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The surface roughness (Ra) of indirect computer-aided design/computer-aided manufacturing (CAD/CAM)-fabricated dental restorations is crucial for their long-term durability. This study intended to evaluate the Ra of five different types of contemporary indirect CAD/CAM restorative materials with varying compositions that were glazed and finished/polished. A total of 75 specimens, disc-shaped (10 mm × 2 mm), were obtained from five materials (n = 15) (Tetric CAD, IPS e.max CAD, IPS e.max ZirCAD, CELTRA Duo, and Vita Enamic) and fabricated by CAD/CAM. One of the two surfaces for each specimen was subjected to glazing, while the other surface was subjected to finishing/polishing. The Ra of the two surfaces in micrometers (µm) was evaluated using a Profilometer, while the surface topography was examined using a scanning electron microscope. Using SPSS, the Kruskal-Wallis, post hoc Conover, and Mann-Whitney tests were used to statistically evaluate the data. A comparison of the Ra for the finished/polished surfaces of the five test materials showed significant differences (p < 0.0001). Among the finished/polished surfaces, the mean rank values of Vita Enamic were significantly higher than the other four test materials (p < 0.0001). A comparison of the Ra of glazed surfaces among the five study materials revealed significant differences (p < 0.0001). The Ra for the IPS e.max ZirCAD material was significantly higher than the rest of the four materials (p < 0.001). A comparison of the Ra for two types of surface conditioning within each of the five test materials showed a significant difference (p < 0.05). Only for IPS e.max ZirCAD was the Ra of the glazed surface significantly higher than the finished/polished surface (p < 0.0001). Significant variations in the surface roughness (Ra) were exhibited between the finished/polished and glazed surfaces of the five test materials. Hybrid ceramics showed the highest Ra values for the finished/polished surfaces, and zirconia exhibited the highest Ra values among the glazed surfaces among the tested materials. The Ra values of either finished/polished or glazed surfaces of the test materials were within the clinically acceptable range (0.2-0.5 µm), except for the glazed surface of the zirconia ceramics (0.84 µm).
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In this modern era where Industry 4.0, plays a crucial role in enhancing productivity, quality, and resource utilization by digitalizing and providing smart operation to industrial systems. Therefore, there is a need to establish a framework that enhances productivity and quality of work to achieve the net-zero from industry. In this study, a comprehensive and generic analytical framework has been established to mitigate or lessen the research and technological gap in the manufacturing sector. In addition to that, the key stages involved in artificial intelligence (AI) based modelling and optimization analysis for manufacturing systems have also been incorporated. To assess the proposed AI framework, electric discharge machining (EDM) as a case study has been selected. The focus enlightens the emergence of optimizing the material removal rate (MRR) and surface roughness (SR) for Inconel 617 (IN617) material. A full factorial design of the experiment was carried out for experimentation. After that, an artificial neural network (ANN) as a modelling framework is selected, and fine-tuning of hyperparameters during training has been carried out. To validate the predictive performance of the trained models, an external validation (Valext) test has been conducted. Through sensitivity analysis (SA) on the developed AI framework, the most influential factors affecting MRR and SR in EDM have been identified. Specifically, powder concentration (Cp) contributes the most to the percentage significance, accounting for 79.00 % towards MRR, followed by treatment (16.35 %) and 4.67 % surfactant concentration (Sc). However, the highest % significance in SR is given by Sc (36.86 %), followed by Cp (33.23 %), and then treatment (29.90 %), respectively. Furthermore, a parametric optimization has been performed using the framework and found that MRR and SR are 93.75 % and 58.90 % better than experimental data. This successful performance optimization proposed by the framework has the potential for application to other manufacturing systems.
