A semi-analytical model for predicting the machining deformation of thin-walled parts considering machining-induced and blank initial residual stress

Li, Bianhong; Deng, Hongbin; Hui, David; Hu, Zheng; Zhang, Wanhao

doi:10.1007/s00170-020-05862-1

Cited by 34 publications

(23 citation statements)

References 32 publications

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“…Fig. 1 Deformation caused by blank initial and machining induced residual stress [33] Among them, heqvx,i and heqvy,i are the equivalent thickness of the remaining workpiece in X and Y directions after the layer i is been removed, fi is the volume fraction of removed material in layer i, hi is the total thickness of the workpiece before stripping layer i, t is the stripped thickness of each layer, E and μ are the elastic model and Poisson's ratio respectively.…”

Section: Machining Deformation Calculationmentioning

confidence: 99%

Effects of residual stress and equivalent bending stiffness on the dimensional stability of the thin-walled parts

Gao

Lin

et al. 2022

Int J Adv Manuf Technol

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The monolithic thin-walled parts are widely used in the aeronautic and astronautic field because of its excellent mechanical performance and light weight, but the thin-walled parts are vulnerable to the machining deformation due to its low stiffness and high material removal rate.According to the relative basic theory, the stiffness and internal residual stress of the part are the critical factors affecting the dimensional stability. In this work, the influences of equivalent bending stiffness and residual stress on the dimensional stability of thin-walled parts are studied.Nine typical thin-walled parts in three groups with two materials (7075 aluminum alloy for A1~A3 and B1~B3, and Ti6Al4V titanium alloy for B4~B6) are machined and treated with different processes. Topology optimization technique is used to optimize the structure of parts to enhance the bending stiffness. Corresponding finite element method (FEM) simulations are carried out to further investigate the generation mechanism. The deformations in 312 hours after machining are measured using coordinate measuring machine, and the deformation changes of the parts are obtained and analyzed. Finally, based on topological optimization and 2 stress relief technology, a machining deformation control method for the monolithic thin-walled parts is proposed. Results show that the maximum and average deformations of thin-walled are evidently decreased using the proposed method.

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Section: Machining Deformation Calculationmentioning

confidence: 99%

Effects of residual stress and equivalent bending stiffness on the dimensional stability of the thin-walled parts

Gao

Lin

et al. 2022

Int J Adv Manuf Technol

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“…In summary, the problem of calculating/monitoring the deformation force of an areoengine casing is transformed into the problem of solving for the deformation force and the equivalent flexibility. The change of equivalent flexibility of the part can be computed by using a finite element solver [39]. But to obtain the optimal pre-support force, the deformation force of the part at each machining step needs to be determined, as will be presented next.…”

Section: Definition Of the Problemmentioning

confidence: 99%

A Deformation Force Monitoring Method for Aero-Engine Casing Machining Based on Deep Autoregressive Network and Kalman Filter

Guo

Liu

et al. 2022

Applied Sciences

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Aero-engine casing is a kind of thin-walled rotary part for which serious deformation often occurs during its machining process. As deformation force is an important physical quantity associated with deformation, the utilization of deformation force to control the deformation has been suggested. However, due to the complex machining characteristics of an aero-engine casing, obtaining a stable and reliable deformation force can be quite difficult. To address this issue, this paper proposes a deformation force monitoring method via a pre-support force probabilistic decision model based on deep autoregressive neural network and Kalman filter, for which a set of sophisticated clamping devices with force sensors are specifically developed. In the proposed method, the pre-support force is determined by the predicted value of the deformation force and the equivalent flexibility of the part, while the measurement errors and the reality gaps are reduced by Kalman filter via fusing the predicted and measured data. Both computer simulation and physical machining experiments are carried out and their results give a positive confirmation on the effectiveness of the proposed method. The results are as follows. In the simulation experiments, when the confidence is 84.1%, the success rate of deformation force monitoring is increased by about 30% compared with the traditional approach, and the final impact of clamping deformation of the proposed method is less than 0.003 mm. In the real machining experiments, the results show that the calculation error of deformation by the proposed method based on monitoring the deformation force is less than 0.008 mm.

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“…Considering both the crossbeam and the upright column are over-constrained in the actual experimental setup, to obtain a more accurate beam deformation model, four adjustment factors k 1,c , k 2,c , k 3,c , k 4,c can be introduced in the SBD model of the intermediate module [21], [28], [29]. The crossbeam with both ends being over-constrained can be simplified as fixed-fixed beam.…”

Section: A Sbd Modeling Of the Intermediate Modulementioning

confidence: 99%

“…For this reason, the finite element analysis (FEA) method is used to analyze the links and the joints compliance [19], which is time-consuming. Therefore, semianalytical modeling methods are proposed to take advantages of the efficiency of the analytical method and the flexibility of the FEA method when dealing with complicated boundary conditions, as in [20], [21]. But the existing methods mainly applied to the compliant mechanisms which give short-stroke motion up to several millimeters.…”

Section: Introductionmentioning

confidence: 99%

Non-Geometric Error Compensation for Long-Stroke Cartesian Robot With Semi-Analytical Beam Deformation and Gaussian Process Regression Model

et al. 2021

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Till now, most calibration methods only compensate geometric error caused by inaccurate kinematic parameters, while the desired accuracy may still not be achieved when the robot is performing long-stroke, heavy-duty loading and unloading tasks. In this paper, a generalized semi-analytical beam deformation model is firstly proposed for the Cartesian robot to compensate the non-geometric error due to structural deformation under both distributed and concentrated loads. The adjustment factors are introduced in this model to deal with the over-constrained boundary conditions for both intermediate and side modules of the long-stroke Cartesian robot. This improves the fitness of the coming geometric error model, which assumes the beam to be rigid and straight. In addition, as the major error components which do not conform to the Gaussian distribution have been extracted in earlier steps, the Gaussian process regression model is imported to predict the residual error more accurately. In this way, comprehensive geometric and nongeometric error modeling and compensation procedures are formed for the multi-module, long-stroke Cartesian robot. Simulations and real-time experiments are conducted to validate the effectiveness of the proposed method.

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A semi-analytical model for predicting the machining deformation of thin-walled parts considering machining-induced and blank initial residual stress

Cited by 34 publications

References 32 publications

Effects of residual stress and equivalent bending stiffness on the dimensional stability of the thin-walled parts

Effects of residual stress and equivalent bending stiffness on the dimensional stability of the thin-walled parts

A Deformation Force Monitoring Method for Aero-Engine Casing Machining Based on Deep Autoregressive Network and Kalman Filter

Non-Geometric Error Compensation for Long-Stroke Cartesian Robot With Semi-Analytical Beam Deformation and Gaussian Process Regression Model

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