Large skin parts play an important role in the aerospace industry. The wall thickness of the machined pocket in the skin part needs to be strictly controlled to ensure the transport capacity and structural strength. The wall-thickness accuracy is generally decreased by various factors, such as the shaping error of the workpiece blank, fixing error, machine tool error, and deformation caused by cutting force or internal stress. These factors are usually inevitable and stochastic due to the extremely weak rigidity and easy-to-deflect characteristics of the large skin parts. To ensure the wall-thickness accuracy, a fuzzy v-support vector machine (Fv-SVM)-based wall-thickness error decomposition method is proposed. The wall-thickness errors, which are monitored in the cutting process, are decomposed into spatial-related errors and time-related errors. The Fv-SVM-based decomposition method with the principle of spatial statistical analysis is a data-driven approach for intelligent manufacturing. The data-driven method can consider all factors that affect the wall-thickness accuracy, while the model-driven method usually only considers one factor, such as the workpiece deformation or fixing error. After decomposition, the spatial-related wall-thickness error is offline compensated, and the time-related wall-thickness error is compensated by using a real-time strategy. The novel method can be applied to complex tool paths. The cutting experiment of rectangular pockets in a large skin panel was conducted to verify the effectiveness of the proposed method. The wall-thickness accuracy can be improved to 0.05 mm for the workpiece with only 2 mm thickness.

Fv-SVM-Based Wall-Thickness Error Decomposition for Adaptive Machining of Large Skin Parts

Framework and development of data-driven physics based model with application in dimensional accuracy prediction in pocket milling

Uncertainty Quantification in Machining Deformation Based on Bayesian Network

以大型薄壁构件镜像加工为研究对象,采用分类模块化的设计方法,利用VC6.0软件中的MFC功能开发了上位机软件系统,设计了线激光测量、超声测量、加工管理和状态监测四大功能模块.利用该上位机软件系统进行了相关实验,结果表明该软件系统各模块稳定运行,能够满足大型薄壁构件镜像加工实验平台的功能需求.

Research and Development of Mirror Machining Software System for Large Thin-walled Parts Based on PMAC

The thin-walled parts of the tank are widely used in aerospace filed and the accuracy is the basis requirement for ensuring the usability. Due to the low rigidity of the thin-walled parts, the milling force becomes a major factor for the machining deformation. Based on analysis the relationship between feed speed and the length of the cutting arc, the feed speed optimization method is proposed to reduce the machining deformation. To reduce the machining deformation the optimal machining path is put forward which is base the finite element analysis results on the rigidity distribution law of the parts. At the same time, an efficient compensation method based on laser measurement is proposed. Through machining the aluminium alloy 2219 thin-walled tank parts as an example, experimental results indicate that the effectiveness of the proposed control method in machining process. This research provides an effective approach to reduce the machining deformation for the tank thin-walled parts.

Machining Deformation Control and Compensation Using Whole Mirror Milling Method for Tank Thin-Walled Parts

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Machining Deformation Control and Compensation Using Whole Mirror Milling Method for Tank Thin-Walled Parts