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The critical applications of difficult-to-machine Inconel 617 (IN617) compel the process to be accurate enough that the requirement of tight tolerances can be met. Electric discharge machining (EDM) is commonly engaged in its machining. However, the intrinsic issue of over/undercut in EDM complicates the achievement of accurately machined profiles. Therefore, the proficiency of deep cryogenically treated (DCT) copper (Cu) and brass electrodes under modified dielectrics has been thoroughly investigated to address the issue. A complete factorial design was implemented to machine a 300 µm deep impression on IN617. The machining ability of DCT electrodes averagely gave better dimensional accuracy as compared to non-DCT electrodes by 13.5% in various modified dielectric mediums. The performance of DCT brass is 29.7% better overall compared to the average value of overcut (OC) given by DCT electrodes. Among the non-treated (NT) electrodes, the performance of Cu stands out when employing a Kerosene-Span-20 modified dielectric. In comparison to Kerosene-Tween-80, the value of OC is 33.3% less if Kerosene-Span-20 is used as a dielectric against the aforementioned NT electrode. Finally, OC's nonlinear and complex phenomena are effectively modeled by an artificial neural network (ANN) with good prediction accuracy, thereby eliminating the need for experiments.
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Studies on using multifunctional graphene nanostructures to enhance the microfabrication processing of monolithic alumina are still rare and too limited to meet the requirements of green manufacturing criteria. Therefore, this study aims to increase the ablation depth and material removal rate and minimize the roughness of the fabricated microchannel of alumina-based nanocomposites. To achieve this, high-density alumina nanocomposites with different graphene nanoplatelet (GnP) contents (0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.%) were fabricated. Afterward, statistical analysis based on the full factorial design was performed to study the influence of the graphene reinforcement ratio, scanning speed, and frequency on material removal rate (MRR), surface roughness, and ablation depth during low-power laser micromachining. After that, an integrated intelligent multi-objective optimization approach based on the adaptive neuro-fuzzy inference system (ANIFS) and multi-objective particle swarm optimization approach was developed to monitor and find the optimal GnP ratio and microlaser parameters. The results reveal that the GnP reinforcement ratio significantly affects the laser micromachining performance of Al2O3 nanocomposites. This study also revealed that the developed ANFIS models could obtain an accurate estimation model for monitoring the surface roughness, MRR, and ablation depth with fewer errors than 52.07%, 100.15%, and 76% for surface roughness, MRR, and ablation depth, respectively, in comparison with the mathematical models. The integrated intelligent optimization approach indicated that a GnP reinforcement ratio of 2.16, scanning speed of 342 mm/s, and frequency of 20 kHz led to the fabrication of microchannels with high quality and accuracy of Al2O3 nanocomposites. In contrast, the unreinforced alumina could not be machined using the same optimized parameters with low-power laser technology. Henceforth, an integrated intelligence method is a powerful tool for monitoring and optimizing the micromachining processes of ceramic nanocomposites, as demonstrated by the obtained results.
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The superior engineering properties and excellent biocompatibility of titanium alloy (Ti6Al4V) stimulate applications in biomedical industries. Electric discharge machining, a widely used process in advanced applications, is an attractive option that simultaneously offers machining and surface modification. In this study, a comprehensive list of roughening levels of process variables such as pulse current, pulse ON time, pulse OFF time, and polarity, along with four tool electrodes of graphite, copper, brass, and aluminum are evaluated (against two experimentation phases) using a SiC powder-mixed dielectric. The process is modeled using the adaptive neural fuzzy inference system (ANFIS) to produce surfaces with relatively low roughness. A thorough parametric, microscopical, and tribological analysis campaign is established to explore the physical science of the process. For the case of the surface generated through aluminum, a minimum friction force of ~25 N is observed compared with the other surfaces. The analysis of variance shows that the electrode material (32.65%) is found to be significant for the material removal rate, and the pulse ON time (32.15%) is found to be significant for arithmetic roughness. The increase in pulse current to 14 A shows that the roughness increased to ~4.6 µm with a 33% rise using the aluminum electrode. The increase in pulse ON time from 50 µs to 125 µs using the graphite tool resulted in a rise in roughness from ~4.5 µm to ~5.3 µm, showing a 17% rise.
