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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.

Although Ti–6Al–4V stands out as one of the best in biomedical, automotive, and aerospace applications due to its low density and higher corrosion resistance, the toxicity associated with Al and V elements is shifting the usage of Ti-based alloys with reduced toxic content but with more biocompatible elements (Zr, Ta, and Nb). Despite the advancements in the era of micromachining, machining these alloys with traditional machining methods is highly tedious. The present research evaluates the micromachining performance of the TNTZ (Ti–29Nb–13Ta–4.6Zr) alloy employing a tungsten carbide electrode using the µ-EDM (micro-electro-discharge-machining). The primary input parameters examined are voltage (80–130 V) and capacitance (10–400nF), with a feed rate of 0.09 mm/s during the experiments. The output responses assessed include VMR, OC, CErr, and SFR. Meanwhile, due to the complexity of the µ-EDM process, it presents significant challenges in predicting performance across different machining settings. The interactions between key process parameters, such as C and V, amplify their parametric sensitivity, making conventional simulation approaches inadequate for accurately modeling these interdependencies. To address these challenges, the latter part of this study explores machine learning techniques, particularly Multiple linear regressor (MLR), decision tree regressor (DTR), and artificial neural network (ANN) for predictive accuracy. The models are evaluated using two key performance metrics: normalized root mean squared error (NRMSE) and R-squared (R 2 ). The ANN demonstrated superior capability in handling experimental variability based on the prediction results. It has the highest R 2 of 0.99, the lowest NRMSE of 0.0245, and the percentage of prediction error is less than 5%.

Ti-6Al-4V is considered a challenging material in terms of accurate machining. Therefore, electric discharge machining (EDM) is commonly engaged, but its low cutting rate depreciates its use. This issue is resolved if graphene nanoparticles are mixed in the dielectric. However, the control over the sparking phenomenon reduces because of the dispersion of graphene particles. Subsequently, the machined profile’s geometric accuracy is compromised. Furthermore, the presence of nanographene induces different sparks along axial and radial cutting orientations. This aspect has not been comprehensively examined yet and dedicatedly targeted in this study to improve the quality of EDM process for Ti-6Al-4V. A total of 18 experiments were conducted under Taguchi’s L18 design considering six parameters namely, electrode type, polarity, flushing time, spark voltage, pulse time ratio, and discharge current. The aluminum electrode proved to be the best choice to reduce the errors in both the cutting orientations. Despite the other parametric settings, negative tool polarity yields lower values of axial (ADE) and radial errors (RDE). The developed optimal settings ensure 4.4- and 6.3-times reduction in RDE and ADE, respectively. In comparison to kerosene, graphene-based dielectric yields 10.2% and 19.4% reduction in RDE and ADE, respectively.

Several components are made from Al-Mg-based composites. MoS2 is used to increase the composite’s machinability. Different weight percent (3, 4, and 5) of MoS2 are added as reinforcement to explore the machinability properties of Al-Mg-reinforced composites. The wire cut electrical discharge machining (WEDM) process is used to study the machinability characteristics of the fabricated Al-Mg-MoS2 composite. The machined surface’s roughness and overcut under different process conditions are discussed. The evaluation-based distance from average solution (EDAS) method is used to identify the optimal setting to get the desired surface roughness and overcut. The following WEDM process parameters are taken to determine the impact of peak current, pulse on time, and gap voltage on surface roughness, and overcut. The WEDM tests were carried out on three different reinforced samples to determine the impact of reinforcement on surface roughness and overcut. The surface roughness and overcut increase as the reinforcement level increases, but the optimal parameters for all three composites are the same. According to EDAS analysis, I3, Ton2, and V1 are the best conditions. Furthermore, peak current and pulse on-time significantly influence surface roughness and overcut.

The need for precise manufacturing in aerospace, medical, and automotive industries requires an investigation of upscale drilling methods that can achieve small-diameter deep holes with exceptional accuracy. Abrasive Waterjet Drilling (AWJD) has developed as a promising technology due to its distinctive blend of precision and adaptability. Despite several advantages, overcutting is the fundamental obstacle restricting the widespread use of AWJD. The novelty of this research is to investigate the impact of process parameters, specifically water pressure, standoff distance, and abrasive mass flow rate, on the top, bottom, and depth-averaged radial overcut developed during the deep hole drilling of stainless steel 316L material. The deep hole drilling experiments have been conducted utilizing Taguchi’s (L 16 ) orthogonal array by adjusting the drilling settings. The statistical significance of specific drilling parameters and second-order quadratic models for the responses have been established by analysis of variance. Additionally, to mitigate the impact of overcut and improve the drilling quality necessary for diverse sectors such as automotive, biomedical, and oil and gas, a metaheuristic optimization method, specifically the Grasshopper Optimization Algorithm (GHO), has been utilized. Thereafter, the effectiveness of the suggested algorithm has been validated using quality measures, namely hyper-volume and spacing by comparing it to the approaches of whale optimization, harmony search, and multiverse optimization algorithms. The comparison shows that the GHO algorithm outperformed the others. The GHO algorithm identified the optimal process parameters for AWJD as water pressure 305.36 MPa, standoff distance 1.00 mm, and mass flow rate 600 g/min. The anticipated values for the top, bottom, and depth-averaged radial overcut, according to the optimal parameters, are 1.19 mm, 0.64 mm, and 1.53 mm, respectively. Furthermore, a validation test has been conducted to verify the efficacy of the GHO algorithm. The validation test showed top, bottom, and depth-averaged radial overcut values of 1.17 mm, 0.66 mm, and 1.49 mm, with percentage deviations of 1.71%, 3.03%, and 2.68%, respectively, with the GHO algorithm. The surface quality of the drilled holes has been examined through a Scanning Electron Microscope (SEM). The SEM images have been obtained at magnifications of 12X and 500X of the drilled hole surface using optimum parameters, demonstrating smooth and uniform surfaces at the top, middle, and bottom of the drilled hole.

