6+X locating principle based on dynamic mass centers of structural parts machined by responsive fixtures

et al. 2022

Applied Sciences

Self Cite

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.

Section: Machining Process Monitoringmentioning

confidence: 99%

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

Guo

et al. 2022

Applied Sciences

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“…e Optimization for Fixture Layout Based on GA. e optimization problem of fixture layout is solved by the genetic algorithm (GA) for the optimal solution. e advantage of employing GA is that such an evolutionary algorithm inspired by the biological reproduction process is robust, stochastic, and heuristic so that it has a high chance to figure out the optimal solution [41]. In this section, the chromosome in GA is defined to represent the design variables, as illustrated in Figure 8.…”

Section: 3mentioning

confidence: 99%

“…Combined with references and sufficient algorithm experiments [20,40,41], sufficient tests have been carried out under different parameter combinations, and the ranges of parameters p m , p c , and G are 0.1-0.3, 0.65-0.85, and 40-100, respectively. Considering the operation efficiency and solution accuracy, the optimal parameter combination is obtained as shown in Table 3.…”

Section: Constructing Genetic Algorithmmentioning

confidence: 99%

Evolutionary Design of Machining Fixture Layout for Thin-Walled Structure

Mathematical Problems in Engineering

Guan

Zhao

et al. 2022

Fixture layout for machining thin-walled structures plays an important role in fixture performance. This work focuses on the optimization design of machining fixture layout including clamp layout and the layout of support heads. A universal optimization methodology that can automatically generate the optimal machining fixture layout is proposed. The optimization of fixture layout is implemented based on the criteria of minimizing maximum deflection of the thin-walled workpiece while remain relatively small torque caused by the clamping tables. An evolutionary algorithm is employed to solve the fixture layout optimization for the optimal solution. The optimal position of the support heads and the layout of clamping tables obtained from this work vary along with the change of cutting force. To validate the proposed optimization methodology, a fixture layout design for a typical thin-walled structure is optimized as an example. The case study demonstrates the application of the proposed approach in the fixture layout design for thin-walled structures, which is beneficial to design an optimal fixture layout.

“…Yoshioka et al (2014) presented a method to monitoring the distance between the workpiece surface and the tool, which provides a means to improve surface quality. In order to overcome the difficulty of workpiece deformation monitoring, the authors' research group developed an adaptive machining method using flexible fixtures, where deformation can be monitored when the workpiece is not restrained by the fixtures during the non-cutting intervals (Li et al 2015;Hao et al 2018).…”

Section: Deformation Monitoring Approachesmentioning

confidence: 99%

On-line part deformation prediction based on deep learning

Zhao

et al. 2019

J Intell Manuf

Self Cite

Deformation prediction is the basis of deformation control in manufacturing process planning. This paper presents an on-line part deformation prediction method using a deep learning model during numerical control machining process, which is different from traditional methods based on finite element simulation of stress release prior to the actual machining process. A fourth-order tensor model is proposed to represent the continuous part geometric information, process information, and monitoring information, which is used as the input to the deep learning model. A deep learning framework with a Conventional Neural Network and a Recurrent Neural Network has been constructed and trained by monitored deformation data and process information associated with interim part geometric information. The proposed method can be generalised for different parts with certain similarities and has the potential to provide a reference for an adaptive machining control strategy for reducing part deformation. The proposed method was validated by actual machining experiments, and the results show that the prediction accuracy has been improved compared with existing methods. Furthermore, this paper shifts the difficult problem of residual stress measurement and off-line deformation prediction to the solution of on-line deformation prediction based on deformation monitoring data.