Modeling of flatness errors in end milling of thin-walled components

Agarwal, Ankit; Desai, K. A.

doi:10.1177/0954405420949214

Cited by 10 publications

(4 citation statements)

References 29 publications

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“…On the basis of the reciprocity theorem, Tang et al [ 3 ] established a theoretical deformation equation model of thin-walled plates under linear loads and predicted the machining deformation of parts. Agarwal et al [ 4 ] presented a flatness error estimation model for end milling of thin-walled planar parts, which estimated the flatness-related parameters on the basis of a combination of a mechanical force model, workpiece deformation model based on finite element analysis (FEA), and an algorithm based on particle swarm optimization.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement

Wu,

Wang,

Wang

et al. 2024

Sensors

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Thin-walled aluminum alloy parts are widely used in the aerospace field because of their favorable characteristics that cater to various applications. However, they are easily deformed during milling, leading to a low pass rate of workpieces. On the basis of on-machine measurement (OMM) and surrogate stiffness models (SSMs), we developed an iterative optimization compensation method in this study to overcome the machining deformation of thin-walled parts. In the error compensation process, the time-varying factors of workpiece stiffness and the impact of prediction model errors were considered. First, we performed machining deformation simulation and information extraction on the key nodes of the machined surface, and an SSM containing the stiffness information of discrete nodes of each cutting layer was established. Subsequently, the machining errors were monitored through intermittent OMM to suppress the adverse impact of prediction model errors. Further, an interlayer correction coefficient was introduced in the compensation process to iteratively correct the prediction model error of each node in the SSM along the depth direction, and a correction coefficient between parts was introduced to realize the iterative correction of the prediction model for the same node position between different parts. Finally, the feasibility of the proposed method was verified through multiple sets of actual machining experiments on thin-walled parts with added pads.

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Section: Literature Reviewmentioning

confidence: 99%

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement

Wu,

Wang,

Wang

et al. 2024

Sensors

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“…Based on the above description of CNC machines need to be flatness testing, machining process the size of the product quality is widely seen from the flatness. Flatness is a major factor for the evaluation of whether or not a machining product is acceptable for production [14][15][16]. Therefore, this paper objective is to analyze a workbench by making a line on the plane with the union jack method by determining the point to be measured for product result of 3 axis CNC router machine.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Workbench Flatness Measurement and Product Result of 3 Axis CNC Router Machine

Mendofa,

Arief

2023

JOMAse

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The development of technology in the industrial sector is increasing rapidly, especially the application of computers in the field of machinery. Demands from consumers who want good quality workpieces, precision, completed in a short time and in large quantities, will be easier to work with a CNC Router machine. Surface flatness is an absolute thing that must be considered in the machining process, especially the 3 Axis CNC Router machine. This type of research is an experimental research, using Dutch teak wood as a workpiece with a height of 15 mm, a length of 100 mm and a width of 80 mm, the machining process uses a 3 Axis CNC Router Machine. The flatness of the workbench is first measured, then the product is prepared with a depth of 2 mm in the product area. The research uses a variation of the spindle speed of 600 rpm, 800 rpm and 1000 rpm. After that, measurements are taken to obtain the flatness value. Measuring flatness using a Dial indicator measuring instrument with accuracy (1µm). The best product flatness test or the lowest value is (32.00 µm) obtained from the spindle speed of 1000 rpm and the most uneven product evenness is (56.00 µm) obtained from the spindle speed of 600 rpm, knowing the effect of the flatness value of the workbench on the product flatness value 3 Axis CNC Router Machine, that the flatter the workbench, the flatter the product results. The results of the research can be used to refine machining strategies to achieve optimal machining.

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“…Li et al [23] proposed a method for predicting surface topography error in flank-mill of TWP, considering the effect of real-time stiffness. Agarwal et al [24] investigated the impact of part stiffness and initial part thickness on flatness error in TWP flank milling. Chen et al [25] studied the axial errors due to tool defection and part stiffness in pocket milling.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-pass machining accuracy prediction method for thin-walled parts

Huang

Wang

et al. 2023

Int J Adv Manuf Technol

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Thin-Walled Parts (TWP) are widely used in aerospace, whose service performance is significantly affected by the machining accuracy. Considering the poor stiffness of TWP, multi-pass machining is used to machine TWP for better surface accuracy. However, it is difficult to accurately predict the final machining accuracy due to the surface topography error's propagation and accumulation in multi-pass machining. Therefore, this paper proposes a multi-pass machining accuracy prediction method for TWP based on dynamic factors (cutting force and stiffness). Firstly, a flexible cutting force prediction model, which considers the axial errors determined by the initial surface topography and part deflection, is proposed. Secondly, a Position-Pass-Dependent Stiffness (PPDS) model is established considering position-dependent of stiffness and multi-pass machining material removal. Finally, combining the two models above, a multi-pass machining accuracy prediction method based on Genetic Algorithm -Back Propagation (GA-BP) neural network is proposed. The experiments under various conditions have been carried out to validate the proposed method. The machining accuracy (flatness as an example) is as high as 90.8% using the method in this paper, while it is only 73.9% when the accumulative error is neglected. The proposed method can significantly improve the performance of machining accuracy prediction by analyzing the error propagation mechanism and the effect of dynamic factors between multi-pass machining. Furthermore, this also provides a theoretical basis for process parameters optimization and machining accuracy improvement in TWP machining.

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Modeling of flatness errors in end milling of thin-walled components

Cited by 10 publications

References 29 publications

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement

Adaptive Optimization Method for Prediction and Compensation of Thin-Walled Parts Machining Deformation Based on On-Machine Measurement

Analysis of Workbench Flatness Measurement and Product Result of 3 Axis CNC Router Machine

A novel multi-pass machining accuracy prediction method for thin-walled parts

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