Abstract
Variation source identification for machining process is a key issue in closed-loop quality control, and critical for quality and productivity improvement. Meanwhile, it is a challenging engineering problem, especially for deep hole boring process of cutting-hard workpiece due to its complexity and instability. In this paper, a systematic method of variation source identification for deep hole boring process based on multi-source information fusion using Dempster–Shafter (D–S) evidence theory is proposed. A logic framework for variation source identification is presented to address how this issue can be formulated in the frame of evidence theory, in terms of evidence acquisition, variation source frame of discernment, mass functions and the rules for evidence combination and decision-making. First, run charts are applied to detect the non-random variation of quality measurements of one workpiece, which are acquired at equidistant positions along the axis direction of the hole. And the unnatural run chart patterns are detected by using fuzzy support vector machine and regarded as information cues for variation source identification. Then, the frame of discernment which consists of potential variation source in case of every specific unnatural pattern is constructed. The mass functions that represent the degree of belief supported by the unnatural patterns regarding the possible causes are determined by using judgment matrixes, and treated as pieces of evidences of variation source identification. Afterwards, all of the evidences are combined by using D–S fusion rules. The rules for making reliable diagnostic decisions are also addressed. Finally, a case study is put forward to demonstrate the feasibility and effectiveness of the proposed methodology. The results indicate that the proposed method can resolve the conflicts among the evidences and improve the accuracy of variation source identification for deep hole boring process.
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Alaeddini, A., & Dogan, I. (2011). Using Bayesian networks for root cause analysis in statistical process control. Expert Systems with Applications, 38(9), 11230–11243. doi:10.1016/j.eswa.2011.02.171.
An, W., & Liang, M. (2013). Fuzzy support vector machine based on within-class scatter for classification problems with outliers or noises. Neurocomputing, 110, 101–110. doi:10.1016/j.neucom.2012.11.023.
Apley, D. W., & Shi, J. (2001). A factor-analysis method for diagnosing variability in mulitvariate manufacturing processes. Technometrics, 43(1), 84–95. doi:10.1198/00401700152404354.
Basir, O., & Yuan, X. (2007). Engine fault diagnosis based on multi-sensor information fusion using Dempster–Shafer evidence theory. Information Fusion, 8(4), 379–386. doi:10.1016/j.inffus.2005.07.003.
Demirli, K., & Vijayakumar, S. (2008). Fuzzy assignable cause diagnosis of control chart patterns. In 2008 Annual meeting of the North American of Fuzzy Information Processing Society (NAFIPS 2008) (pp. 1–6), Piscataway: IEEE. doi:10.1109/NAFIPS.2008.4531260.
Deng, C.-S., & Chin, J.-H. (2005). Hole roundness in deep-hole drilling as analysed by Taguchi methods. The International Journal of Advanced Manufacturing Technology, 25(5–6), 420–426. doi:10.1007/s00170-003-1825-5.
Dey, S., & Stori, J. A. (2005). A Bayesian network approach to root cause diagnosis of process variations. International Journal of Machine Tools and Manufacture, 45(1), 75–91. doi:10.1016/j.ijmachtools.2004.06.018.
Ding, Y., Ceglarek, D., & Shi, J. J. (2002a). Fault diagnosis of multistage manufacturing processes by using state space approach. Transactions of the ASME Journal of Manufacturing Science and Engineering, 124(2), 313–322. doi:10.1115/1.1445155.
Ding, Y., Shi, J. J., & Ceglarek, D. (2002b). Diagnosability analysis of multi-station manufacturing processes. Transactions of the ASME Journal of Dynamic Systems Measurement and Control, 124(1), 1–13. doi:10.1115/1.1435645.
Du, S., Huang, D., & Lv, J. (2013). Recognition of concurrent control chart patterns using wavelet transform decomposition and multiclass support vector machines. Computers & Industrial Engineering, 66(4), 683–695. doi:10.1016/j.cie.2013.09.012.
