In this article, the effects of material hardness and high-pressure coolant jet over dry machining are evaluated in respect of surface roughness and cutting temperature using Taguchi L
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orthogonal ...array. The experimental data was analyzed using empirical cumulative distribution function and box plot with respect to material hardness and machining environment. Afterward, optimization of the quality responses is performed using signal-to-noise ratio. As part of Taguchi optimization, the “smaller is better” was adopted as optimization principle; the design of experiment was used for parameters orientation, and the analysis of variance was used for determining the effects of control factors. For the present experimental studies, three types of hardened steels (40 HRC, 48 HRC, and 56 HRC) were turned by coated carbide insert at industrial speed–feed combinations under both dry and high-pressure coolant jet. Depth of cut, being a less significant parameter, was kept fixed. The high-pressure coolant jet was found successful in reducing cutting temperature, surface roughness, and tool wear. The statistical analysis showed that work material hardness is the most significant factor for both cutting temperature and surface roughness. However, for surface roughness, other variables exerted somewhat similar contribution, while in determining the cutting temperature, the environment demonstrated crucial role. The confirmation tests showed 15.85 and 0.28 % error in predicting surface roughness and cutting temperature, respectively.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
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•Artificial neural network based predictive model of surface roughness.•Dry and high pressure coolant (HPC) applied turning of hardened steels.•Levenberg–Marquardt, Bayesian ...regularization, scaled conjugate gradient training.•3-4-2 ANN structure trained by BR is recommended.•Effective cooling and lubrication by HPC reduced roughness parameter.
In this study, an artificial neural network (ANN) based predictive model of average surface roughness in turning hardened EN 24T steel has been presented. The prediction was performed by using Neural Network Tool Box 7 of MATLAB R2015a for different levels of cutting speed, feed rate, material hardness and cutting conditions. To be specific the dry and high pressure coolant (HPC) jet environments were explored as cutting conditions. The experimental runs were determined by full factorial design of experiment. Afterward the 3-n-1, 3-n-2 and 4-n-1 ANN architectures were trained by utilizing the Levenberg–Marquardt (LM), Bayesian regularization (BR) and scaled conjugate gradient (SCG) algorithms, and evaluated based on the lowest root mean square error (RMSE). The 3-10-1 and 3-4-2 ANN models, trained by BR, revealed the lowest RMSE. A good prediction fit of the models was established by the regression coefficients higher than 0.997. At last, the behavior of the surface roughness in respect of speed-feed-hardness for dry and HPC conditions has been analyzed. The HPC reduced surface roughness by the efficient cooling and lubrication whereas the higher hardness of material induced higher average surface roughness due to higher restraining force against tool imposed cutting force.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Owing to superior physio-chemical characteristics, titanium alloys are widely adopted in numerous fields such as medical, aerospace, and military applications. However, titanium alloys have poor ...machinability due to its low thermal conductivity which results in high temperature during machining. Numerous lubrication and cooling techniques have already been employed to reduce the harmful environmental footprints and temperature elevation and to improve the machining of titanium alloys. In this current work, an attempt has been made to evaluate the effectiveness of two cooling and lubrication techniques namely cryogenic cooling and hybrid nanoadditive–based minimum quantity lubrication (MQL). The key objective of this experimental research is to compare the influence of cryogenic CO
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and hybrid nanofluid–based MQL techniques for turning Ti–6Al–4V. The used hybrid nanofluid is alumina (Al
2
O
3
) with multi-walled carbon nanotubes (MWCNTs) dispersed in vegetable oil. Taguchi-based L9 orthogonal-array was used for the design of the experiment. The design variables were cutting speed, feed rate, and cooling technique. Results showed that the hybrid nanoadditives reduced the average surface roughness by 8.72%, cutting force by 11.8%, and increased the tool life by 23% in comparison with the cryogenic cooling. Nevertheless, the cryogenic technique showed a reduction of 11.2% in cutting temperature compared to the MQL-hybrid nanofluids at low and high levels of cutting speed and feed rate. In this regard, a milestone has been achieved by implementing two different sustainable cooling/lubrication techniques.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
