Prediction on milling distortion for aero-multi-frame parts

Guo, Hao; Zuo, Dun Wen; Wu, Han; Xu, Feng; Guo-quan, Tong

doi:10.1016/j.msea.2007.11.137

Cited by 59 publications

(22 citation statements)

References 6 publications

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“…Rai and Xirouchakis studied the milling thin-walled component distortion based on an FEM machining environment [4]. An FEM method called "housebuilding frame modelling" was used to predict the milling distortion of monolithic aero-component under different milling conditions [5]. For this study, machining loads were used to replace the machininginduced residual stresses, and initial residual stresses were ignored.…”

Section: Introductionmentioning

confidence: 99%

Effect of Initial Residual Stress and Machining-Induced Residual Stress on the Deformation of Aluminium Alloy Plate

Huang¹,

Sun²,

Li³

2015

SV-JME

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During the machining of aerospace thin-walled components, a large amount material is removed, and machining-induced residual stress is induced in the boundary layer of the work piece, which results in deformation of the components. In this study, the effects of material initial residual stress and machining-induced residual stress on the deformation of aluminium alloy plate are studied. A theoretical model of the plate is analysed first, and the experiments of the milling deformation under different initial residual stress conditions are performed. The results show that the machining-induced residual stress is the primary factor of distortion. The coupling action of compressive initial residual stress and machining-induced residual stress increase the plate deformation, and the coupling action of tensile initial residual stress and machining-induced residual stress decrease the plate deformation. The finite element simulation results are compared with experimental results and found to be in good agreement. Highlights• Machining deformation mechanism of plate was studied.• Analysis of deformation factors weights.• The effect of the plate initial residual stress on the magnitude of the deformation.• Studied the relationships between the maximum deflection and the thickness of the specimens.

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Section: Introductionmentioning

confidence: 99%

Effect of Initial Residual Stress and Machining-Induced Residual Stress on the Deformation of Aluminium Alloy Plate

Huang¹,

Sun²,

Li³

2015

SV-JME

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“…Chantzis et al (2013) developed a deformation control method by predicting the deformation caused by initial residual stresses which is obtained by displacement measurements. Guo et al (2009) measured the initial residual stresses by using a modified layer-removal method and established a finite element model of machining distortion for aluminum alloy multi-frame components. Huang et al (2015) presented a prediction model based on FEM to study the effect of black initial residual stress on part deformation and a crack compliance method was used to measure original residual stress.…”

Section: Literature Review 21 Deformation Prediction Approachesmentioning

confidence: 99%

On-line part deformation prediction based on deep learning

Zhao

Liu

et al. 2019

J Intell Manuf

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Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.

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“…Sridhar obtained a combination of milling parameters for the minimum deformation of thin wall parts through orthogonal experiments [4]. Guo proposed a new house-building frame modeling method to simulate the machining deformation of complex multi-frame aluminum alloy thin wall parts [5]. Chen established multi-layer cutting model, multi-layer cutting deformation prediction model and deformation error compensation model for thin wall parts [6].…”

Section: Introductionmentioning

confidence: 99%

Study on deformation and compensation for micromilled thin walls with high aspect ratios

Cheng

Ling

et al. 2021

Int J Adv Manuf Technol

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In order to further improve the dimensional accuracy of micromilled thin walls with high aspect ratios, the machining process should be actively controlled. An active cutting force measurement and cutting parameter compensation device is developed to realize the real-time measurement of radial cutting forces and compensation of radial cutting parameters in thin wall cutting process. Firstly, based on the cantilever beam deformation theory, a mathematical model is established to calculate the deformation and cutting force of thin walls. By measuring the cutting force, the thin wall deformation in the cutting process could be estimated. Then, the obtained incremental thin wall deformation is to be compared with the compensation threshold, which is set at 0.5 μm. If the value of the incremental deformation is less than 0.5 μm, compensation will not be processed. Otherwise, the incremental deformation is used as the compensation value for iterative compensation, until the incremental deformation of the thin wall is less than 0.5 μm. At last, a contrast experiment is carried out. The experimental results show that the introduced device and method are feasible. Machining quality of the thin wall has been obviously improved in dimension precision after the cutting parameter compensations.

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Prediction on milling distortion for aero-multi-frame parts

Cited by 59 publications

References 6 publications

Effect of Initial Residual Stress and Machining-Induced Residual Stress on the Deformation of Aluminium Alloy Plate

Effect of Initial Residual Stress and Machining-Induced Residual Stress on the Deformation of Aluminium Alloy Plate

On-line part deformation prediction based on deep learning

Study on deformation and compensation for micromilled thin walls with high aspect ratios

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