Abstract
The dimensional precision of casting is threatened by the deformation of the corresponding ceramic shell in the investment casting (IC) processes. Therefore, the monitoring and root cause diagnosis for the deformation of ceramic shell play an important role in the continuous dimensional quality improvement of the final casting. However, under the condition of small batch production, the data set for root cause diagnosis is small and incomplete, which makes the traditional statistical methods difficult to perform process effectively. This paper applies the approach of systematic Bayesian to solve this problem. Firstly, it is advised to gain the initial structure and parameters through the Bayesian network (BN) mapping method based on process expert knowledge representation. Then, a mutual information approach is proposed to update the structure of the BN. With more accessible measurement date, the conditional probability is updated. Finally, the root causes can be identified with the monitoring signals as evidence. The industrial computerized tomography (CT) method is also creatively advised to measure the inner dimensions of shell. The proposed BN approach and the industrial CT measurement method have a great significance to the dimensional precision improvement for the casting in the IC process.
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Abdul-Karem W, Green N, Al-Raheem KF, Hasan AH (2013) Effect of vibration after filling on mechanical reliability in thin wall investment casting with fillability filling regime—part 1. Int J Adv Manuf Technol 67(9–12):2075–2082
Yao W, Leu MC (1999) Analysis of shell cracking in investment casting with laser stereolithography patterns. Rapid Prototyp J 5(1):12–20
Arzt A (1987) Optimizing control of shell cracking in investment casting. Mod Cast 77(2):30–33
Capadona J (1991) Slurry process control in production can ‘crack down’ on shell cracking. Incast 4(4):10–12
Guerra M, Schiefelbein G (1994) Review of shell components shell characteristics and properties refractory selection for primary shell coat. In: Investment Casting Institute 42nd Annual Meeting 1994.
Liu C, Jin S, Lai X, Wang Y (2015) Dimensional variation stream modeling of investment casting process based on state space method. Proc Inst Mech Eng B J Eng Manuf 229(3):463–474
Jones S, Jolly M, Lewis K (2002) Development of techniques for predicting ceramic shell properties for investment casting. Br Ceram Trans 101(3):106–113
Jones S, Yuan C (2003) Advances in shell moulding for investment casting. J Mater Process Technol 135(2):258–265
Mishra S, Ranjana R (2010) Reverse solidification path methodology for dewaxing ceramic shells in investment casting process. Mater Manuf Process 25(12):1385–1388
Chen X, Li D, Wu H, Tang Y, Zhao L (2011) Analysis of ceramic shell cracking in stereolithography-based rapid casting of turbine blade. Int J Adv Manuf Technol 55(5–8):447–455
Everhart W, Lekakh S, Richards V, Smith J, Li H, Chandrashekhara K, Zhao H, Nam P (2012) Foam pattern aging and its effect on crack formation in investment casting ceramic shells. In: Proceedings of American Foundry Society (AFS) Conference, pp 1–8
Everhart W, Lekakh S, Richards V, Chen J, Li H, Chandrashekhara K (2013) Corner strength of investment casting shells. Int J Met 7(1):21–27
Jiang J, Liu XY (2004) Burning-out process of ceramic moulds. Int J Cast Metals Res 17(2):121–127
Jiang J, Liu XY (2007) Dimensional variations of castings and moulds in the ceramic mould casting process. J Mater Process Technol 189(1):247–255
Liu C, Jin S, Lai X, He B, Li F (2015) Influence of complex structure on the shrinkage of part in investment casting process. Int J Adv Manuf Technol 77(5–8):1191–1203
Morrell R, Quested PN, Jones S, Ford DA (2006) Studio Project: DISIC: Dimensional stability of ceramic casting moulds. National Physical Laboratory, UK
Wereszczak A, Breder K, Ferber M, Kirkland T, Payzant E, Rawn C, Krug E, Larocco C, Pietras R, Karakus M (2002) Dimensional changes and creep of silica core ceramics used in investment casting of superalloys. J Mater Sci 37(19):4235–4245
Canakci A, Ozsahin S, Varol T (2014) Prediction of effect of reinforcement size and volume fraction on the abrasive wear behavior of AA2014/B4Cp MMCs using artificial neural network. Arab J Sci Eng 39(8):6351–6361
Varol T, Canakci A, Ozsahin S (2014) Prediction of the influence of processing parameters on synthesis of Al2024-B4C composite powders in a planetary mill using an artificial neural network. Sci Eng Compos Mater 21(3):411–420
Canakci A, Varol T, Ozsahin S (2015) Artificial neural network to predict the effect of heat treatment, reinforcement size, and volume fraction on AlCuMg alloy matrix composite properties fabricated by stir casting method. Int J Adv Manuf Technol 78(1–4):305–317
Varol T, Canakci A, Ozsahin S (2015) Modeling of the prediction of densification behavior of powder metallurgy Al–Cu–Mg/B4C composites using artificial neural networks. Acta Metall Sin 28(2):182–195
Dey S, Stori J (2005) A Bayesian network approach to root cause diagnosis of process variations. Int J Mach Tools Manuf 45(1):75–91
Lela B, Bajić D, Jozić S (2009) Regression analysis, support vector machines, and Bayesian neural network approaches to modeling surface roughness in face milling. Int J Adv Manuf Technol 42(11–12):1082–1088
Sayed MS, Lohse N (2014) Ontology-driven generation of Bayesian diagnostic models for assembly systems. Int J Adv Manuf Technol 74(5–8):1033–1052
Liu Y, Jin S (2013) Application of Bayesian networks for diagnostics in the assembly process by considering small measurement data sets. Int J Adv Manuf Technol 65(9–12):1229–1237
Liu Y, Ye X, Ji F, Zheng S, Jin S (2015) Dynamic maintenance plan optimization of fixture components for a multistation autobody assembly process. Int J Adv Manuf Technol:1–12.
Jung M, Jun H-B, Kim K-W, Suh H-W (2010) Ontology mapping-based search with multidimensional similarity and Bayesian network. Int J Adv Manuf Technol 48(1–4):367–382
Sulun I (2008) Industrial computed tomography. Quality 47(4):S8
Canakci A, Erdemir F, Varol T, Patir A (2013) Determining the effect of process parameters on particle size in mechanical milling using the Taguchi method: measurement and analysis. Measurement 46(9):3532–3540
Canakci A, Erdemir F, Varol T, Ozkaya S (2014) Effect of process parameters on the formation of Fe-Al intermetallic coating fabricated by mechanical alloying. Indian J Eng Mater Sci 21:595–600
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Jin, S., Liu, C., Lai, X. et al. Bayesian network approach for ceramic shell deformation fault diagnosis in the investment casting process. Int J Adv Manuf Technol 88, 663–674 (2017). https://doi.org/10.1007/s00170-016-8795-x
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DOI: https://doi.org/10.1007/s00170-016-8795-x