Deviation diagnosis and analysis of hull flat block assembly based on a state space model

Abstract

Dimensional control is one of the most important challenges in the shipbuilding industry. In order to predict assembly dimensional variation in hull flat block construction, a variation stream model based on state space was presented in this paper which can be further applied to accuracy control in shipbuilding. Part accumulative error, locating error, and welding deformation were taken into consideration in this model, and variation propagation mechanisms and the accumulative rule in the assembly process were analyzed. Then, a model was developed to describe the variation propagation throughout the assembly process. Finally, an example of flat block construction from an actual shipyard was given. The result shows that this method is effective and useful.

Keywords

hull flat block / state space model / deviation source diagnosis

References

[1] Ceglarek D., Shi J. Fixture failure diagnosis for auto body assembly using patter recognition. ASME J. Eng. Ind., 1996, 118: 55-65

[2] Ding Y., Ceglarek D., Shi J.J. Fault diagnosis of multi-station manufacturing processes by using state space approach. ASME Journal of Manufacturing Science and Engineering, 2002, 124(2): 313-322

[3] Ding Y., Ceglarek D., Shi J.J. Design evaluation of multi-station assembly processes by using state space approach. Journal of Mechanical Design, 2002, 124(3): 408-417

[4] Huang Q, Zhou N, Shi J (2000). Stream of variation modeling and diagnosis of multi-station machining processes. Proceedings of the 2000 ASME International Mechanical Engineering Congress and Exposition, Orlando, 81–88.

[5] Huang Q., Zhou S., Shi J. Diagnosis of multi-operational machining processes through process analysis. Robotics and Computer Integrated Manufacturing, 2002, 18: 233-239

[6] Huang Q., Shi J. Variation transmission analysis and diagnosis of multi-operation machining processes. IEEE Transactions on Quality and Reliability, 2004, 36: 807-815

[7] Huang Qiang, Zhou Nairong, Shi Jianjun (2000). Stream of variation modeling and diagnosis of multi-station machining processes. International Mechanical Engineering Congress & Exposition, Orlando, Florida, 5–10.

[8] Hu S.J. Stream-of-variation theory for automotive body assembly. Annals of CIRP, 1997, 46(1): 1-6

[9] Hu S.J., Wu S.W. Identifying root cause of variation in automobile body assembly using principal component analysis. Transactions of NAMRI, 1992, 20: 311-316

[10] Jin J., Guo H. ANOVA method for variation component decomposition and diagnosis in batch manufacturing processes. The International Journal of Flexible Manufacturing Systems, 2003, 15(2): 167-186

[11] Johnson GW, Laskey SE, Robson S, Shortis MR (2004). Dimensional & accuracy control automation in shipbuilding fabrication: an integration of advanced image interpretation, analysis and visualization techniques. ISPRS Congress, Istanbul, Turkey, Commission V, WG V/1.

[12] Lightfoot MP, Bruce GJ, Barber DM (2007). The measurement of welding distortion in shipbuilding using close range photogrammetry. 2007 Annual Conference of the Remote Sensing and Photogrammery, Newcastle, UK.

[13] Mortell R.R., Ruger G.C. Statistical process control of multiple stream process. Journal of Quality Technology, 1995, 27(1): l-12

[14] Zhang P., Ye H. On the relationship between parity space and H2 approaches to fault detection. System & Control Letters, 2006, 55(2): 94-100

[15] Zhou S., Ding Y., Chen Y., Shi J. Diagnosability study of multistage manufacturing processes based on linear mixed-effects models. Technomatrics, 2003, 45(4): 312-325