Inconel 718 superalloy. During experimentation, the pulse-on-time (TON), pulse-off-time (TOFF), servo-voltage (SV), peak current (IP), and wire tension (WT) were chosen as control factors, whereas, the kerf width (Kf), surface roughness (Ra), and materials removal rate (MRR) were selected as performance attributes. The analysis of variance tests was performed to identify the control factors that significantly affect the performance attributes. The double hidden layer ANN model was developed using a back-propagation ANN algorithm, trained by the experimental results. The prediction accuracy of the established ANN model was found to be superior to the RSM model. Finally, the Non-Dominated Sorting Genetic Algorithm-II (NSGA- II) was implemented to determine the optimum WEDM conditions from multiple objectives.

Laser beam machining is a complex thermal process of material removal. In this process, thermal energy is used to vaporize and remove the material from a particular area. Hence a comprehensive study is needed to completely understand the process which can be achieved by a simulation model and its validation through experimentation. In this paper this was achieved with an experimental study to see the relation between the Laser power and the material removal rate (MRR) of SS 316 L, Inconel 718 and Ti6Al4V during the process of Laser Machining. Also a 3D transient model of a moving Gaussian heat source was developed in ANSYS 18.2 to predict the MRR for different values of laser power. Mesh sensitivity analysis was done prior to the usage of the model so as to choose the perfect mesh size which can give the best results possible. After validation of the model, a close correlation was found between the experimental data and the simulation results. In the simulation model it was also observed that the temperature is maximum at the point of contact of the laser and is comparatively very high for a fraction of second.

Industries demand stringent requirements towards economical machining without hindering the surface quality while cutting high carbon high chromium (HcHcr) steel. Electrical discharge machining (EDM) of HcHcr steel aims at reducing machining cost (i.e., maximize material removal rate (MRR) and minimize tool wear rate (TWR)) with good surface quality (i.e., minimize surface roughness (SR)). A comparative study was carried out on EDM of HcHcr D2 steel (DIN EN ISO 4957) by applying Taguchi L18 experimental design considering different electrode materials (copper, graphite, and brass), dielectric fluids (distilled water and kerosene), peak current, and pulse-on-time. The process performances were analyzed with respect to material removal rate, surface roughness, and tool wear rate. Pareto analysis of variance was employed to estimate the significance of the process variables and their optimal levels for achieving lower SR and TWR and higher MRR. Hybrid Taguchi-CRITIC-Utility and Taguchi-PCA-Utility methods were implemented to determine the optimal EDM parameters. Higher MRR of 0.0632 g/min and lower SR of 1.68 µm and TWR of 0.012 g/min was attained by graphite electrode in presence of distilled water as dielectric fluid compared to the brass and copper. Additionally, a metallographic analysis was carried out to study the surface integrity on the machined surfaces. Micrographic analysis of the optimal conditions showed lower surface roughness and fewer imperfections (lesser impression, waviness surface, and micro-cracks) compared to worst conditions.

Purpose This article targeted to experimentally examine the impact of several considered process parameters namely, applied voltage (AV), tool feed rate, electrolyte concentration and pulse frequency (PF), on the material removal rate (MRR) and radial overcut (ROC) while performing shaped tube drilling of aviation grade Inconel 625 super alloy through electrochemical machining principle. Further, an attempt has also been made to develop mathematical models for the process responses along with advanced optimization with evolutionary methods. Design/methodology/approach The central composite rotatable design matrix was used to scheme out the experiments in the present study. The consistency and accuracy of the developed mathematical models were confirmed through statistical results. Additionally, a field emission scanning electron microscope analysis was conducted to assess and analyze the microstructure of the machined work samples. The study also seeks to optimize the selected process inputs for MRR and ROC through the implementation of the desirability method, particle swarm optimization (PSO) and Teaching Learning-Based Optimization (TLBO). Findings The ROC is significantly influenced by the input parameters, specifically the PF and AV. Less ROC values were observed when the high PF with moderate AV. The minimum and maximum values of ROC and MRR were obtained as; 135.128 µm and 380.720 µm; 1.37 mg/min and 2.3707 mg/min, correspondingly. The best optimized confirmatory results were obtained through the TLBO approach, with an MRR value of 3.1587 mg/min and a ROC of 71.9629 µm, in comparison to the PSO and desirability approaches. Originality/value The various challenges associated with the productive machining of aviation grade Inconel 625 superalloy have been explored experimentally. The conducted experimentation has been performed on the in-house fabricated micro-electrochemical setup capable of performing a variety of advanced machining operations at the miniaturized level. Further, the application of shaped tube drilling while processing aviation grade Inconel 625 superalloy has been explored with the developed micro-ECM set-up. Moreover, the performed microstructure analysis of the machined work samples has elaborated and explored the various associated surface integrity aspects which are quite crucial when it comes to real-life aerospace-related applications. The utility of designed experiments has further made the attempted experimental analysis more fruitful and qualitative too.

