Finite strip modeling of the varying dynamics of thin-walled pocket structures during machining

Ahmadi, Keivan

doi:10.1007/s00170-016-8931-7

Cited by 23 publications

(12 citation statements)

References 19 publications

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“…The model used by Tian et al [26] is presented as a theoretical base for suppressing resonance in the milling process. Ahmadi [55] compared a Finite Strip Model (FSM), FEM analysis and a semi-analytical model for the study of the dynamics of thin-wall machining (Figure 9). Lin et al [56] studied the FRF of the machining system and related the waviness of the part with the force vibrations and not to the self-exited vibrations.…”

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%

“…Average deflection obtained for ( a ) the first mode, ( b ) the second mode and ( c ) the third mode of a pocket structure [55]. …”

Section: Figurementioning

confidence: 99%

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

et al. 2019

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“…Semianalytical methods are examples of IPW dynamics prediction methods, which are based on the analytical beam or plate theories 25–29 . Meshreki et al 26 presented a model of thin‐walled pocket workpieces using a two‐dimensional (2D) multispan plate method with variable thicknesses for predicting the natural frequencies of the workpieces.…”

Section: Introductionmentioning

confidence: 99%

“…Meshreki et al 26 presented a model of thin‐walled pocket workpieces using a two‐dimensional (2D) multispan plate method with variable thicknesses for predicting the natural frequencies of the workpieces. Although the influences of both material removal and variation of tool position can be taken into account in their model, it is difficult to model the workpieces with complicated geometries 27 . To maintain the computational efficiency of the multispan plate method and enhance the versatility of the semianalytical methods, Ahmadi 27 proposed to model the varying dynamics of thin‐walled pocket structures using the Finite Strip method.…”

Section: Introductionmentioning

confidence: 99%

“…Semianalytical methods are examples of IPW dynamics prediction methods, which are based on the analytical beam or plate theories. [25][26][27][28][29] Meshreki et al 26 presented a model of thin-walled pocket workpieces using a two-dimensional (2D) multispan plate method with variable thicknesses for predicting the natural frequencies of the workpieces. Although the influences of both material removal and variation of tool position can be taken into account in their model, it is difficult to model the workpieces with complicated geometries.…”

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A Gaussian process regression‐based surrogate model of the varying workpiece dynamics for chatter prediction in milling of thin‐walled structures

Yang

Xiao

et al. 2022

Int Journal of Mech Sys Dyn

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Since the dynamics of thin‐walled structures instantaneously varies during the milling process, accurate and efficient prediction of the in‐process workpiece (IPW) dynamics is critical for the prediction of chatter stability of milling of thin‐walled structures. This article presents a surrogate model of the IPW dynamics of thin‐walled structures by combining Gaussian process regression (GPR) with proper orthogonal decomposition (POD) when IPW dynamics at a large number of cutting positions has to be predicted. The GPR method is used to learn the mapping between a set of the known IPW dynamics and the corresponding cutting positions. POD is used to reduce the order of the matrix assembled by the mode shape vectors at different cutting positions, before the GPR model of the IPW mode shape is established. The computation time of the proposed model is mainly composed of the time taken for predicting a known set of IPW dynamics and the time taken for training GPR models. Simulation shows that the proposed model requires less computation time. Moreover, the accuracy of the proposed model is comparable to that of the existing methods. Comparison between the predicted stability lobe diagram and the experimental results shows that IPW dynamics predicted by the proposed model is accurate enough for predicting the stability of milling of thin‐walled structures.

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Automated Upgraded Generalized Full-Discretization Method: Application to the Stability Study of a Thin-Walled Milling Process

Ozoegwu

Eberhard

2020

Mechanical Sciences

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Finite strip modeling of the varying dynamics of thin-walled pocket structures during machining

Cited by 23 publications

References 19 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

A Gaussian process regression‐based surrogate model of the varying workpiece dynamics for chatter prediction in milling of thin‐walled structures

Automated Upgraded Generalized Full-Discretization Method: Application to the Stability Study of a Thin-Walled Milling Process

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