Analysis of face milling performance on Inconel 718 using FEM and historical data of RSM

Chan, Chee Hoong; Kasim, Mohd Shahir; Izamshah, R.; Bakar, Hadzley Abu; Sundi, S. A.; Zakaria, Kamarul Ariffin; Haron, Che Hassan Che; Ghani, Jaharah A.; Hafiz, M. S.A.

doi:10.1088/1757-899x/270/1/012038

Cited by 4 publications

(2 citation statements)

References 12 publications

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“…The machining process for Inconel 718 includes drilling, face milling and turning. Among the all, face milling is known for a highquality surface nish, producing more precise components for very minimal dimensional tolerance applications [3]. However, the typical machinability rating of Inconel 718 is between 0.09-0.3, which is less than 0.4, 1.2, and 1.9 for stainless steel 304, Al 6061 and 7075 alloys, respectively [4].…”

Section: Introductionmentioning

confidence: 99%

A machine learning model for flank wear prediction in face milling of Inconel 718

Banda

Liu

Farid

et al. 2023

Int J Adv Manuf Technol

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Optimization of ank wear width (VB) progression during face milling of Inconel 718 is challenging due to the synergistic effect of cutting parameters on the complex wear mechanisms and failure modes. The lack of quantitative understanding between VB and the cutting conditions limits the development of the tool life extension. In this study, a Gaussian kernel ridge regression was employed to develop the VB progression model for face milling of Inconel 718 using multi-layer physical vapor deposition-TiAlN/NbN coated carbide inserts with the input feature of cutting speed, feed rate, axial depth of cut, and cutting length. The model showed a root-mean-square error of 30.9 (49.7) µm and R 2 of 0.93 (0.81) in full t (5fold cross-validation test). The statistics along with the cross-plot analyses suggested that the model had a high predictive ability. A new promising condition at the cutting speed of 40 m/min, feed rate of 0.08 mm/tooth, and axial depth of cut of 0.9 mm was designed and experimentally validated. The measured and predicted VB agreed well with each other. This model is thus applicable for VB prediction and optimization in the real face milling operation of Inconel 718.

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Section: Introductionmentioning

confidence: 99%

A machine learning model for flank wear prediction in face milling of Inconel 718

Banda

Liu

Farid

et al. 2023

Int J Adv Manuf Technol

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show abstract

Section: Introductionmentioning

confidence: 99%

A machine learning model for flank wear prediction in face milling of Inconel 718

Banda¹,

Liu

Farid³

et al. 2022

Preprint

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Optimization of flank wear width (VB) progression during face milling of Inconel 718 is challenging due to the synergistic effect of cutting parameters on the complex wear mechanisms and failure modes. The lack of quantitative understanding between VB and the cutting conditions limits the development of the tool life extension. In this study, a Gaussian kernel ridge regression was employed to develop the VB progression model for face milling of Inconel 718 using multi-layer physical vapor deposition-TiAlN/NbN coated carbide inserts with the input feature of cutting speed, feed rate, axial depth of cut, and cutting length. The model showed a root-mean-square error of 30.9 (49.7) µm and R2 of 0.93 (0.81) in full fit (5-fold cross-validation test). The statistics along with the cross-plot analyses suggested that the model had a high predictive ability. A new promising condition at the cutting speed of 40 m/min, feed rate of 0.08 mm/tooth, and axial depth of cut of 0.9 mm was designed and experimentally validated. The measured and predicted VB agreed well with each other. This model is thus applicable for VB prediction and optimization in the real face milling operation of Inconel 718.

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3D Finite Element Simulation of CK45 Steel Face-Milling: Chip Morphology and Tool Wear Validation

Kyratsis

Tzotzis

Davim

2023

SpringerBriefs in Applied Sciences and Technology

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Analysis of face milling performance on Inconel 718 using FEM and historical data of RSM

Cited by 4 publications

References 12 publications

A machine learning model for flank wear prediction in face milling of Inconel 718

A machine learning model for flank wear prediction in face milling of Inconel 718

A machine learning model for flank wear prediction in face milling of Inconel 718

3D Finite Element Simulation of CK45 Steel Face-Milling: Chip Morphology and Tool Wear Validation

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