Machining deformation prediction of thin-walled workpieces in five-axis flank milling

Wang, Liping; Si, Hao

doi:10.1007/s00170-018-2248-7

Cited by 47 publications

(13 citation statements)

References 29 publications

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“…Resonance and amplification can be predicted based on the differential equations of the dynamic behavior of the system, which can be calculated through semi-analytical methods. These models have usually been developed in MATLAB [25,48,49] or C++ [45]. However, this solution is time consuming and has low accuracy.…”

Section: Computational Solutionsmentioning

confidence: 99%

“…Their models were validated with an error lower than 15%. Wang and Si [49] discarded the mesh subdivision or mesh adaptive technology because both considerably increased the computational burden, whereas the accuracy was not improved. Meanwhile, they simulated stiffness variation by removing the two elements adjacent to the cutter location, improving deflection accuracy.…”

Section: Computational Solutionsmentioning

confidence: 99%

“…Some of them studied the effect of the wall thickness [67,73], the depth of cut [66] or the feed rate [61,78] on the dimensional error. Others authors considered the tool position [49,70] and the fixture system [5]. Zhang et al [28] went further by including the effect of the distance to the clamping system.…”

Section: Computational Solutionsmentioning

confidence: 99%

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Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

et al. 2019

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Thin-wall parts are common in the aeronautical sector. However, their machining presents serious challenges such as vibrations and part deflections. To deal with these challenges, different approaches have been followed in recent years. This work presents the state of the art of thin-wall light-alloy machining, analyzing the problems related to each type of thin-wall parts, exposing the causes of both instability and deformation through analytical models, summarizing the computational techniques used, and presenting the solutions proposed by different authors from an industrial point of view. Finally, some further research lines are proposed.

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Section: Computational Solutionsmentioning

confidence: 99%

Section: Computational Solutionsmentioning

confidence: 99%

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Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

et al. 2019

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“…The proposed model performs a computationally efficient transient thermo-mechanical coupled field milling simulation, predicts the workpiece non-linear behavior during machining due to its changing geometry, allows the modelling of the effects of initial residual stresses on part deformations and integrated analytical machining load -cutting force and shear plane temperature. A model of deformations of thin-walled structures in the milling process and prediction of machining errors are investigated in [20,21]. In [20], a new model is proposed to predict surface form errors caused by milling force during the five-axis flank milling process.…”

mentioning

confidence: 99%

“…A model of deformations of thin-walled structures in the milling process and prediction of machining errors are investigated in [20,21]. In [20], a new model is proposed to predict surface form errors caused by milling force during the five-axis flank milling process. The results showed that the surface deformation error of thin-walled structures is affected together by the instantaneous milling force and the local stiffness of the workpiece.…”

mentioning

confidence: 99%

Multi-Criteria Selection of the Optimal Parameters for High-Speed Machining of Aluminum Alloy Al7075 Thin-Walled Parts

Lukić

Čep

Vukman

et al. 2020

Metals

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Thin-walled parts made of aluminum alloy are mostly used as structural elements in the aerospace, automobile, and military industries due to good homogeneity, corrosion resistance, and the excellent ratio between mechanical properties and mass. Manufacturing of these parts is mainly performed by removing a large volume of material, so it is necessary to choose quality machining parameters that will achieve high productivity and satisfactory quality and accuracy of machining. Using the Taguchi methodology, an experimental plan is created and realized. Based on its results and comparative analysis of multi-criteria decision making (MCDM) methods, optimal levels of machining parameters in high-speed milling of thin-walled parts made of aluminum alloy Al7075 are selected. The varying input parameters are wall thickness, cutting parameters, and tool path strategies. The output parameters are productivity, surface quality, dimensional accuracy, the accuracy of forms and surface position, representing the optimization criteria. Selection of the optimal machining parameter levels and their ranking is realized using 14 MCDM methods. Afterward, the obtained results are compared using correlation analysis. At the output, integrative decisions were made on selecting the optimal level and rank of alternative levels of machining parameters.

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Rapid prediction and compensation method of cutting force-induced error for thin-walled workpiece

Yang

2020

Int J Adv Manuf Technol

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Machining deformation prediction of thin-walled workpieces in five-axis flank milling

Cited by 47 publications

References 29 publications

Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

Thin-Wall Machining of Light Alloys: A Review of Models and Industrial Approaches

Multi-Criteria Selection of the Optimal Parameters for High-Speed Machining of Aluminum Alloy Al7075 Thin-Walled Parts

Rapid prediction and compensation method of cutting force-induced error for thin-walled workpiece

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