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Polymer nanocomposite is commonly used to develop structural components of space, aircraft, biomedical, sensor, automobile, and battery sector applications. It remarkably substitutes the heavyweight metallic and nonmetallic engineering materials. The machining principles of polymer nanocomposites are intensely different and complex from traditional metals and alloys. The nonhomogeneity, abrasive, and anisotropic nature differs its machining aspect from conventional metallic materials. This investigation aims to execute the CNC drilling of modified nanocomposite using Graphene–carbon (G-C) @ epoxy matrix. The process constraints, namely, cutting speed (S), feed (F), and wt.% of graphene oxide (GO) vary up to three levels and are designed according to the response surface methodology (RSM) array. The nonlinear model is created to predict surface roughness (Ra) and delamination (Fd) on regression analysis. It has been found that the average error for Ra is 0.94% and for Fd it is 3.27%, which is acceptable in model predictions. The metaheuristics-based evolutionary Dragonfly algorithm (DA) evaluated the optimal parametric condition. The optimal setting prediction for the DA is observed as cutting speed (S)-37.68m/min, feed (F)-80mm/min, and wt.% of graphene oxide (GO)-1%. This algorithm demonstrates a higher application potential than the previous efforts in controlling Ra and Fd values. Both the drilling response values are found to be minimized when the cutting speed increases and the feed decreases. The best fitness value for the DA is 1.626 for surface roughness and 5.086 for delamination. This study agreed with the prediction model’s outcomes and the process parameters’ optimal condition. The defects generated during the sample drilling, such as fiber pull out, uncut/burr, and fiber breakage, were examined using FE-SEM analysis. The optimal findings of the DA module significantly controlled the damages during machining.

Drilling is a famous and widely used machining operation to make holes in the workpiece. The size and surface quality of drilled hole are two factors that should be considered mainly. In this research, we examine the effect of different machining parameters and conditions on the surface quality of generated hole in drilling operation. For this purpose, we employ fractal theory and investigate how the variations of depth of cut and spindle speed affect the complexity of surface texture of drilled holes in wet and dry machining conditions. Based on the obtained results, the increment of depth of cut and spindle speed in case of wet and dry machining causes lower complexity on the generated surface from drilling. In addition, the generated surface from dry machining is more complex than the generated surface from wet machining. The obtained method in this research can be applied to other machining operations in order to investigate the effect of machining parameters and conditions on the surface quality of machined workpiece.

The usages of carbon-fiber reinforced polymer (CFRP) in aerospace, defense, and structural fields are increasing due to their excellent properties. However, the materials design, forming of material, machine tool and processing conditions are major tasks in manufacturing industries. Particularly, the micro feature making on macro-components using vertical machining center is a challenge nowadays. In this work, two different drill bits, such as high-speed steel (HSS) and solid carbide (SC) micro-drill, were used to make drilling on CFRP material. The performance of drills was evaluated by obtaining minimum delamination and stress in drilling by varying cutting velocity (CV), feed rate (FR), and air pressure (AP). Regression equations were formed according to the measured quality performance characteristics. The linear weighted method-based combined objective function algorithm and Genetic Algorithm was followed to multi-objective optimization. Besides, the most influencing factors were also identified and discussed using analysis of variance. The results explained that the SC micro-drill performance was better than HSS micro-drill. Also, the CV has the most eminent parameters followed by FR.

This article describes new control criteria and robust optimization methodology to balance drilling parameters and machining characteristics. Experimentation was performed according to response surface methodology (RSM) using a TiAlN coated SiC tool. The full drilling force signal and cutting parameters tested are categorized into five stages, indicating the drilling tool-workpiece interactions’ different statuses. Principal component analysis (PCA) assigns real response priority weight during the aggregation of conflicting characteristics. The hybrid module of combined compromise solution and PCA (CoCoSo–PCA) is used to decide the optimal parametric setting. It efficiently undertakes a trade-off between minimal thrust ($Th=30.02$N), torque ($T=0.05$Nm) surface roughness ($R_a=1.55\mu$m). A regression model between input parameters and output function was established using RSM quadratic model. The validation experiment shows significant improvement, and the proposed module can be recommended for quality-productivity characteristics control.

