An analytical multilayer source stress approach for the modelling of material modifications in machining

Heinzel, Carsten; Sölter, Jens; Gulpak, Maxim; Riemer, Oltmann

doi:10.1016/j.cirp.2017.04.073

Cited by 6 publications

(3 citation statements)

References 11 publications

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“…Nervi et al (2009) established a third-dimensional mathematical model for the prediction of distortion of airframe components from aluminum plates according to the Navier-Lamè equation. Heinzel et al (2017) presented an analytical method based on multilayer source stress model to analyze the effects of initial residual stress and machining induced stress on materials modifications. The shortcoming of analytical models is that these methods are only suitable for very simple and regular parts, it is not possible to calculate the deformation of complex parts.…”

Section: Literature Review 21 Deformation Prediction Approachesmentioning

confidence: 99%

“…Deformation is one of the main causes of geometric error and scrap parts (Moehring et al 2018), especially in machining large scale complex aerospace structural parts, where deformation is typically controlled within 0.05mm/m (Heinzel et al 2017). Deformation mainly results from the release of initial residual stress within the workpiece and machining-induced stress.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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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Section: Literature Review 21 Deformation Prediction Approachesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

On-line part deformation prediction based on deep learning

Zhao

Liu

et al. 2019

J Intell Manuf

View full text Add to dashboard Cite

show abstract

“…The results indicate that the deformation or strain energy of the optimized process reduced rapidly in the early stages, and gradually became stable at the end of the process. Heinzel et al [10] present a new multilayer source stress model which incorporates the effects of machining induced source stresses and the contribution of residual stresses present in the workpiece before machining. Considerable work has been done to explore the relationship between initial residual stress and machining distortion.…”

Section: Introductionmentioning

confidence: 99%

Machining Distortion Minimization of Monolithic Aircraft Parts Based on the Energy Principle

Fan

Tian

et al. 2020

Metals

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Machining distortion is a recurring problem in the machining of monolithic aircraft parts. This paper aims to study the machining distortion minimization of monolithic aircraft parts. Firstly, the energy principle of machining distortion was analyzed. Then, a rapid prediction model of the final part distortion for beam parts was proposed based on the equivalent stress, and the initial bending strain energy contained in the final part was used to characterize the bending distortion risk of the final part. Numerical simulation and milling experiments verified the effectiveness of the proposed prediction model. The relative error between the experimental and calculated results does not exceed 26.5%. Finally, the influence of initial residual stress fluctuation, part geometry and the part location on part distortion was analyzed from the energy point of view. The obtained results indicated that the expected final part distortion can be minimized by adjusting these three factors.

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Control of machining distortion stability in machining of monolithic aircraft parts

Fan

Yang

et al. 2021

Int J Adv Manuf Technol

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An analytical multilayer source stress approach for the modelling of material modifications in machining

Cited by 6 publications

References 11 publications

On-line part deformation prediction based on deep learning

On-line part deformation prediction based on deep learning

Machining Distortion Minimization of Monolithic Aircraft Parts Based on the Energy Principle

Control of machining distortion stability in machining of monolithic aircraft parts

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