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This study investigates the influence of design, relative density (RD), and carbon fiber (CF) incorporation parameters on mechanical characteristics, including compressive modulus (E), strength, and specific energy absorption (SEA) of triply periodic minimum surface (TPMS) lattice structures. The TPMS lattices were 3D-printed by fused filament fabrication (FFF) using polylactic acid (PLA) and carbon fiber-reinforced PLA(CFRPLA). The mechanical properties of the TPMS lattice structures were evaluated under uniaxial compression testing based on the design of experiments (DOE) approach, namely, full factorial design. Prediction modeling was conducted and compared using mathematical and intelligent modeling, namely, adaptive neuro-fuzzy inference systems (ANFIS). ANFIS modeling allowed the 3D printing imperfections (e.g., RD variations) to be taken into account by considering the actual RDs instead of the designed ones, as in the case of mathematical modeling. In this regard, this was the first time the ANFIS modeling utilized the actual RDs. The desirability approach was applied for multi-objective optimization. The mechanical properties were found to be significantly influenced by cell type, cell size, CF incorporation, and RD, as well as their combination. The findings demonstrated a variation in the E (0.144 GPa to 0.549 GPa), compressive strength (4.583 MPa to 15.768 MPa), and SEA (3.759 J/g to 15.591 J/g) due to the effect of the studied variables. The ANFIS models outperformed mathematical models in predicting all mechanical characteristics, including E, strength, and SEA. For instance, the maximum absolute percent deviation was 7.61% for ANFIS prediction, while it was 21.11% for mathematical prediction. The accuracy of mathematical predictions is highly influenced by the degree of RD deviation: a higher deviation in RD indicates a lower accuracy of predictions. The findings of this study provide a prior prediction of the mechanical behavior of PLA and CFRPLA TPMS structures, as well as a better understanding of their potential and limitations.
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Studies about adding graphene reinforcement to improve the microfabrication performance of alumina (Al2O3) ceramic materials are still too rare and incomplete to satisfy sustainable manufacturing requirements. Therefore, this study aims to develop a detailed understanding of the effect of graphene reinforcement to enhance the laser micromachining performance of Al2O3-based nanocomposites. To achieve this, high-density Al2O3 nanocomposite specimens were fabricated with 0 wt.%, 0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.% graphene nanoplatelets (GNPs) using a high-frequency induction heating process. The specimens were subjected to laser micromachining. Afterward, the effects of the GNP contents on the ablation depth/width, surface morphology, surface roughness, and material removal rate were studied. The results indicate that the micro-fabrication performance of the nanocomposites was significantly affected by the GNP content. All nanocomposites exhibited improvement in the ablation depth and material removal rate compared to the base Al2O3 (0 wt.% GNP). For instance, at a higher scanning speed, the ablation depth was increased by a factor of 10 times for the GNP-reinforced specimens compared to the base Al2O3 nanocomposites. In addition, the MRRs were increased by 2134%, 2391%, 2915%, and 2427% for the 0.5 wt.%, 1 wt.%, 1.5 wt.%, and 2.5 wt.% GNP/Al2O3 nanocomposites, respectively, compared to the base Al2O3 specimens. Likewise, the surface roughness and surface morphology were considerably improved for all GNP/Al2O3 nanocomposite specimens compared to the base Al2O3. This is because the GNP reinforcement reduced the ablation threshold and increased the material removal efficiency by increasing the optical absorbance and thermal conductivity and reducing the grain size of the Al2O3 nanocomposites. Among the GNP/Al2O3 nanocomposites, the 0.5 wt.% and 1 wt.% GNP specimens showed superior performance with minimum defects in most laser micromachining conditions. Overall, the results show that the GNP-reinforced Al2O3 nanocomposites can be machined with high quality and a high production rate using a basic fiber laser system (20 Watts) with very low power consumption. This study shows huge potential for adding graphene to alumina ceramic-based materials to improve their machinability.