Drilling process plays a vital role in the development of polymer clay nanocomposites. The effects of various parameters, such as tool, feed rate and speed to generate the impact on tangential force, thrust force and delamination factor acting on the material in production are studied during drilling operations in the aerospace and automotive industries. The input variable settings to adjust the speed and feed rate to show the outcomes of tangential force, thrust force and delamination factor of the material according to the consumption of the parts in the next stage of manufacturing are calculated by efficient feed rate optimization. A series of tests are performed by changing tools such as High speed steel (HSS), end mill (HEM), High speed steel (HSS), twist drill (HTD) and carbide twist drill (CTD) on various materials to evaluate the influence on the feed rate and speed. Based on the experimental analysis, mathematical modeling is implemented to study the properties of glass fiber reinforced polyester nanocomposite (GFRPNC; 3wt.%). Using the desirability approach, the optimum operating conditions of the selected process variables are considered to minimize delamination. A full factorial experiment design is adopted using the two fundamental concepts of replication and randomization of experimental design to research the relationship between the variables. Based on the analysis of variance (ANOVA), the process model is formulated using the ${\text{Minitab}}^{\text{®}}$ statistical kit. It is inferred that the delamination factor is minimal for all tools (CTD, HTD and HEM) at 0.1mm/rev feed rate and speed at 852rpm.

Carbon nanomaterial (CNM)-reinforced polymer composite is broadly employed in emergent industrial needs due to advanced mechanical properties. In this research paper, a comparatively innovative integrated approach (SOA–CoCoSo) is proposed by using Principal Component Analysis (PCA)-based Combined Compromise Solution (CoCoSo) and Seagull Optimization Algorithm (SOA). This modified module is used in the drilling operation of zero-dimensional (0D) carbon nano-onion (CNO)-reinforced polymer (epoxy) composite. The desired machining performances, namely, surface roughness ($R_a$), thrust force (Th), and Torque (Tr), are optimized to improve the quality and productivity concerns. The control of process constraints, i.e. the wt.% of nanomaterial ($A$), spindle speed ($B$), and feed rate ($C$), was performed to achieve the desired objective value. The drilling experimentation was executed at three different levels of Box–Behnken Design (BBD). The objective function of PCA–CoCoSo was fed as input into the SOA. To acquire a better work efficiency, higher spindle speed, lower feed rate, and incremental wt.% of nanomaterial reinforcement are considered. The results demonstrated that the wt.% of CNO reinforcement and feed rate are the most influential factors for optimal machining performance results. The optimal constraints condition from the SOA–CoCoSo hybrid module is found at a combination of lower level of CNO wt.% (0.5wt.%) and feed rate (61mm/min) and high value of spindle speed (1500rpm). Also, the hybrid SOA–CoCoSo module shows a lesser amount of error percentage than the usual PCA–CoCoSo. The experiments were performed to confirm the feasibility of the suggested hybrid module for optimizing the varying machining parameters. The results indicated that the hybrid method is more efficient than the conventional method.

Fiber metal laminates (FML) are used in outer covering of the fuselage skin structure. The thrust force and the torque generated during drilling process affect the quality of the holes on the structure. The magnitude of cutting forces is controlled by optimizing the drilling process parameters. In this study, the influence of drilling parameters such as spindle speed, feed rate and the weight percentage of layered double hydroxides (LDH) in the binder epoxy on the thrust force and torque during drilling operation was studied. The experiments were designed based on Taguchi’s L₉ orthogonal array. The Gray Relational Analysis was used as multi-objective optimization tool for finding the optimal combination of process parameters. The spindle speed was identified as the most influencing process parameter to affect the drill quality in the FMLs. SEM images taken on the drilled specimens for the best and worst input parameter settings were compared and discussed. The regression models were generated to predict the output response values within the range of actual experiments.

Drilling, which constitutes one third of the machining operations, is widely used in many areas of the manufacturing industry. Various difficulties are encountered in the drilling process since the chip is formed in a closed limited chip flows. These difficulties directly affect the output parameters such as energy consumption, surface quality, and cutting force. Therefore, it is necessary to determine the ideal processing parameters to achieve the best performance. However, experimental research on machining processes requires both a long time and a high cost. For these reasons, machining outputs can be estimated by conducting drilling simulations with the finite element method. In this study, the finite element method is used in order to investigate the influence of different cutting parameters and different helix angles on the power and thrust force of Ti–6Al–4V (grade 5) alloy that is commonly used in the aviation industry. The study selected three different cutting speeds, feed rates, and helix angles as the cutting parameters. The experimental design was made according to the response surface method (RSM) Box–Behnken design in the Design-Expert program. Drilling simulations were performed using the ThirdWave AdvantEdge^TM software. The lowest thrust force measured is 1241.39 N at 40° helix angle, 2000-rpm revolution rate, and 0.05-mm/rev feed rate, while the lowest power consumed is 765.025 W at 30° helix angle, 1500-rpm revolution rate, and 0.05-mm/rev feed rate. As a result, it was determined that the most effective parameter for power and thrust force was the feed rate.