An effective method of fluid application such as high-pressure coolant (HPC) augments the performance characteristics by producing quality products. Effective control of parameters, prior to actual ...machining, prevents the loss of resources which in turn maximize the productivity. Thus, an adequate prediction model of surface roughness and an optimization model of control parameters must be determined that can be efficiently used for HPC employed machining. In this regard, this article presents the formulation of two predictive models of surface roughness, one by using artificial intelligence-based technique, namely support vector regression (SVR), and another by applying conventional technique called response surface methodology, in turning of hardened and tempered AISI 1060 steel in dry cutting and under the application of pressurized oil jet. The cutting speed, feed rate and material hardness were considered as input variables for model formulation, and based on these factors, the full factorial experimental design plan was conducted. The performance of the predictive models was evaluated on the basis of root mean square error. Additionally, the effects of control factors were evaluated by using analysis of variance. Furthermore, separate optimization models were created using composite desirability function and genetic algorithm (GA) to determine the control factor setting corresponding to minimal surface roughness. Both of the optimization models suggested an optimal parameter setting at 0.10 mm/rev feed rate, 161 m/min cutting speed and ~43 HRC material hardness. The adequacy of the optimization models was evaluated by a confirmation test. The predictive model by SVR and optimization model by GA provided the highest accuracy.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper presents the analysis of average surface roughness, cutting force, and feed force in turning of difficult-to-machine Ti-6Al-4V alloy by experimental investigation and performance modeling. ...Based on knowledge of the literature, to pacify the elevated temperature in machining Ti-6Al-4V and to ensure a clean environment, the experiments are carried out in cryogenic (liquid nitrogen) condition by following the Taguchi L
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mixed-level orthogonal array. Afterward, the models of responses have been formulated by the response surface methodology (RSM) and artificial neural network (ANN). The higher values of correlation coefficient (≥96%) and lower values of error determined the adequacy of the developed models. Comparative study of both models revealed that the RSM-based model revealed greater accuracy for the testing data and hence recommended. Analysis of variance (ANOVA) determined the effects of cutting speed, feed rate, and insert configuration on the quality characteristics. The results revealed that a cutting speed not exceeding 110 m/min is likely to generate favorable machining responses. In addition, the higher feed rate was found to ensure better machining performances. Moreover, the desirability-based multi-response optimization determined that a cutting speed of 78 m/min, a feed rate of 0.16 mm/rev, and use of the SNMM tool insert are capable of minimizing surface roughness at 1.05 μm, main cutting force at 315 N, and feed force at 208 N.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Machinability of Ni-based aerospace alloy is considered to be difficult due to its numerous intrinsic properties. However, the machining performance of nickel-based alloys can be improved with the ...geometric alteration on the tool rake zone and by the proper cooling-lubrication mechanism. However, the complete consideration of the proper mechanisms is required. To fill this gap, the impact of cutting speed, machining time, and tool texturing was thoroughly inquired about along with cooling conditions on machinability indices such as tool wear, chip morphology, and cutting forces as well as surface finish. The machining tests were done with textured tools on Inconel 718 alloy at cutting speeds 80, 120, and 180 m/min respectively. Then, the comparison of machining characteristics with or without using solid lubrication mixed minimum quantity lubrication system were made. For that, the time of cutting was restricted to 10 min for comparison purposes. For machining at 80 and 180 m/min, the noteworthy reduction in flank and crater wear was observed, whereas at 120 m/min, small reduction is seen from 1 to 10 min under NFMQL condition. The surface roughness was found to be higher under a dry environment compared to a NFMQL environment due to the low coefficient of friction of MoS
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at a constant feed rate with an increase in cutting speed. The worst surface finish with maximum of 28.17% difference under dry machining condition was observed. It was clearly seen that the blend of canola oil mixed with MoS
2
particles improved the cooling and friction at the cutting zone. In addition, analysis on the scanning electron microscope (SEM) has been done on the worn tools for better comprehension of tool wear during turning of Inconel 718 alloy. Finally, it has been reported that the performance of the textured tool under solid lubrication conditions is better to achieve a lower tool wear (
V
b
), surface roughness (
R
a
), cutting forces, and acceptable form of chips.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In machining of soft alloys, the sticky nature of localized material instigated by tool-work interaction exacerbates the tribological attitude and ultimately demeans it machinability. Moreover, the ...endured severe plastic deformation and originated thermal state alter the metallurgical structure of machined surface and chips. Also, the used tool edges are worn/damaged. Implementation of cooling-lubrication (C/L) agents to reduce friction at faying surfaces can ameliorate overall machinability. That is why, this paper deliberately discussed the influence of pure C/L methods, i.e., such as dry cutting (DC) and nitrogen cooling (N