Du, S., & Lv, J. (2013). Minimal Euclidean distance chart based on support vector regression for monitoring mean shifts of auto-correlated processes. International Journal of Production Economics, 141(1), 377–387. doi:10.1016/j.ijpe.2012.09.002.
Du, S., Lv, J., & Xi, L. (2010a). An integrated system for on-line intelligent monitoring and identifying process variability and its application. International Journal of Computer Integrated Manufacturing, 23(6), 529–542. doi:10.1080/09511921003667730.
Du, S., Lv, J., & Xi, L. (2012a). On-line classifying process mean shifts in multivariate control charts based on multiclass support vector machines. International Journal of Production Research, 50(22), 6288–6310. doi:10.1080/00207543.2011.631596.
Du, S., Lv, J., & Xi, L. (2012b). A robust approach for root causes identification in machining processes using hybrid learning algorithm and engineering knowledge. Journal of Intelligent Manufacturing, 23(5), 1833–1847. doi:10.1007/s10845-010-0498-9.
Du, S., & Xi, L. (2011). Fault diagnosis in assembly processes based on engineering-driven rules and PSOSAEN algorithm. Computers and Industrial Engineering, 60(1), 77–88. doi:10.1016/j.cie.2010.10.001.
Du, S., Yu, L. X. J., & Sun, J. (2010b). Online intelligent monitoring and diagnosis of aircraft horizontal stabilizer assemble processes. The International Journal of Advanced Manufacturing Technology, 50(1–4), 377–389. doi:10.1007/s00170-009-2490-0.
Guan, J. W., & Bell, D. A. (1992). Evidence theory and its applications (Vol. 2). Amsterdam: North Holland.
Hassan, A., Shariff Nabi Baksh, M., Shaharoun, A. M., & Jamaluddin, H. (2003). Improved SPC chart pattern recognition using statistical features. International Journal of Production Research, 41(7), 1587–1603. doi:10.1080/0020754021000049844.
Hayajneh, M. T. (2001). Hole quality in deep hole drilling. Materials and Manufacturing Processes, 16(2), 147–164. doi:10.1081/AMP-100104297.
He, S.-G., He, Z., & Wang, G. (2013). Online monitoring and fault identification of mean shifts in bivariate processes using decision tree learning techniques. Journal of Intelligent Manufacturing, 24(1), 25–34. doi:10.1007/s10845-011-0533-5.
Hou, T.-H., Liu, W.-L., & Lin, L. (2003). Intelligent remote monitoring and diagnosis of manufacturing processes using an integrated approach of neural networks and rough sets. Journal of Intelligent Manufacturing, 14(2), 239–253. doi:10.1023/A:1022911715996.
Huang, Q., Zhou, S., & Shi, J. (2002). Diagnosis of multi-operational machining processes through variation propagation analysis. Robotics and Computer-Integrated Manufacturing, 18(3–4), 233–239. doi:10.1016/S0736-5845(02)00014-5.
Huang, Y., McMurran, R., Dhadyalla, G., & Peter Jones, R. (2008). Probability based vehicle fault diagnosis: Bayesian network method. Journal of Intelligent Manufacturing, 19(3), 301–311. doi:10.1007/s10845-008-0083-7.
Jakovljevic, Z., Petrovic, P. B., Mikovic, V. D., & Pajic, M. (2014). Fuzzy inference mechanism for recognition of contact states in intelligent robotic assembly. Journal of Intelligent Manufacturing, 25(3), 571–587. doi:10.1007/s10845-012-0706-x.
Jiang, P., Jia, F., Wang, Y., & Zheng, M. (2014). Real-time quality monitoring and predicting model based on error propagation networks for multistage machining processes. Journal of Intelligent Manufacturing, 25(3), 521–538. doi:10.1007/s10845-012-0703-0.
Jin, N., & Zhou, S. (2006a). Data-driven variation source identification for manufacturing process using the eigenspace comparison method. Naval Research Logistics (NRL), 53(5), 383–396. doi:10.1002/nav.20150.