The drilling of glass fiber-reinforced plastic (GFRP) composites gained importance since they are used as structural components in many industries such as automotive, aerospace, and aviation. A large number of holes are needed in the industry to join these composite parts. However, some failures occur in drilling GFRP composites, such as delamination, matrix cracking, and fiber breakage. These failures not only reduce the strength of the composite, but also reduce its service life. Drilling parameters, drill bits, and woven types have a great influence on the occurrence of these failures by greatly influencing the thrust force, surface quality, and cutting temperature. In this study, the effects of drilling parameters and woven types of GFRP composites on thrust force, surface roughness, delamination factor, and cutting temperature were examined in the drilling of uni-directional (UD), $\pm 45^{\circ }$ and 0${}^{\circ }∕90^{\circ }$ GFRP woven composites. The effects of drilling parameters and the delamination factor on the tensile strength of the drilled specimen were also investigated. The result of this study indicated that thrust force, delamination factor, and surface roughness increased with increasing cutting speed and feed rate. An increase in feed rate decreased the cutting temperature, while an increase in cutting speed increased the cutting temperature. Also, it was found that the delamination factor had a critical influence on the tensile strength of the GFRP composites.

In the present scenario, electrochemical arc machining (ECAM) (hybrid of electric discharge erosion and electrochemical dissolution) is an evolving procedure for difficulty in machining the materials due to constraints of existing processes. This research aims to investigate the machinability of Ni${}_{55.7}$Ti alloy through electrochemical arc drilling using molybdenum electrode. Electrolyte concentration (ethanol with ethylene glycol and sodium chloride), supply voltage, and tool rotation are considered as the variable factors to evaluate the ECAM performance characteristics in drilling blind hole operation concerning overcut (OC), tool wear rate (TWR) and materials removal rate (MRR). Consequently, response surface methodology is implemented for predictive modeling of various performance characteristics. Finally, multi-objective optimization through desirability function approach (DFA) has produced a set of optimal parameters to improve the productivity along with the accuracy, which is the prime requirement for the industrial applicability of the ECAM process. Results demonstrated that supply voltage is the influential key factor for improvement of machining rate. Scanning electron microscope (SEM) photographs revealed the development of heat affected zone (HAZ), white layer, melted droplet, craters, re-solidified material, ridge-rich surface and voids as well as cavities around the end-boundary surfaces of a blind hole. Composition analysis through energy dispersive spectroscopy (EDS) indicated the oxygen content on the machined surface because electrolyte breakdown causes oxidation to take place at elevated temperatures across the machining zone. Moreover, carbide precipitation like TiC was found in the melting zone of the drilled hole, as revealed by X-ray diffraction (XRD) analyses, which has the affinity to reduce the SMA properties in HAZ.

In manufacturing industries, polymers are widely used due to their exceptional physiochemical and mechanical characteristics. It consists of high strength, low weight, corrosive resistivity, and ease of fabrication. Glass fiber is more cost-effective and easily available than other fibers such as carbon, aramid and kevlar. The most challenging issue for the manufacturer in the laminated polymer is the non-homogeneity and anisotropic behavior. This nature also hinders the machining performance of laminated polymer composites, which are entirely different from metals and their alloys. The supplements of nanomaterials enhanced the physiomechanical properties and the machining efficiency of fiber laminates. This work highlights the machining (drilling) aspect of glass fiber-reinforced polymer composites modified by multiwall carbon nanotube (MWCNT). The effect of drilling factors such as spindle speed ($S$), feed rate ($F$), and MWCNT weight percent (wt.%) on machining responses such as Thrust force (Th), Torque ($T$), and Surface roughness (SR) has been investigated. The drilling operations were conducted using the 5 mm diameter TiAN (Sic coated) according to the response surface methodology (RSM) design. The process constraints were controlled by the hybrid module of additive ratio assessment (ARAS) and the Artificial Bee Colony (ABC) algorithm. The nature-inspired principles of the bee are used to optimize the objective function. The multiple responses were aggregated using the ARAS method, and its objective function is fed into the ABC algorithm. It was remarked that the hybrid ARAS-ABC is more capable than the traditional ARAS, with an overall improvement of 7.33% in assessment values. The scanning electron microscopy (SEM) test confirms the feasibility of the proposed hybrid (ARAS-ABC) module to achieve a favorable machining environment while drilling modified nanocomposites.