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Triply periodic minimum surface (TPMS)-based lattice structures have gained interest for their outstanding capacity to absorb energy, their high load-bearing capacity, and their high surface-to-volume ratio. This study considered three TPMS cell topologies, including Diamond, Gyroid, and Primitive. The FDM process was used to print the lattice structures with two materials: pure polylactic acid (PLA) and carbon fiber-reinforced PLA (PLA + CF). The influence of carbon fiber (CF) incorporation, unit cell type (topologies) and size, and relative density (RD) on mechanical properties and failure patterns were explored comprehensively under uniaxial compression testing. The results demonstrate a change in the compressive modulus (0.09 to 0.47 GPa), compressive strength (2.98 to 13.89 MPa), and specific energy absorption (SEA) (0.14 MJ/m3/g to 0.58 MJ/m3/g) due to the influence of CF incorporation, cell type and size, and RD. Results indicate that the Diamond structure outperformed both Primitive and Gyroid structures in terms of compressive modulus and strength, and SEA. All the CF-based TPMS structures showed a higher compressive modulus. Compressive strength and energy absorption capacity were both slightly enhanced in most PLA + CF-based Diamond structures. On the contrary, Gyroid and Primitive structures showed better performance for pure PLA-based structures in terms of compression strength and specific absorption energy.
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One of the sustainability goals in the aeronautical industry includes developing cost-effective, high-performance engine components possessing complex curved geometries with excellent dimensional precision and surface quality. In this regard, several developments in wire electric discharge machining have been reported, but the influence of flushing attributes is not thoroughly investigated and is thus studied herein. The influence of four process variables, namely servo voltage, flushing pressure, nozzle diameter, and nozzle-workpiece distance, were analyzed on Inconel 718 in relation to geometrical errors (angular and radial deviations), spark gap formation, and arithmetic roughness. In this regard, thorough statistical and microscopical analyses are employed with mono- and multi-objective process optimization. The grey relational analysis affirms the reduction in the process's limitations, validated through confirmatory experimentation results as 0.109 mm spark gap, 0.956% angular deviation, 3.49% radial deviation, and 2.2 µm surface roughness. The novel flushing mechanism improved the spark gap by 1.92%, reducing angular and radial deviations by 8.24% and 29.11%, respectively.
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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.
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This study evaluated the microstructure, grain size, and mechanical properties of the alloy 800H rotary friction welds in as-welded and post-weld heat-treated conditions. The standards for the alloy 800H not only specify the composition and mechanical properties but also the minimum grain sizes. This is because these alloys are mostly used in creep resisting applications. The dynamic recrystallization of the highly strained and plasticized material during friction welding resulted in the fine grain structure (20 ± 2 µm) in the weld zone. However, a small increase in grain size was observed in the heat-affected zone of the weldment with a slight decrease in hardness compared to the base metal. Post-weld solution heat treatment (PWHT) of the friction weld joints increased the grain size (42 ± 4 µm) in the weld zone. Both as-welded and post-weld solution heat-treated friction weld joints failed in the heat-affected zone during the room temperature tensile testing and showed a lower yield strength and ultimate tensile strength than the base metal. A fracture analysis of the failed tensile samples revealed ductile fracture features. However, in high-temperature tensile testing, post-weld solution heat-treated joints exhibited superior elongation and strength compared to the as-welded joints due to the increase in the grain size of the weld metal. It was demonstrated in this study that the minimum grain size requirement of the alloy 800H friction weld joints could be successfully met by PWHT with improved strength and elongation, especially at high temperatures.
