2019
DOI: 10.1007/s40430-019-1846-0
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Effect of cutting parameters on surface residual stresses in dry turning of AISI 1035 alloy

Abstract: Residual stresses (RSes) induced by turning processes have a great effect on the material properties of the machined components and their abilities to withstand severe loading conditions (creep, fatigue, and stress corrosion cracking). The final state of RSes in a workpiece depends on its material and on the employed cutting parameters/conditions such as cutting speed, depth of cut, feed speed, cutting tool geometry, wear of the tool, cutting tool geometry, cutting tool coating, and cooling. This study introdu… Show more

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Cited by 43 publications
(11 citation statements)
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References 74 publications
(79 reference statements)
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“…The 3-D turning module of AdvantEdge v7.1 software was used for the cutting simulation. The finite element commercial software specialized for machining simulations has been used similar to the work by Huang et al [14], Salman et al [15] and Li [16]. The software allows a coupled mechanical and thermal modeling using an explicit integration method and provides a high-quality solution for an elastic-viscoplastic cutting simulation by adopting automatic remeshing technology to avoid the problem of severe distortion of mesh.…”
Section: Methodsmentioning
confidence: 99%
“…The 3-D turning module of AdvantEdge v7.1 software was used for the cutting simulation. The finite element commercial software specialized for machining simulations has been used similar to the work by Huang et al [14], Salman et al [15] and Li [16]. The software allows a coupled mechanical and thermal modeling using an explicit integration method and provides a high-quality solution for an elastic-viscoplastic cutting simulation by adopting automatic remeshing technology to avoid the problem of severe distortion of mesh.…”
Section: Methodsmentioning
confidence: 99%
“…The predicted results in this study imply that ANN may be used as a robust tool to predict the thermal conductivity of nanofluids. Moreover, it is recommended to apply different metaheurestic methods [72][73][74][75][76] to select the optimal nanofluids parameters that maximize their utilization as heat transfer fluid.…”
Section: Examplementioning
confidence: 99%
“…Moreover, the integration between artificial intelligence modeling and metaheuristic optimizers such as equilibrium optimizer [39], Harris hawks optimizer [40], cat swarm optimizer [41], flower pollination [42], crow search optimizer [43], mayfly optimizer [44], ecosystem-based optimizer [45], manta ray foraging optimizer [46], parasitism-predation optimizer [47], and political optimizer [48], has shown promising application in modeling different engineering problems. This artificial based modeling approaches overcomes the problems of conventional mathematical modeling techniques as well as numerical modeling techniques such as model complexity and nonlinearity [49,50]. Kumar et al [51] developed an ANN model as well as a fuzzy inference model to predict the wear resistance of FSPS made of AA5083 plates.…”
Section: Introductionmentioning
confidence: 99%