The paper focuses on the cutting behavior of Glass Fiber Reinforced Polymer (GFRP) composites and GNP–GFRP composites that contain varying amounts of Graphene Nano Platelets (GNP). GFRP composites are increasingly being used in a variety of industrial applications due to their excellent mechanical properties, such as high strength, stiffness, and low weight. However, their machining and cutting behavior can be challenging due to the presence of the reinforcing fibers. Therefore, the study aims to investigate the machining behavior of GFRP composites and the effect of adding GNP on their cutting behavior. The effect of different parameters such as cutting speed, feed rate and reinforcement rate on cutting forces and delamination factor is investigated. In addition, the tensile strength and fatigue behavior of the composite materials with the best and worst delamination factors were also determined. Addition of up to 0.2 wt.% of GNP to GFRP composites resulted in an increase in cutting forces and delamination factor when drilling GFRP composites. While the cutting force and delamination factor decreased with the increase in cutting speed, the cutting force and delamination factor increased with the increase in the feed rate. Analysis of variance (ANOVA) was performed to determine the effects of drilling parameters and reinforcement ratio on cutting force and delamination factor according to full factorial experimental design. The most efficient factor on the cutting forces is found to be feed rate (84.97%), followed by the reinforced rate (6.48%) and cutting speed (6.13%). The most efficient factor on the delamination factor is determined to be feed rate for (44.49%), followed by the reinforced rate (29.20%) and cutting speed (21.73%).

The buckling behaviors of drill string may cause serious down-hole problems, such as high friction during drilling, loss of weight on bit (WOB), and even drill string failure. This paper analyzes the dynamic buckling characteristics of drill string, considering the friction between drill string and wellbore. In the theoretical research, the key parameter expressions are determined, especially the contact behaviors of drill string and mathematical models are proposed, which include axial displacement, angular displacement and contact force. The numerical calculation results show that the influence of friction on the drill string buckling is mainly reflected in angular displacement. The influences of friction effect on the dynamic characteristics of drill string become greater with the increase of inclination angle, while become lower with the increase of drill string length. The friction effect appears to have insignificant influence on drill string condition with the accumulation of time. The comparison results indicate that the friction affects the action of axial load on drill string and delays the onset of buckling. On the other hand, the angular displacements decrease with drill string length increasing, while the axial displacements have an opposite effect. The research results provide theoretical references for studying dynamic buckling characteristics; furthermore, we can determine the methods and solutions to prevent drill string buckling based on this, and improve the safety of downhole drilling.

The paper discusses the study on thrust force and torque while drilling GFRP composites with SiC fillers. The input parameters such as cutting speed, feed rate and point angle were varied and influencing parameters such as thrust force and torque were studied. The experimental investigation was made during the drilling of GFRP with SiC fillers using four standard twist drills of point angles 90°, 100°, 110° and 120°.

Titanium alloys find increased applications in many engineering fields such as bio-medical, aerospace, electronics and automobile industries due to their superior physical properties such as high specific strength, weight to strength ratio, corrosive resistant and toughness. Low thermal conductivity of titanium alloys makes it difficult to machine since high cutting temperature is generated at tool work piece interface. As it is highly chemically reactive at high temperature, work piece is adhered to the tool surface. The present work aims at investigating the effect of drilling process parameters viz. spindle speed, drill bit diameter and feed rate on machining performance measures such as burr height at entry and exit, surface roughness and circularity at entry and exit. In order to optimize all the performance measures simultaneously, the present investigation utilizes a hybrid approach known as superiority and inferiority ranking method combined with technique for order preference by similarity to ideal solution (SIR-TOPSIS) to convert these characteristics into single equivalent characteristic ($r$-flow). A statistically valid empirical relationship is developed for r-flow in terms of machining parameters using nonlinear regression technique. The study aims at attaining optimal parametric setting that maximizes $r$-flow. To optimize $r$-flow, an improved version of meta-heuristic evolutionary algorithm known as harmony search (HS) algorithm is implemented. The confirmatory experiment suggests the robustness of the methodology in optimizing the drilling operation.

