Fluctuation evaluation and identification model for small-batch multistage machining processes of complex aircraft parts

Abstract

The key to improve the machining quality of workpiece is to decrease the process fluctuation, which requires identifying the fluctuation sources first. For small-batch multistage machining processes of complex aircraft parts, how to identify the fluctuation sources efficiently has become a difficult issue due to the limited shop-floor data and the complicated interactive effects among different stages. Aiming at this issue, a fluctuation evaluation and identification model for smallbatch multistage machining process is proposed based on the sensitivity analysis theory. In order to improve the data utilization, an analytical structure of the fluctuation evaluation and identification model for small-batch multistage machining process is presented, which comprises four levels, namely, part level, multistage level, single-stage level and quality feature level. Corresponding to the four levels in the analytical structure, four fluctuation analysis indices are proposed to quantitatively evaluate the fluctuation level of different parts and identify the weak stages and elements that result in the abnormal fluctuation in the process flow. A five-stage deep-hole machining process of aircraft landing gear is used as a case to verify the proposed model.

An Enhanced DMAIC Method for Feature-Driven Continuous Quality Improvement for Multi-Stage Machining Processes in One-of-a-Kind and Small-Batch Production

“…After modelling the MEPN, metric analyses for EPN should be done [42]. Figure 5 shows the logic flow of the metrics analysis method.…”

An extended machining error propagation network model for small-batch machining process control of aircraft landing gear parts

“…As detailed in Table 7, the process data (including the kinematic error and vibration of machining tool, the vibration and temperature of cutting-tool, and the radial run-out error of fixture) were collected by various sensors and devices for evaluating the running state of each ME. 31 To guarantee the data validity, this article adopts mature methods of acquisition of working condition data from literature. 32,42,43 Besides, the in-process quality data are acquired by the Binocular Vision System.…”

“…By systematically analyzing of the effect of MEs on machining quality, the machininginduced variations including kinematic error, thermal deformation, vibration, forced deformation, and run-out error are considered to mining the information on the dynamic running state of the machining system. [29][30][31] In actual machining process, the dynamic running state is ever changing with time. It is usually monitored by various sensors and then transformed into meaningful feature information that could describe the machining status adequately.…”

“…The systematical description of common SEs related to MEs is given in Table 3. 31 Weight of edges. Based on the complex network theory, the graph of EMEPN can be completely described by giving the adjacency matrix, a N Â N square matrix whose entry a ij (i, j ¼ 1,. .…”

Key Process and Quality Characteristic Identification for Manufacturing Systems using Dynamic Weighting Function and D-S Evidence Theory

“…Therefore, the identification and diagnosis of bottle-neck processing stages play an important role in these research works. For small-batch multistage machining processes of complex aircraft parts, Wang and Jiang [17] proposed an analytical structure of the fluctuation evaluation and identification model for small-batch multistage machining process, which comprises four levels, namely, part level, multistage level, single-stage level and quality feature level. Corresponding to the four levels in the analytical structure, four fluctuation analysis indices are proposed to quantitatively evaluate the fluctuation level of different parts and identify the weak stages and elements that result in the abnormal fluctuation in the process flow.…”