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Laser-powder bed fusion (L-PBF) process is a family of modern technologies, in which functional, complex (3D) parts are formed by selectively melting the metallic powders layer-by-layer based on fusion. The machining of L-PBF parts for improving their quality is a difficult task. This is because different component orientations (L-PBF-layer orientations) produce different quality of machined surface even though the same cutting parameters are applied. In this paper, stainless steel grade SS 316L parts from L-PBF were subjected to the finishing (milling) process to study the effect of part orientations. Furthermore, an attempt is made to suppress the part orientation effect by changing the layer thickness (LT) of the parts during the L-PBF process. L-PBF parts were fabricated with four different layer thicknesses of 30, 60, 80 and 100 µm to see the effect of the LT on the finish milling process. The results showed that the layer thickness of 60 µm has significantly suppressed the part orientation effect as compared to the other three-layer thicknesses of 30, 80 and 100 µm. The milling results showed that the three-layer thickness including 30, 80 and 100 µm presented up to a 34% difference in surface roughness among different part orientations while using the same milling parameters. In contrast, the layer thickness of 60 µm showed uniform surface roughness for the three-part orientations having a variation of 5-17%. Similarly, the three-layer thicknesses 30, 80 and 100 µm showed up to a 25%, 34% and 56% difference of axial force (Fa), feed force (Ff) and radial force (Fr), respectively. On the other hand, the part produced with layer thickness 60 µm showed up to 11%, 25% and 28% difference in cutting force components Fa, Ff and Fr, respectively. The three-layer thicknesses 30, 80 and 100 µm in micro-hardness were found to vary by up to 14.7% for the three-part orientation. Negligible micro-hardness differences of 1.7% were revealed by the parts with LT 60 µm across different part orientations as compared to 6.5-14% variations for the parts with layer thickness of 30, 80 and 100 µm. Moreover, the parts with LT 60 µm showed uniform and superior surface morphology and reduced edge chipping across all the part orientations. This study revealed that the effect of part orientation during milling becomes minimum and improved machined surface integrity is achieved if the L-PBF parts are fabricated with a layer thickness of 60 µm.
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In this paper, an experimental investigation into the machinability of AISI 316 alloy during finishing end milling operation under different cooling conditions and with varying process parameters is presented. Three environmental-friendly cooling strategies were utilized, namely, dry, minimal quantity lubrication (MQL) and MQL with nanoparticles (Al2O3), and the variable process parameters were cutting speed and feed rate. Power consumption and surface quality were utilized as the machining responses to characterize the process performance. Surface quality was examined by evaluating the final surface roughness and surface integrity of the machined surface. The results revealed a reduction in power consumption when MQL and MQL + Al2O3 strategies were applied compared to the dry case by averages of 4.7% and 8.6%, respectively. Besides, a considerable reduction in the surface roughness was noticed with average values of 40% and 44% for MQL and MQL + Al2O3 strategies, respectively, when compared to the dry condition. At the same time, the reduction in generated surface roughness obtained by using MQL + Al2O3 condition was marginal (5.9%) compared with using MQL condition. Moreover, the results showed that the improvement obtained in the surface quality when using MQL and MQL + Al2O3 coolants increased at higher cutting speed and feed rate, and thus, higher productivity can be achieved without deteriorating final surface quality, compared to dry conditions. From scanning electron microscope (SEM) analysis, debris, furrows, plastic deformation irregular friction marks, and bores were found in the surface texture when machining under dry conditions. A slight smoother surface with a nano-polishing effect was found in the case of MQL + Al2O3 compared to the MQL and dry cooling strategies. This proves the effectiveness of lubricant with nanoparticles in reducing the friction and thermal damages on the machined surface as the friction marks were still observed when machining with MQL comparable with the case of MQL + Al2O3.
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The studies about the effect of the graphene reinforcement ratio and machining parameters to improve the machining performance of Ti6Al4V alloy are still rare and incomplete to meet the Industry 4.0 manufacturing criteria. In this study, a hybrid adaptive neuro-fuzzy inference system (ANFIS) with a multi-objective particle swarm optimization method is developed to obtain the optimal combination of milling parameters and reinforcement ratio that lead to minimize the feed force, depth force, and surface roughness. For achieving this, Ti6Al4V matrix nanocomposites reinforced with 0 wt.%, 0.6 wt.%, and 1.2 wt.% graphene nanoplatelets (GNPs) are produced. Afterward, a full factorial approach was used to design experiments to investigate the effect of cutting speed, feed rate, and graphene nanoplatelets ratio on machining behaviour. After that, artificial intelligence based on ANFIS is used to develop prediction models as the fitness function of the multi-objective particle swarm optimization method. The experimental results showed that the developed models can obtain an accurate estimation of depth force, feed force, and surface roughness with a mean absolute percentage error of 3.87%, 8.56%, and 2.21%, respectively, as compared with experimentally measured outputs. In addition, the developed artificial intelligence models showed 361.24%, 35.05%, and 276.47% less errors for depth force, feed force, and surface roughness, respectively, as compared with the traditional mathematical models. The multi-objective optimization results from the new approach indicated that a cutting speed of 62 m/min, feed rate of 139 mm/min, and GNPs reinforcement ratio of 1.145 wt.% lead to the improved machining characteristics of GNPs reinforced Ti6Al4V matrix nanocomposites. Henceforth, the hybrid method as a novel artificial intelligent method can be used for optimizing the machining processes with complex relationships between the output responses.