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), as well as hybrid C/L strategies, i.e., nitrogen minimum quantity lubrication (N
2
MQL) and Ranque–Hilsch vortex tube (RHVT) N
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MQL conditions in turning of Al 7075-T6 alloy, respectively. With respect to the variation of cutting speed and feed rate, at different C/Ls, the surface roughness, tool wear, and chips are studied by using SEM and 3D topographic analysis. The mechanism of heat transfer by the cooling methods has been discussed too. Furthermore, the new chip management model (CMM) was developed under all C/L conditions by considering the waste management aspects. It was found that the R-N
2
MQL has the potential to reduce the surface roughness up to 77% and the tool wear up to 118%. This significant improvement promotes sustainability in machining industry by saving resources. Moreover, the CMM showed that R-N
2
MQL is more attractive for cleaner manufacturing system due to a higher recyclability, remanufacturing, and lower disposal of chips.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The constant pressure on the manufacturers to innovate and implement sustainable processes has triggered researching on machining with low carbon footprint, minimum energy consumption by machine ...tools, and improved products at the lowest cost—this is exactly done in this paper. Herein, the advanced cooling lubrication, i.e., nanofluid assistance, besides dry and flood cooling, during machining has been experimented, and used as the basis for sustainability assessment. This assessment is carried out in respect of surface quality and power consumption as well as the impact on environment, cost of machining, management of waste, and finally the safety and health issues of operators. For a better sustainability, a systematic optimization has been performed. In addition, the solution for an improved machinability has been proposed along with the statistically verified mathematical models of machining responses. Results showed that the nanofluid minimum quantity lubrication showed the most sustainable performance with a total weighted sustainability index 0.7, and it caused the minimum surface roughness and power consumption. The highest desirable (desirability = 0.9050) optimum results are the cutting speed of 116 m/min, depth of cut 0.25 mm, and feed rate of 0.06 mm/rev. Furthermore, a lower feed rate is suggested for better surface quality while for reduced power consumption the lower control factors are better.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
In this study, turning of titanium (Ti-6Al-4V) alloy under four different environments as dry, vegetable oil under minimum quantity lubrication (MQL), texture on the rake face filled with graphene ...particles, and graphene-mixed vegetable oil under nanoparticle-based minimum quantity lubrication (NMQL) with textured carbide tools is investigated. Results shows that maximum tool life, lower cutting forces, and minimum cutting temperature generated are with NMQL followed by MQL, texture filled with graphene, and dry turning. The tool life under NMQL is improved by 178 to 190%, main cutting force minimized by 36 to 40%, and cutting temperature reduced by 31 to 42% as compared with dry condition at various cutting speeds. The best turning performance is achieved under NMQL which is mainly due to higher thermal conductivity of MQL fluid mixture and shearing action imparted by graphene on different contact surfaces of tool. Further, the phenomena of improved thermal conductivity and shearing action imparted by graphene are explained by using transient hot-wire/SEM/Raman spectroscopy in this study. Finally, it is concluded that graphene has potential to act as lubricant/coolant in turning processes.
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DOBA, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, IZUM, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, SIK, UILJ, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
This paper presents the development of mathematical, predictive and optimization models of average surface roughness parameter (
R
a
) in turning hardened AISI 1060 steel using coated carbide tool in ...dry condition. Herein, the mathematical model is formulated by response surface methodology (RSM), predictive model by fuzzy inference system (FIS), and optimization model by simulated annealing (SA) technique. For all these models, the cutting speed, feed rate and material hardness were considered as input factors for full factorial experimental design plan. After the experimental runs, the collected data are used for model development and its subsequent validation. It was found, by statistical analysis, that the quadratic model is suggested for
R
a
in RSM. The adequacy of the models was checked by error analysis and validation test. Furthermore, the constructed model was compared with an analytical model. The analysis of variance revealed that the material hardness exerts the most dominant effect, followed by the feed rate and then cutting speed. Eventually, the RSM model was found with a coefficient of determination value of 99.64%; FIS model revealed 79.82% prediction accuracy; and SA model resulted in more than 70% improved surface roughness. Therefore, these models can be used in industries to effectively control the hard turning process to achieve a good surface quality.
