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 small-batch 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.
TsungFLiYJinM. Statistical process control for multistage manufacturing and service operations: a review and some extensions. Int J Serv Oper Inform2008; 3(2): 191–204.
2.
ZhangG. A new diagnosis theory with two kinds of quality. Total Qual Manage2006; 1: 249–258.
3.
JohanssonPChakhunashviliABaroneSet al. Variation mode and effect analysis: a practical tool for quality improvement. Qual Reliab Eng2006; 22(8): 865–876.
4.
ShiJ. Stream of variation modeling and analysis for multistage manufacturing processes. Technometrics2009; 4: 479–480.
5.
LawlessJFMackayRJRobinsonJA. Analysis of variation transmission in manufacturing processes–part I. J Qual Technol1999; 31(2): 131–142.
6.
ShiJZhouS. Quality control and improvement for multistage systems: a survey. IIE Trans2009; 41(9): 744–753.
7.
MccullochCESearleSR. Generalized, linear, and mixed models. New York: John Wiley & Sons (Wiley-Interscience), 2008.
8.
ApleyDWSeligerGVoitLet al. Diagnostics in disassembly unscrewing operations. Int J Flex Manuf Syst1998; 10(2): 111–128.
9.
ZhouSChenYShiJ. Statistical estimation and testing for variation root-cause identification of multistage manufacturing processes. IEEE T Autom Sci Eng2004; 1(1): 73–83.
10.
JinNZhouS. Data-driven variation source identification for manufacturing process using the eigenspace comparison method. Nav Res Log2006; 53(5): 383–396.
11.
ZhangMDjurdjanovicDNiJ. Diagnosibility and sensitivity analysis for multi-station machining processes. Int J Mach Tool Manu2007; 47(3–4): 646–657.
12.
DavoodiMNiakiSTATorkamaniEA. A maximum likelihood approach to estimate the change point of multistage Poisson count processes. Int J Adv Manuf Tech2015; 77(5–8): 1443–1464.
13.
CeglarekDShiJ. Fixture failure diagnosis for autobody assembly using pattern recognition. J Manuf Sci Eng1996; 118(1): 55–66.
14.
ShiJLJ. Knowledge discovery from observational data for process control using causal Bayesian networks. IIE Trans2007; 39(6): 681–690.
15.
LiZZhouSDingY. Pattern matching for variation-source identification in manufacturing processes in the presence of unstructured noise. IIE Trans2007; 39(3): 251–263.
16.
CeglarekDPrakashP. Enhanced piecewise least squares approach for diagnosis of ill-conditioned multistation assembly with compliant parts. Proc IMechE, Part B: J Engineering Manufacture2012; 226(3): 485–502.
17.
AlaeddiniADoganI. Using Bayesian networks for root cause analysis in statistical process control. Expert Syst Appl2011; 38(9): 11230–11243.
18.
DuSLvJXiL. A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge. J Intell Manuf2012; 23(5): 1833–1847.
19.
MiaoRZhangXLiSet al. Part dimension deviation control in batch manufacturing process for monitoring error sources. Int J Prod Res2013; 51(8): 2518–2526.
20.
ZhangLWangHLiSet al. Variation propagation modeling and pattern mapping method for aircraft assembly structure considering residual stress from manufacturing process. Proc IMechE, Part B: J Engineering Manufacture. Epub ahead of print 8 July 2015. DOI: 10.1177/0954405415592197.
21.
WeeYYCheahWPTanSCet al. A method for root cause analysis with a Bayesian belief network and fuzzy cognitive map. Expert Syst Appl2015; 42(1): 468–487.
22.
LiuYJinS. Application of Bayesian networks for diagnostics in the assembly process by considering small measurement data sets. Int J Adv Manuf Tech2013; 65(9–12): 1229–1237.
23.
ZhouXJiangP. Variation source identification for deep hole boring process of cutting-hard workpiece based on multi-source information fusion using evidence theory. J Intell Manuf. Epub ahead of print 9 October 2014. DOI: 10.1007/s10845-014-0975-7.
24.
LoganathanMGandhiMSGandhiO. Functional cause analysis of complex manufacturing systems using structure. Proc IMechE, Part B: J Engineering Manufacture2015; 229(3): 533–545.
25.
LiuDJiangP. Modeling of machining error propagation network for multistage machining processes. Intell Robot Appl2008; 5315: 408–418.
26.
JiangPWangYWangHet al. Quality prediction of multistage machining processes based on assigned error propagation network. Chin J Mech Eng2013; 49(6): 160–170.
27.
LiuDJiangP. The complexity analysis of a machining error propagation network and its application. Proc IMechE, Part B: J Engineering Manufacture2009; 223(6): 623–640.
28.
DebK. Genetic algorithm in search and optimization: the technique and applications. In: Proceedings of international workshop on soft computing & intelligent systems, Calcutta, India, 1998, pp.58–87.
29.
ZhangDLPanQZhangHC. Modeling and simulation on radiator threatening evaluation based on degrees keeping close to ideal point. J Syst Simulat2008; 20(14): 3896–3898.
30.
SunCHeZCaoHet al. A non-probabilistic metric derived from condition information for operational reliability assessment of aero-engines. IEEE T Reliab2015; 64(1): 167–181.
31.
ZhaoFMeiXDuZet al. Online evaluation method of machining precision based on built in signal testing technology. CIRP Conf Manuf Syst2012; 3(7): 144–148.
32.
Abellan-NebotJVRomero SubirónF. A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Tech2010; 47(1–4): 237–257.
