“…A five-stage deep-hole machining process of aircraft landing gear was studied and the vulnerable spots were identified using variation evaluation. 4,5,18,19,39 The drawing of the outer barrel workpiece is shown in Figure 10. Figure 10.The drawing of the outer barrel workpiece.…”
Quality controlling and process stability evaluating for one-of-a-kind production are the key aspects in improving the major technical equipment production capability. However, it is difficult to find the relationships between upstream machining errors and the machining process conditions and the machining quality due to the samples’ insufficiency. A novel sensitivity analysis-based process stability evaluation for one-of-a-kind production is proposed to solve this problem. First, a variation evaluation model for one-of-a-kind production is established. Second, this model will be analyzed, and the safety and real feasible spaces are described with performance distribution analyses. Finally, the process stability index for one-of-a-kind production is calculated by comparing the relationship between the two spaces. Also, a case validates the effectiveness of the proposed method. It is expected that this paper would contribute to the research of innovative QC methods and process stability evaluation of one-of-a-kind production.
“…A five-stage deep-hole machining process of aircraft landing gear was studied and the vulnerable spots were identified using variation evaluation. 4,5,18,19,39 The drawing of the outer barrel workpiece is shown in Figure 10. Figure 10.The drawing of the outer barrel workpiece.…”
Quality controlling and process stability evaluating for one-of-a-kind production are the key aspects in improving the major technical equipment production capability. However, it is difficult to find the relationships between upstream machining errors and the machining process conditions and the machining quality due to the samples’ insufficiency. A novel sensitivity analysis-based process stability evaluation for one-of-a-kind production is proposed to solve this problem. First, a variation evaluation model for one-of-a-kind production is established. Second, this model will be analyzed, and the safety and real feasible spaces are described with performance distribution analyses. Finally, the process stability index for one-of-a-kind production is calculated by comparing the relationship between the two spaces. Also, a case validates the effectiveness of the proposed method. It is expected that this paper would contribute to the research of innovative QC methods and process stability evaluation of one-of-a-kind production.
“…Liu and Jin 22 proposed a new Bayesian networks modeling approach under the condition of small data sets. Zhou and Jiang 23 proposed a systematic variation source identification method for deep-hole boring process based on multi-source information fusion using Dempster–Shafer (D-S) evidence theory. Loganathan et al 24 presented a function event digraph model for functional cause analysis of complex manufacturing system by considering its input and output functions and their interrelations.…”
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.
“…Dai et al [4] reported that the dynamic rigidity was increased by 30% and chatter was significantly suppressed by using a high rigidity and damping composite boring bar. Zhou and Jiang [5] proposed an identification method of the variation source during the deep-hole machining process based on the Dempster-Shafter theory, which notably improved the boring accuracy. Zhang et al [6] designed and analyzed a tapered carbon composite boring bar to match the need for high-speed cutting, and their boring bar model based on the Adomian modified decomposition method (AMDM) provided guidelines to improve the surface finish.…”
Deep-holes are typical super-dimensioned parts of aircraft structures, with associated machining technology which is recognized as challenging due to the difficult-to-machine material, the narrow and enclosed machining environment, and the weak rigidity and large deformation of the boring bar. Axial ultrasonic vibration-assisted cutting has been proved to greatly enhance machining performance, especially in the precision machining of aviation alloy. This paper focuses on the machining of a super-dimensioned titanium alloy Ti6Al4V aviation deep-hole part (aspect ratio exceeding 20) with the axial ultrasonic vibration-assisted boring (AUVB) method. First, the kinetics of the AUVB process is analyzed and a retrospective of its separation cutting feature is provided. Subsequently, a multi-step cantilever beam model of the boring bar is established to analyze its static rigidity and dynamic stability. The aperture error of bored hole is deduced, and it is found to be mainly determined by the diameter of the basic hole, the cutting depth, the boring force and the length, diameter, elastic modulus, and rotational inertia of each step. Size coefficient, rather than aspect ratio, is then put forward to represent the static rigidity of the boring bar, which is proportional to the third power of the diameter and inversely proportional to the fourth power of the diameter. In addition, two different vibration cases, namely modal-coupling vibration and regenerative vibration are considered for dynamic stability analysis. The actual dynamic rigidity of AUVB in both of these cases is larger than that of conventional boring (CB), resulting from the lower actual boing force and the larger actual cutting depth. Next, the morphology of bored surface is analyzed, and the geometric height of peaks formed by AUVB and CB are calculated. Phase shift φ= π is suggested for obtaining a better surface in AUVB. Finally, the feasibility of AUVB on machining a super-dimensioned titanium alloy Ti6Al4V aviation deep-hole part with three different sizes of boring bar (size coefficients are 7473, 4076 and 2718, respectively) is verified through systematic experiments, and compared with CB. Results demonstrate that AUVB has obvious advantages in reducing the boring force, improving boring accuracy, suppressing vibration and promoting surface quality. Furthermore, the aperture error decreases to 50% and vibration amplitudes decrease to only 20%-25%. The overall surface roughness of the deep-hole part stays below Ra=0.8μm with rotational speeds of 60r/min and 80r/min, and the surface residual stress state is transferred from the tensile state to a compressive one. As a result, not only AUVB can provide better boring accuracy and surface finish, but it also can enhance the surface fatigue properties.
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