Investigation on tritium ([.sup.3]H) washout process in a tropical region at Kakrapar Atomic Power Station (KAPS), Gujarat, India was carried out. [.sup.3]H concentration in air as well as that in rainwater is estimated near KAPS Site having Pressurized Heavy Water Reactors (PHWRs) operational. Samples were collected covering the four rainy seasons from 2016 to 2019. The corresponding meteorological parameters of relative humidity, ambient temperature, wind speed, wind direction, and atmospheric stability were measured. The rain spectral characteristics such as raindrop diameter, fall velocity, liquid water content (LWC) in raindrops and average rain rate are also studied. Site-specific wet deposition rate (Bq [m.sup.-2] [s.sup.-1]) and washout coefficient for [.sup.3]H ([s.sup.-1]) is observed to be in the range of 1.4E-05 to 4.8E-01 (Geometric Mean = 5.3E-03) and 1.1E-07to 3.6E-02 (Geometric Mean = 4.1E-05) respectively. Significant and positive correlation was observed between average rain rate and washout coefficient ([R.sup.2] = 0.73). Significant and negative correlation was observed between raindrop diameter of different rain events and [.sup.3]H activity in rainwater ([R.sup.2] = 0.70).

Single-crystal SiC is a typical third-generation semiconductor power-device material because of its excellent electronic and thermal properties. An ultrasmooth surface with atomic surface roughness that is scratch free and subsurface damage (SSD) free is indispensable before its application. As the last process to reduce the surface roughness and remove surface defects, precision polishing of single-crystal SiC is essential. In this paper, precision polishing technologies for 4H-SiC and 6H-SiC, which are the most commonly used polytypes of single-crystal SiC, such as chemical mechanical polishing (CMP), photocatalytic chemical mechanical polishing (PCMP), plasma-assisted polishing (PAP), electrochemical mechanical polishing (ECMP), and catalyst-referred etching (CARE), were reviewed and compared with emphasis on the experimental setup, polishing mechanism, material removal rate (MRR), and surface roughness. An atomically smooth surface without SSD can be obtained by CMP, PCMP, PAP, and CARE for single-crystal SiC. However, their MRRs are meager, and the waste treatment after CMP is difficult and expensive. Moreover, PAP’s operation is poor due to the complex polishing system, plasma generation, and irradiation devices. A high MRR can be achieved by ECMP. In addition, it is an environmentally friendly precision polishing process for single-crystal SiC since the neutral salt solution is generally used as the electrolyte in ECMP. However, the formation of the egglike protrusions at the oxide/SiC interface during anodic oxidation would lead to a bigger surface roughness after ECMP than that after PAP is processed. The HF solution used in CARE was toxic, and Pt was particularly expensive. Ultrasonic vibration-assisted single-crystal SiC polishing and electrolyte plasma polishing (EPP) were discussed; furthermore, the research direction of further improving the surface quality and MRR of single-crystal SiC was prospected.

In this paper, tungsten carbide powder adding the dielectric liquid during electro-discharge machining (EDM) process for processing SKD61 steel was explored. Firstly, the influence of main process variables, comprising peak current ( I p ), pulse on time ( T on ), and amount of powder ( A p ) on material removal rate (MRR), tool wear rate (TWR), and surface roughness (R a ) was explored. Secondly, an optimal combination of these process variables is sought to enhance the quality of surfaces, MRR, and reduce TWR. A series of 15 experiments of the Box-Behnken design was performed. Subsequently, adequate mathematical models for MRR, TWR, and R a were established, with the application of analysis of variance (ANOVA) to evaluate the adequacy of these models. Finally, Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) and desirability approach (DA) were adopted for the multi-attribute optimization. Besides, Non-Dominated Sorting Genetic Algorithm II (NSGA II)-evaluation by an area-based method of ranking (EAMR) was also conducted and compared with both DA and TOPSIS for the most appropriate choice. The outcomes indicated that I p demonstrates the strongest influence on R a , MRR, and TWR, followed by T on and A p for MRR, while the proceeding effect is A p and T on for TWR and R a . In comparison with TOPSIS, DA provides the best solution with a decline of 41.5% in TWR and an increment of 22.7% in MRR, while TOPSIS contributes the best solution with a drop of 13.89% in R a when compared with DA. In addition, TOPSIS provides better surface quality than DA.