Adaptive sampling method for thin-walled parts based on on-machine measurement

Wei, Long; Wang, Aimin; Xing, Wenhao; Wang, Kang

doi:10.1007/s00170-022-09962-y

Cited by 5 publications

(5 citation statements)

References 24 publications

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“…It should be noted that improper OMM schemes can introduce significant errors, especially in the arrangement of sampling points, because the accuracy of the substitute geometric model reconstructed using the measurement results is positively related to the final compensation accuracy. Therefore, it is necessary to optimize the sampling scheme and evaluate its reconstruction accuracy before conducting compensation machining based on OMM [ 24 ]. Sampling optimization methods have already been discussed in our prior works [ 24 ]; therefore, this article does not discuss them in depth.…”

Section: Iterative Optimization Compensation Methods Based On Omm And...mentioning

confidence: 99%

“…Therefore, it is necessary to optimize the sampling scheme and evaluate its reconstruction accuracy before conducting compensation machining based on OMM [ 24 ]. Sampling optimization methods have already been discussed in our prior works [ 24 ]; therefore, this article does not discuss them in depth. This paper focuses on the compensation method of machining deformation.…”

Section: Iterative Optimization Compensation Methods Based On Omm And...mentioning

confidence: 99%

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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: Iterative Optimization Compensation Methods Based On Omm And...mentioning

confidence: 99%

Section: Iterative Optimization Compensation Methods Based On Omm And...mentioning

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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“…To improve the analysis efficiency of the machining deformation of parts, a fast estimation method for the local stiffness of cutter location points based on the machining deformation prediction model was proposed. This is based on a previous study [31]. As shown in Figure 2, in a previous study, we combined the NURBS reconstruction method with finite element simulation results to establish a machining deformation prediction model.…”

Section: Local Stiffness Estimation Model Of Thin-walled Partsmentioning

confidence: 99%

Offline Feed-Rate Scheduling Method for Ti–Al Alloy Blade Finishing Based on a Local Stiffness Estimation Model

Wang

Xing

2023

Metals

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In the aerospace field, Ti–Al alloy thin-walled parts, such as blades, generally undergo a large amount of material removal and have a low processing efficiency. Scheduling the feed rate during machining can significantly improve machining efficiency. However, existing feed-rate scheduling methods rarely consider the influence of machining deformation factors and cannot be applied in the finishing stages of thin-walled parts. This study proposes an offline feed-rate scheduling method based on a local stiffness estimation model that can be used to reduce machining errors and improve efficiency in the finishing stage of thin-walled parts. In the proposed method, a predictive model that can rapidly calculate the local stiffness at each cutter location point and a cutting-force prediction model that considers the effect of cutting angle are established. Based on the above model, an offline feed-rate scheduling method that considers machining deformation error constraints is introduced. Finally, an experiment is performed by taking the finishing of actual blade parts as an example. The experimental results demonstrate that the proposed feed-rate scheduling method can improve the machining efficiency of parts while ensuring machining accuracy. The proposed method can also be conveniently applied to feed-rate scheduling in the finishing stage of other thin-walled parts without being limited by machine tools.

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“…This technology has been developed for several decades and has become mature. The in-situ measurement uses a contact measurement method with a ruby probe [4]. This measurement method has the advantages of high accuracy, intuitive data, and being unaffected by the measurement environment.…”

Section: Introductionmentioning

confidence: 99%

A method for expanding sampling area boundary based on on-machine measurement

Xing,

Wang,

Zhang

et al. 2024

International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2023)

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The technology of adaptive deformation compensation for thin-walled parts, based on on-machine measurement (OMM), has found widespread application in various industries. However, due to the size of the ruby probe, a certain amount of space must be reserved along the workpiece boundary when using the probe to sample the surface of thin-walled parts. This precaution is taken to prevent any collisions or overtravel incidents involving the probe. Consequently, this reservation results in an inability to gather information from the boundary area of thin-walled parts. After undergoing multi-layer milling compensation processing, stepped processing transition areas can develop. In light of the theory of NURBS (Non-Uniform Rational B-Spline) surface reconstruction, this article introduces an algorithm that extends a distance along the tangent direction at the beginning and end of the sampling curve to expand the boundary. A sampling surface was constructed and expanded, and the maximum error γ between the two surfaces was calculated to be -3 0.27 10 mm  . The results showed that it met the accuracy requirements. The verification results confirm that this method can indeed be practically applied in the field of adaptive deformation compensation technology for thin-walled parts.

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Adaptive sampling method for thin-walled parts based on on-machine measurement

Cited by 5 publications

References 24 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

Offline Feed-Rate Scheduling Method for Ti–Al Alloy Blade Finishing Based on a Local Stiffness Estimation Model

A method for expanding sampling area boundary based on on-machine measurement

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