Magnetic field-assisted electrochemical spark drilling (MA-ECSD) is a cost-effective triplex hybrid machining technique that has been developed to enhance the machining depth and surface roughness of insulated and hard-to-scribe materials. The study presented adopts a reformed approach for creation of dynamic magnetic field during drilling of Sodalime glass where a 34 AWG copper wire coiled electromagnet has been installed in the in-house designed and fabricated setup of MA-ECSD. The experimental plan is based on Box–Behnken design (BBD) of Response Surface Methodology (RSM) and significance of parameters is determined using ANOVA. Multi-objective optimization (MOO) is performed by applying Grey Relational Analysis (GRA). A noncontact optical profilometer measures the machining depths and surface roughness of drilled holes. The installed electromagnet generated dynamic magnetic field intensity (MFI) ranging between 0.00 and 0.18 Tesla. Preliminary experiments were conducted to select and set the range of input parameters. Significant effect of voltage, NaOH concentration and MFI on machining depth and surface roughness is found and optimal parameter settings obtained are 24V, 30wt% and 0.09 Tesla. Machining depth increased by about 13.03% with rise in voltage-NaOH concentration and surface roughness improved by 25.3% with elevation in voltage-MFI. Dynamic MFI generated from electromagnet helped in smooth motion of electrolyte in the fine space amidst cathode and glass slide due to magnetohydrodynamic effect (MHD) which resulted in enhanced machining depth and surface roughness. The experimental and predicted results obtained after confirmatory test are appreciable which is evident from SEM images and images obtained from Optical profilometer.

In computerized tool path programming, the operator/user can generate the tool path based on the shape and geometry of the part to be produced by choosing from a set of predefined strategies available in the library of Computer Aided Manufacturing (CAM) software. These tool paths are typically not optimum, specifically for complex geometries. This paper employed Travelling Salesman Problem (TSP) as a foundation to propose a new tool path optimization algorithm for drilling to minimize the tool path length and subsequently reduce the time spent on nonproductive movements. The proposed algorithm was solved using local search approach in the presence of multiple constraints including geometric obstacles and initial location of tool origin. The outcome was a near-optimum tool path for drilling operations with no collision with workpiece features. The computational efficiency of the proposed algorithm was also compared with other methods in available literature using a standard workpiece as a benchmark. The results confirmed that for given examples, the near-optimum collision-free tool paths using the developed model in this paper were almost 50% shorter than the tool path generated by a commercial CAM software.

Background: Simulation models enable learners to have repeated practise at their own time, to master the psycho-motor and sensory acuity aspects of surgery and build their confidence in the procedure. The study aims to develop and evaluate the feasibility of a low-cost drilling model to train surgeons in the drilling task. The model targets three aspects of drilling – (1) Reduce plunge depth, (2) Ability to differentiate between bone and medullary canal and (3) Increase accuracy drilling in various angles.

Methods: This cross-sectional study was conducted after obtaining ethics approval. We invited Consultants in the field of Orthopaedic or Hand Surgery to form the ‘expert’ group, and the ‘novice’ group consisted of participants who had no prior experience in bone drilling. We developed a drilling simulator model made from a polyvinyl chloride (PVC) pipe filled with liquid silicone. This model cost less than US$5. An electric Bosch drill (model GBM 10 RE) with a 1.4 mm K-wire 10 cm in length (6.5 cm outside the drill) was used for drilling. The main outcomes of the study were time taken for drilling, plunge depth, ability to penetrate the far cortex and accuracy.

Results: Thirty-one participants were recruited into the study, of which 15 were experts and 16 were novices. The experts performed significantly better for plunge depth (t = −3.65, p = 0.0003) and accuracy (t = −2.07, p = 0.04). The experts required 20% less time to complete the drilling tasks, but it was not statistically significant (t = −0.79, p = 0.43).

Conclusions: The low-cost drilling model could be useful in training Residents in the drilling task. It will allow Residents to practise independently at their own time and assess their own performance.

Machining processes focused primarily on the quality of surfaces and sizes of machined workpieces, as well as minimizing costs and tool wear. Existing publications related to maximizing the productivity of machining processes, present results that are in fact sub-optimal. The increase in machining regimes improves productivity but may reduce the quality of machined products. Mathematical optimization methods help find a balance between the benefits and drawbacks of machining processes. The tool life of cutters largely influences machining productivity and sets limits on machining speed. The normative tool life equations developed in research labs do not take into account the reliability of machine units and the specificities of technological processes. The normative machining speeds are suboptimal for manufacturing processes and may not maximize productivity. This paper presents a mathematical model for optimizing the cutting speed of a drilling machine tool to achieve its maximum productivity under realistic manufacturing conditions.