Silicon carbide particle reinforced aluminum matrix composites (SiC p /Al), combine the properties of aluminum matrix materials and reinforced silicon carbide (SiC) particles, finding widespread application in aerospace, optics, and electronics. The increased hardness and electrochemical inertness of SiC particles present significant challenges in their processing. This research investigates the impact of photocatalytic processes on machining accuracy in electrochemical processing (ECM). The dissolution characteristics of SiC p /Al during ECM and photocatalytic-assisted electrochemical machining (PAECM) were analyzed with varying electrolyte compositions, using polarization curves and oxidation–reduction potential (ORP). Validation of improved machining accuracy and surface quality in PAECM was achieved through no-feeding machining. According to theoretical principles and laboratory analyses of the workpiece surface, it was found that PAECM, through its photocatalytic reaction, generated more oxides, blocked stray currents and stray corrosion effectively, and attained accuracy in profile creation. Through a comparative analysis it revealed that PAECM exhibited smoother profiles and reduced Overcut Ratio (OCR) in hole and groove machining compared to ECM, particularly in groove machining. Adjusting machining gaps as well as reducing abrasive particle size in PAECM influenced groove depth and width, which achieved optimal morphology with a 0.4 mm gap and a 2–3 nm abrasive particle size, resulting in a 370 μm depth.

Regardless of the materials’ intrinsic characteristics, electrochemical discharge drilling (ECDD) effectively micro-machines various materials. The present article optimizes the ultrasonic assisted rotary ECDD (UR-ECDD) process for maximizing the material removal rate (MRR), while minimizing the hole overcut (HOC) and circularity error (CE). The micro-holes are produced using a Taguchi’s L16 array and multi-criteria optimization is carried out using grey relational based analysis (GRA). MRR, HOC and CE serve as a response parameter while tool vibration, tool feed rate, working material rotation, applied voltage and electrolyte concentration are control variables. UR-ECDD results in the improvement of 14.8% in MRR, 15.4% in HOC and 17.2% in CE when compared to the ECDD process. The optimized control variables based on GRA are derived as A4C3B4D1E4 (6 µm, 80 rpm, 0.9 mm/min, 35 V, 25 wt%). Tool vibration emerged as the most significant control variable. The GRG’s predicted results at optimum conditions provide a satisfactory alignment with the experimental results. Machine learning-based algorithms are also used to predict the responses using Random Forest and Gradient Boost approaches. Comparative results indicated that the Random Forest predicts the responses with reduced error in comparison to the Gradient Boost method. The validation of the dataset exhibits a similar trend confirming the efficacy of prediction.

The challenging characteristics of Inconel 617 (IN617), such as its modest modulus of elasticity, heightened chemical reactivity, and low thermal conductivity, pose difficulties in employing conventional machining methods for this material. This complexity is further amplified when considering the specific requirements for applications in aerospace. Consequently, electric discharge machining (EDM) emerges as a preferred approach for working with this alloy. However, inherent challenges within EDM, specifically electrode wear rate (EWR) and dimensional overcuts, limit its efficacy. To address these issues, a comprehensive exploration of the potential of powder-based additives in waste cooking oil (WCO) against cryogenically treated brass electrode material has been undertaken. The investigation holds pivotal importance because the careful choice of an optimal dielectric plays a significant role in affecting the heat input to the electrode, thereby influencing the melting/vaporization and tool wear of the electrode. It is essential to highlight that these considerations have not been adequately addressed in the current body of literature. The experimentation employs the Response Surface Methodology (RSM) experimental design. The findings indicate that the shallow cryogenically treated (SCT) brass electrode exhibited exceptional performance, yielding the lowest values for electrode wear rate (EWR) at 1.49 mg/min and dimensional overcuts (OC) at 0.01 mm. These results are notably superior, surpassing the least values obtained with the deep cryogenically treated (DCT) brass electrode, showcasing a 202.68% improvement in EWR and a 2.33% improvement in OC.

Geometrical inaccuracy is one of the electric discharge machining (EDM) errors and is mainly influenced by the dielectric being used during the operation. The non-conventional machining processes are opted over traditional machining operations due to the greater strength of Ni-based superalloy, specially, Inconel 617 (IN617). But still, there is a need to upgrade the dielectric fluid to minimize the overcut. Therefore, this study uses a combination of surfactant-added dielectrics of transformer oil and cryogenically treated (CT) electrodes (copper and brass). One of the exceptional benefits of applying the cryogenic treatment on the electrodes is that it refines the grain size of the electrode which helped in the regular sparking and reduced the dimensional inaccuracy throughout the EDM operation. A set of 20 experiments under the full factorial design technique was implemented. The experimental results have been explained with process physics, and along with simulation to explore the mechanism of machining with mathematical expressions. The machining capabilities of CT electrodes provided greater dimensional accuracy by an average of 17.1% compared to non-CT electrodes. CT brass provided the minimum dimensional inaccuracy (0.023 mm) in T-20. However, the non-CT copper electrode showed the lowest overcut (0.134 mm) obtained transformer oil without the addition of surfactant. The modeling was also performed to measure the crater sizes produced during the machining of Ni-based superalloy. The simulation results revealed that the mean absolute divergence between the current simulation findings and the ones provided in the literature was approximately equal to 5–7%.