Jin, N. N., & Zhou, S. Y. (2006b). Signature construction and matching for fault diagnosis in manufacturing processes through fault space analysis. IIE Transactions, 38(4), 341–354. doi:10.1080/07408170500216498.
Kaftandjian, V., Dupuis, O., Babot, D., & Min Zhu, Y. (2003). Uncertainty modelling using Dempster–Shafer theory for improving detection of weld defects. Pattern Recognition Letters, 24(1–3), 547–564. doi:10.1016/S0167-8655(02)00276-3.
Kyung, J. M., Gibaek, L., Dong, S. N., Yeo, H. Y., & En, S. Y. (1997). Robust fault diagnosis based on clustered symptom trees. Control Engineering Practice, 5(2), 199–208. doi:10.1016/S0967-0661(97)00226-8.
Lewis, R. W., & Ransing, R. S. (1997). A semantically constrained Bayesian network for manufacturing diagnosis. International Journal of Production Research, 35(8), 2171–2188. doi:10.1080/002075497194796.
Li, D.-C., Chang, C.-C., Liu, C.-W., & Chen, W.-C. (2013a). A new approach for manufacturing forecast problems with insufficient data: the case of TFT-LCDs. Journal of Intelligent Manufacturing, 24(2), 225–233. doi:10.1007/s10845-011-0577-6.
Li, M., Wang, L., & Wu, M. (2013b). A multi-objective genetic algorithm approach for solving feature addition problem in feature fatigue analysis. Journal of Intelligent Manufacturing, 24(6), 1197–1211. doi:10.1007/s10845-012-0652-7.
Li, Z., & Zhou, S. (2006). Robust method of multiple variation sources identification in manufacturing processes for quality improvement. Transactions of the ASME Journal of Manufacturing Science And Engineering, 128(1), 326. doi:10.1115/1.2117447.
Li, Z., Zhou, S., & Ding, Y. (2007). Pattern matching for root cause identification of manufacturing processes with consideration of general structured noise. IIE Transactions, 39(3), 251–263.
Lim, C., Nam, S. W., & Chang, J.-H. (2013). Fast SVM-based epileptic seizure prediction employing data prefetching. Electronics Letters, 49(1), 13–15. doi:10.1049/el.2012.3414.
Lin, C.-F., & Wang, S.-D. (2002). Fuzzy support vector machines. IEEE Transactions on Neural Networks, 13(2), 464–471.
Loose, J. P., Shiyu, Z., & Ceglarek, D. (2007). Kinematic analysis of dimensional variation propagation for multistage machining processes with general fixture layouts. IEEE Transactions on Automation Science and Engineering, 4(2), 141–152. doi:10.1109/TASE.2006.877393.
Loose, J. P., Zhou, S. Y., & Ceglarek, D. (2008). Variation source identification in manufacturing processes based on relational measurements of key product characteristics. Transactions of the ASME Journal of Manufacturing Science and Engineering, 130(3), 0310071–03100711. doi:10.1115/1.2844591.
Lu, C.-J. (2012). An independent component analysis-based disturbance separation scheme for statistical process monitoring. Journal of Intelligent Manufacturing, 23(3), 561–573. doi:10.1007/s10845-010-0394-3.
Montgomery, D. C. (2007). Introduction to statistical quality control. New York: Wiley.
Parikh, C. R., Pont, M. J., & Barrie Jones, N. (2001). Application of Dempster–Shafer theory in condition monitoring applications: A case study. Pattern Recognition Letters, 22(6–7), 777–785. doi:10.1016/S0167-8655(01)00014-9.
Pham, D. T., & Wani, M. A. (1997). Feature-based control chart pattern recognition. International Journal of Production Research, 35(7), 1875–1890. doi:10.1080/002075497194967.
