On the machining induced residual stresses in IN718 nickel-based alloy: Experiments and predictions with finite element simulation

-Inconel 718 is a typical hard-to-machine material that requires thermally enhanced machining technology such as laser-assisted milling. Based upon finite element analysis, this study simulates the forces in the laser-assisted milling process of Inconel 718 considering the effects of grain growth due to c 0 and c 00 phases. The c 00 phase is unstable and becomes the d phase, which is likely to precipitate at a temperature over 750°C. The temperature around the center of spot in the experiments is 850°C, so the phase transformation and grain growth happen throughout the milling process. In the analysis, this study includes the microstructure evolution while accounting for the effects of dynamic recrystallization and grain growth through the Avrami model. The grain growth reduces the yield stress and flow stress, which improves the machinability. In finite element analysis (FEA), several boundary conditions of temperature varying with time are defined to simulate the movement of laser spot, and the constitutive model is described by Johnson-Cook equation. In experiments, this study collects three sets of cutting forces and finds that the predicted values are in close agreements with measurements especially in feed direction, in which the smallest error is around 5%. In another three simulations, this study also examines the effect of laser preheating on the cutting forces by comparison with a traditional milling process without laser assist. When the laser is off, the forces increase in all cases, which prove the softening effect of laser-assisted milling. In addition, when the axial depth of milling increases, the laser has a more significant influence, especially in axial direction, in which the force with laser is more than 18% smaller than the one without laser. Overall, this study validates the influence of laser-assisted milling on Inconel 718 by predicting the cutting forces in FEA.

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“…Therefore, it presents six simulations and three experiments. The mechanical and thermal properties of Inconel 718 are listed in Table 3 [19].…”

Section: Simulation Modeling and Experimental Validationmentioning

confidence: 99%

Microstructure-sensitive flow stress modeling for force prediction in laser assisted milling of Inconel 718

Pan

Feng

et al. 2017

Manufacturing Rev.

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“…Table 3. Johnson-Cook parameters of GH4169 [20][21] The FE model of prestressed cutting is shown in Fig.2. Base on the method of prestress loading proposed by the author's previous literatures [1][2], the internal expansion method is adopt to load the prestress on the workpiece.…”

Section: Constitutive Modelmentioning

confidence: 99%

FEM-DEM coupling simulations of the tool wear characteristics in prestressed machining superalloy

Peng

Tang

et al. 2016

MATEC Web Conf.

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Abstract. Due to the complicated contact loading at the tool-chip interface, ceramic tool wear in prestressed machining superalloy is rare difficult to evaluate only by experimental approaches. This study aims to develop a methodology to predict the tool wear evolution by using combined FEM and DEM numerical simulations. Firstly, a finite element model for prestressed cutting is established, subsequently a discrete element model to describe the toolchip behaviour is established based on the obtained boundary conditions by FEM simulations, finally, simulated results are experimentally validated. The predicted tool wear results show nice agreement with experiments, the simulation indicates that, within a certain range, higher cutting speed effectively results in slighter wear of Sialon ceramic tools, and deeper depth of cut leads to more serious tool wear.

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“…Therefore, it is necessary to develop predictive mathematical models in order to determine the surface roughness, in advance (before the actual machining is performed). There are a large number of tools available to develop predictive models for machining processes, namely Response Surface Methodology (RSM), Artificial Neural Network (ANN), Taguchi method, etc [5][6][7][8][9]. Among all predictive model technique, RSM is a widely used tool for designing experiments and analyzing data to predict model and to select the best mathematical model that represents the real life experimental results [10].…”

Section: Introductionmentioning

confidence: 99%

Effect of machining parameters on surface finish of Inconel 718 in end milling

2017

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Abstract. Surface finish is an important criteria in machining process and selection of proper machining parameters is important to obtain good surface finish. In the present work effects of the machining parameters in end milling of Inconel 718 were investigated. Central composite design was used to design the total number of experiments. A Mathematical model for surface roughness has been developed using response surface methodology. In this study, the influence of cutting parameters such as cutting speed, feed rate and depth of cut on surface roughness was analyzed. The study includes individual effect of cutting parameters on surface roughness as well as their interaction. The analysis of variance (ANOVA) was employed to find the validity of the developed model. The results show that depth of cut mostly affected the surface roughness. It is also observed that surface roughness values are comparable in both dry and wet machining conditions.

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On the machining induced residual stresses in IN718 nickel-based alloy: Experiments and predictions with finite element simulation

Cited by 92 publications

References 17 publications

Microstructure-sensitive flow stress modeling for force prediction in laser assisted milling of Inconel 718

Microstructure-sensitive flow stress modeling for force prediction in laser assisted milling of Inconel 718

FEM-DEM coupling simulations of the tool wear characteristics in prestressed machining superalloy

Effect of machining parameters on surface finish of Inconel 718 in end milling

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