Notch wear prediction model in high speed milling of AerMet100 steel with bull-nose tool considering the influence of stress concentration

Zeng, Haohao; Yan, Rong; Du, Pengle; Zhang, Mingkai; Peng, Fangyu

doi:10.1016/j.wear.2018.05.024

Cited by 19 publications

(5 citation statements)

References 28 publications

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“…In this paper, a FE model is established to predict the temperature distribution in workpiece during LAM process. According to the experiment and simulation results, as well as the analysis reported above, the following conclusions can be drawn: (1) The reasonable agreement obtained between simulated and experimental results under different process parameters (laser power, spindle speed and feed per tooth) indicates that the presented thermal model is feasible. (2) Workpiece temperature increases dramatically with increasing laser power and decreasing feed speed.…”

Section: Discussionmentioning

confidence: 53%

“…Ultra-high strength steel (AerMet100) has been widely used to manufacture important structural parts in aviation industry, such as carrier aircraft landing gear and jet engine shaft. It is a typical difficult-to-cut material because of low thermal conductivity, high strength, and easy to be adhered on tool face [1]. Conventional milling of AerMet100 steel is a low productivity process with a high machining cost, such as the consumption of cutting tool and coolant.…”

Section: Introductionmentioning

confidence: 99%

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A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel

Zeng

Yan

Wang

et al. 2020

MSF

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Laser-assisted milling (LAM) represents an innovative process to enhance productivity in comparison with conventional milling. The workpiece temperature in LAM not only affects the cutting performance of materials, but also the machined surface quality of the part. This paper presents a 3D transient finite element (FE) model for workpiece temperature prediction in LAM. A moving Gaussian laser heat source model is implemented as a user-defined subroutine and linked to ABAQUS. The thermal model is validated by machining AerMet100 steel under different process parameters (laser power, spindle speed and feed per tooth). Good agreement between predicted and measured workpiece temperatures indicates that the FE model is feasible. In addition, the effects of laser spot size and incident angle on workpiece temperature are analyzed based on the proposed model. This work can be further applied to optimize process parameters for controlling the machined surface quality in LAM.

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Section: Discussionmentioning

confidence: 53%

Section: Introductionmentioning

confidence: 99%

A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel

Zeng

Yan

Wang

et al. 2020

MSF

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“…The workpiece material used in this study is AerMet100 steel. The main chemical compositions (wt.%) and material properties of AerMet100 steel are shown in Table 1 [29] and Table 2 [30], respectively. The metallographic morphology of AerMet100 steel is shown in Figure 6.…”

Section: Materials and Machining Processmentioning

confidence: 99%

A Multiphysics Model for Predicting Microstructure Changes and Microhardness of Machined AerMet100 Steel

et al. 2022

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The machined-surface integrity plays a critical role in corrosion resistance and fatigue properties of ultra-high-strength steels. This work develops a multiphysics model for predicting the microstructure changes and microhardness of machined AerMet100 steel. The variations of stress, strain and temperature of the machined workpiece are evaluated by constructing a finite-element model of the orthogonal cutting process. Based on the multiphysics fields, the analytical models of phase transformation and dislocation density evolution are built up. The white layer is modeled according to the phase-transformation mechanism and the effects of stress and plastic strain on real phase-transformation temperature are taken into consideration. The microhardness changes are predicted by a model that accounts for both dislocation density and phase-transformation evolution processes. Experimental tests are carried out for model validation. The predicted results of cutting force, white-layer thickness and microhardness are in good agreement with the measured data. Additionally, from the proposed model, the correlation between the machined-surface characteristics and processing parameters is established.

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“…Even with the "patches" suggested by many researchers, notch wear still occurs and is detrimental to the part quality, and altering machining parameters manually at sporadic time intervals is neither automated nor practical [9]. Therefore, an automated, constant, and consistent way of altering the depth of cut without the need for an involvement of workers is needed [25]. In this study, a work towards the solution for this problem is presented.…”

Section: Figure 1 Notch Wear Initiation and Propagation [6]mentioning

confidence: 99%

Nikel-Bazlı Alaşımlarda (IN-718) Gerçek Değişken-Derinlikli Frezeleme

Ulutan

2020

Çukurova Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi

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Rapid tool wear in machining causes increased process cost. While flank wear and crater wear have been investigated deeply by researchers, notch wear has been somewhat overlooked, despite the fact that it has an important role in the tool replacement decision. Notch wear happens due to impact forces at the depth of cut particularly during intermittent cutting. To avoid frequent tool change decisions, varying the depth of cut constantly during machining has been proposed as an alternative. In this study, milling experiments were conducted on the nickel-based alloy IN-718 where the depth of cut was varied throughout the cut. Results show favorable findings towards eliminating notch wear without compromising from machining efficiency.

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Notch wear prediction model in high speed milling of AerMet100 steel with bull-nose tool considering the influence of stress concentration

Cited by 19 publications

References 28 publications

A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel

A Finite Element Model for Temperature Prediction in Laser-Assisted Milling of AerMet100 Steel

A Multiphysics Model for Predicting Microstructure Changes and Microhardness of Machined AerMet100 Steel

Nikel-Bazlı Alaşımlarda (IN-718) Gerçek Değişken-Derinlikli Frezeleme

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