Ranaee, V., Ebrahimzadeh, A., & Ghaderi, R. (2010). Application of the PSO-SVM model for recognition of control chart patterns. ISA Transactions, 49(4), 577–586. doi:10.1016/j.isatra.2010.06.005.
Rokach, L., & Hutter, D. (2012). Automatic discovery of the root causes for quality drift in high dimensionality manufacturing processes. Journal of Intelligent Manufacturing, 23(5), 1915–1930. doi:10.1007/s10845-011-0517-5.
Rooney, J. J., & Heuvel, L. N. V. (2004). Root cause analysis for beginners. Quality Progress, 37(7), 45–56.
Saaty, T. (1990). How to make a decision. The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. doi:10.1016/0377-2217(90)90057-I.
Shannon, C. E. (2001). A mathematical theory of communication. ACM SIGMOBILE Mobile Computing and Communications Review, 5(1), 3–55.
Tsai, T.-N. (2014). A hybrid intelligent approach for optimizing the fine-pitch copper wire bonding process with multiple quality characteristics in IC assembly. Journal of Intelligent Manufacturing, 25(1), 177–192. doi:10.1007/s10845-012-0685-y.
Wang, C.-H., & Tsai, S.-W. (2014). Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm. Journal of Intelligent Manufacturing, 25(3), 603–616. doi:10.1007/s10845-012-0708-8.
Whitley, D. (1994). A genetic algorithm tutorial. Statistics and Computing, 4(2), 65–85. doi:10.1007/BF00175354.
Widodo, A., & Yang, B.-S. (2007). Support vector machine in machine condition monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 21(6), 2560–2574. doi:10.1016/j.ymssp.2006.12.007.
Wu, Z., Zhang, H., & Liu, J. (2014). A fuzzy support vector machine algorithm for classification based on a novel PIM fuzzy clustering method. Neurocomputing, 125, 119–124. doi:10.1016/j.neucom.2012.07.049.
Yager, R., & Liu, L. (2008). Classic works of the Dempster–Shafer theory of belief functions. Berlin: Springer.
Yang, H.-Y., Wang, X.-Y., Zhang, X.-Y., & Bu, J. (2012). Color texture segmentation based on image pixel classification. Engineering Applications of Artificial Intelligence, 25(8), 1656–1669. doi:10.1016/j.engappai.2012.09.010.
Zeng, L., Jin, N., & Zhou, S. (2008). Multiple fault signature integration and enhancing for variation source identification in manufacturing processes. IIE Transactions, 40(10), 919–930. doi:10.1080/07408170801961404.
Zhang, M., Djurdjanovic, D., & Ni, J. (2007). Diagnosibility and sensitivity analysis for multi-station machining processes. International Journal of Machine Tools and Manufacture, 47(3–4), 646–657. doi:10.1016/j.ijmachtools.2006.04.011.
Zhou, S., Chen, Y., & Shi, J. (2004). Statistical estimation and testing for variation root-cause identification of multistage manufacturing processes. IEEE Transactions on Automation Science and Engineering, 1(1), 73–83. doi:10.1109/TASE.2004.829427.
Zhou, S., Ding, Y., Chen, Y., & Shi, J. (2003a). Diagnosability study of multistage manufacturing processes based on linear mixed-effects models. Technometrics, 45(4), 312–325. doi:10.1198/004017003000000131.
Zhou, S. Y., Huang, Q., & Shi, J. J. (2003b). State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. IEEE Transactions on Robotics and Automation, 19(2), 296–309. doi:10.1109/TRA.2003.808852.
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The research outcome is under the supports of both the national basic research 973 project with Grant No. 2011CB706805. The authors hereby thank the Ministry of Science and Technology (MOST) of China for the financial aids.
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Zhou, X., Jiang, P. Variation source identification for deep hole boring process of cutting-hard workpiece based on multi-source information fusion using evidence theory. J Intell Manuf 28, 255–270 (2017). https://doi.org/10.1007/s10845-014-0975-7
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DOI: https://doi.org/10.1007/s10845-014-0975-7