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Titanium alloys, especially Ti-6Al-4V, which is considered a difficult-to-cut material, bears numerous applications in aerospace and biomedical sectors. The criticality of the accurate formation of the machined cavity for the said applications and properties of Ti-6Al-4V accentuated the use of electric discharge machining (EDM). However, the issues of lower material removal rate (MRR) and tool wear (TWR) discouraged the use of EDM. These inherent issues hold a pivotal role regarding the sustainable machining of Ti-alloy. Therefore, in this research the potentiality of kerosene-based dielectric, having graphene nanoparticles, is comprehensively examined for the sustainable EDM of Ti-6Al-4V, which was not focused upon yet. Experimentation was performed under Taguchi's design (L18) with three types of electrodes, namely Aluminum, Brass and Copper. In total, 36 experiments were conducted, of which 18 were with graphene-mixed dielectric and the remaining were with kerosene. Experimental results reveal that the brass electrode with negative tool polarity yields higher MRR for both types of dielectrics. The maximum MRR (7.602 mm3/min) achieved with graphene mixed dielectric is 64.5% greater as compared to that obtained with kerosene (4.621 mm3/min). Moreover, the minimum TWR obtained for graphene-based dielectric, i.e., 0.17 mg/min is approximately 1.5 times less than that achieved with kerosene.
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This paper presents an experimental study into the comparative response of wiper and round-nose conventional carbide inserts coated with TiCN + AL2O3 + TiN when turning an AISI 4340 steel alloy. The optimal process parameters, as identified by pre-experiments, were used for both types of inserts to determine the machined surface quality, tool wear, and specific cutting energy for different cutting lengths. The wiper inserts provided a substantial improvement in the attainable surface quality compared with the results obtained using conventional inserts under optimal cutting conditions for the entire range of the machined lengths. In addition, the conventional inserts showed a dramatic increase in roughness with an increased length of the cut, while the wiper inserts showed only a minor increase for the same length of cut. A scanning electron microscope was used to examine the wear for both types of inserts. Conventional inserts showed higher trends for both the average and maximum flank wear with cutting length compared to the wiper inserts, except for lengths of 200-400 mm, where conventional inserts showed less average flank wear. A higher accumulation of deposited chips was observed on the flank face of the wiper inserts than the conventional inserts. The experimental results demonstrated that edge chipping was the chief tool wear mechanism on the rake face for both types of insert, with more edge chipping observed in the case of the conventional inserts than the wiper inserts, with negligible evidence of crater wear in either case. The wiper inserts were shown to have a higher specific cutting energy than those detected with conventional inserts. This was attributed to (i) the irregular nose feature of the wiper inserts differing from the simpler round nose geometry of the conventional inserts and (ii) a higher tendency of chip accumulation on the wiper inserts.
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This paper presents the results of an investigation on the capacity of wire electrical discharge machining (WEDM) to produce microchannels in the Nickel-based alloy, Monel 400. The main objective of the current study is to produce microchannels with desired/target geometry and acceptable surface quality. Square cross-sectional microchannels with dimensions of 500 × 500 µm were investigated. Experiments were conducted based on the one-factor-at-a-time approach for the key input WEDM process parameters, namely pulse-on time (TON), pulse-off time (TOFF), average gap voltage (VGAP), wire feed (WF), and dielectric flow rate (FR). Dimensional accuracy, machining speed, surface roughness, surface morphology, microhardness, and microstructure were analyzed to evaluate the microchannels. The minimum errors of 6% and 3% were observed in the width and depth of the microchannels, respectively. Furthermore, microchannels with enhanced surface integrity could be produced exhibiting smooth surface morphology and shallow recast layer (~0-2.55 µm).
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Titanium alloys are widely used in various applications including biomedicine, aerospace, marine, energy, and chemical industries because of their superior characteristics such as high hot strength and hardness, low density, and superior fracture toughness and corrosion resistance. However, there are different challenges when machining titanium alloys because of the high heat generated during cutting processes which adversely affects the product quality and process performance in general. Thus, optimization of the machining conditions while machining such alloys is necessary. In this work, an experimental investigation into the influence of different cutting parameters (i.e., depth of cut, cutting length, feed rate, and cutting speed) on surface roughness (Rz), flank wear (VB), power consumption as well as the material removal rate (MRR) during high-speed turning of Ti-6Al-4V alloy is presented and discussed. In addition, a backpropagation neural network (BPNN) along with the technique for order of preference by similarity to ideal solution (TOPSIS)-fuzzy integrated approach was employed to model and optimize the overall cutting performance. It should be stated that the predicted values for all machining outputs demonstrated excellent agreement with the experimental values at the selected optimal solution. In addition, the selected optimal solution did not provide the best performance for each measured output, but it achieved a balance among all studied responses.
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Ti-6Al-4V is a material of high interest in various industrial sectors including biomedical, automotive and aerospace. Conventional means of machining encounter different types of difficulties. Electric discharge machining (EDM) is not a contest of hardness. Circular impressions of micro-depth are produced in Ti-6Al-4V using four different electrode materials including aluminum, brass, graphite and copper, each assigned positive and negative polarity. In order to get precise control over the geometry of micro-impressions dimensional accuracy and tool wear must be controlled. Thus, EDM performance has been evaluated in terms of axial dimensional error (D.E_Axi), radial dimensional error (D.E_Rad), tool length reduction (TLR), and surface roughness (SR). Since the EDM process is stochastic in nature therefore in addition to tool polarity only two factors are considered as variables, i.e. discharge current and pulse-time-ratio (ration of on-time to off-time). The behaviors of each of the four electrode materials are compared together under each of the two polarities and two variables for each of the four response characteristics. The search is carried out to select the most appropriate tool electrode polarity (common for all responses) and a single common electrode capable of minimizing all the four response measures simultaneously. Moreover, microstructures of the machined impressions are discussed. Without any compromise in the minimum values of response measures, no single polarity and a single electrode are found common. However, with a slight compromise over the machining measures negative tool polarity and copper electrode served the purpose of set objectives (minimum of D.E, TLR, and SR). The expanse of compromise is found to be ≤ 50 µm in axial and radial dimensional errors, 0.8 µm in surface roughness and no compromise in tool length reduction if the copper electrode is assigned with negative polarity.
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Nowadays, titanium alloys are achieving a significant interest in the field of aerospace, biomedical, automobile industries especially due to their extremely high strength to weight ratio, corrosive resistance, and ability to withstand higher temperatures. However, titanium alloys are well known for their higher chemical reactive and low thermal conductive nature which, in turn, makes it more difficult to machine especially at high cutting speeds. Hence, optimization of high-speed machining responses of Ti-6Al-4V has been investigated in the present study using a hybrid approach of multi-objective optimization based on ratio analysis (MOORA) integrated with regression and particle swarm approach (PSO). This optimization approach is employed to offer a balance between achieving better surface quality with maintaining an acceptable material removal rate level. The position of global best suggested by the hybrid optimization approach was: Cutting speed 194 m/min, depth of cut of 0.1 mm, feed rate of 0.15 mm/rev, and cutting length of 120 mm. It should be stated that this solution strikes a balance between achieving lower surface roughness in terms of Ra and Rq, with reaching the highest possible material removal rate. Finally, an investigation of the tool wear mechanisms for three studied cases (i.e., surface roughness based, productivity-based, optimized case) is presented to discuss the effectiveness of each scenario from the tool wear perspective.