Abstract
Product variation reduction is critical to improve process efficiency and product quality, especially for multistage machining process (MMP). However, due to the variation accumulation and propagation, it becomes quite difficult to predict and reduce product variation for MMP. While the method of statistical process control can be used to control product quality, it is used mainly to monitor the process change rather than to analyze the cause of product variation. In this paper, based on a differential description of the contact kinematics of locators and part surfaces, and the geometric constraints equation defined by the locating scheme, an improved analytical variation propagation model for MMP is presented. In which the influence of both locator position and machining error on part quality is considered while, in traditional model, it usually focuses on datum error and fixture error. Coordinate transformation theory is used to reflect the generation and transmission laws of error in the establishment of the model. The concept of deviation matrix is heavily applied to establish an explicit mapping between the geometric deviation of part and the process error sources. In each machining stage, the part deviation is formulized as three separated components corresponding to three different kinds of error sources, which can be further applied to fault identification and design optimization for complicated machining process. An example part for MMP is given out to validate the effectiveness of the methodology. The experiment results show that the model prediction and the actual measurement match well. This paper provides a method to predict part deviation under the influence of fixture error, datum error and machining error, and it enriches the way of quality prediction for MMP.
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References
ZHOU Shiyu, CHEN Yong, SHI Jianju. Statistical estimation and testing for variation root-cause identification of multistage manufacturing processes[J]. Automation Science and Engineering, IEEE Transactions on, 2004, 1(1): 73–83.
WANG Hui, KATZ R, HUANG Qiang. Multi-operational machining processes modeling for sequential root cause identification and measurement reduction[J]. Journal of manufacturing science and engineering, 2005, 127(3): 512–521.
CAMELIO J, HUS Jack, ZHONG Weiping. Diagnosis of multiple fixture faults in machining processes using designated component analysis[J]. Journal of manufacturing systems, 2004, 23(4): 309–315.
RONG Y, HU Wei. Locating error analysis and tolerance assignment for computer-aided fixture design[J]. International Journal of Production Research, 2001, 39(15): 3529–3545.
ABELLAN-NEBOT J V, LIU Jian, SUBIRON F R. Process-oriented tolerancing using the extended stream of variation model[J]. Computers in Industry, 2013, 64(5): 485–498.
JIAO Yibo, DJURDJANOVIC D. Compensability of errors in product quality in multistage manufacturing processes[J]. Journal of Manufacturing Systems, 2011, 30(4): 204–213.
XIANG Liming, TSUNG F. Statistical monitoring of multi-stage processes based on engineering models[J]. IIE Transactions, 2008, 40(10): 957–970.
LIU Jian. Variation reduction for multistage manufacturing processes: a comparison survey of statistical process control vs stream of variation methodologies[J]. Quality and Reliability Engineering International, 2010, 26(7): 645–661.
WAN Xinjin, XIONG Caihua, ZHAO Can, et al. A unified framework of error evaluation and adjustment in machining[J]. International Journal of Machine Tools and Manufacture, 2008, 48(11): 1198–1210.
ZHANG Min, DJURDJANOVIC D, JUN Ni Diagnosibility and sensitivity analysis for multi-station machining processes[J]. International Journal of Machine Tools and Manufacture, 2007, 47(3): 646–657.
LIU Qinyan, DING Yu, CHEN Yong. Optimal coordinate sensor placements for estimating mean and variance components of variation sources[J]. IIE Transactions, 2005, 37(9): 877–889.
MANDROLI S S, SHRIVASTAVA A K, DING Yu. A survey of inspection strategy and sensor distribution studies in discrete-part manufacturing processes[J]. IIE Transactions, 2006, 38(4): 309–328.
HU S Jack, KOREN Yoram. Stream-of-variation theory for automotive body assembly[J]. CIRP Annals-Manufacturing Technology, 1997, 46(1): 1–6.
LAWLESS J F, MACKAY R J, ROBINSONJ A. Analysis of variation transmission in manufacturing processes-part I[J]. Journal of Quality Technology, 1999, 31(2):131–142.
JIN Jionghua, SHI Jianjun. State space modeling of sheet metal assembly for dimensional control[J]. Journal of Manufacturing Science and Engineering, 1999, 121(4): 756–762.
HUANG Qiang, SHI Jianjun, YUAN Jingxia. Part dimensional error and its propagation modeling in multi-operational machining processes[J]. Journal of manufacturing science and engineering, 2003, 125(2): 255–262.
HUANG Qiang ZHOU Nairong, SHI Jianjun. Stream of variation modeling and diagnosis of multi-station machining processes[J]. Ann Arbor, 2000, 1001: 48109–2117.
ZHOU Shiyu, HUANG Qiang, SHI Jianjun. State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors[J]. Robotics and Automation, IEEE Transactions on, 2003, 19(2): 296–309.
DJURDJANOVIC D, NI Jun. Linear state space modeling of dimensional machining errors[J]. Transactions-North American Manufacturing research institution of SME, 2001: 541–548.
HUANG Qiang, SHI Jianjun. Variation transmission analysis and diagnosis of multi-operational machining processes[J]. IIE Transactions, 2004, 36(9): 807–815.
HUANG Qiang, ZHOU Shiyu, SHI Jianjun. Diagnosis of multi-operational machining processes through variation propagation analysis[J]. Robotics and Computer-Integrated Manufacturing, 2002, 18(3): 233–239.
LOOSEJ P, ZHOU Shiyu, CEGLAREK D. Kinematic analysis of dimensional variation propagation for multistage machining processes with general fixture layouts[J]. Automation Science and Engineering, IEEE Transactions on, 2007, 4(2): 141–152.
DJURDJANOVIC D, NI Jun. Dimensional errors of fixtures, locating and measurement datum features in the stream of variation modeling in machining[J]. Journal of Manufacturing Science and Engineering, 2003, 125(4): 716–730.
ABELLAN-NEBOTJ V, LIU Jian, SUBIRONF R. Design of multi-station manufacturing processes by integrating the stream-of-variation model and shop-floor data[J]. Journal of Manufacturing Systems, 2011, 30(2): 70–82.
ABELLAN-NEBOTJ V, LIU Jian, SUBIRONF R, et al. Limitations of the current state space modelling approach in multistage machining processes due to operation variations[C]//AIP Conference Proceedings, 2009, 1181(1): 231.
ABELLAN-NEBOTJ V, LIU Jian, SUBIRONF R, et al. State space modeling of variation propagation in multistage machining processes considering operation-induced variation[J]. Journal of Manufacturing Science and Engineering: Transactions of the ASME, under revision, 2012, 134(2):1–13.
ZHANG Faping, LIU Jian, TANG Shuiyuan, et al. Locating error considering dimensional errors modeling for multistation manufacturing system[J]. Chinese Journal of Mechanical Engineering, 2010, 23(6): 765–743.
QIN Guohua, ZHANG Weihong, WAN Min. A machining dimension based approach to locating scheme design[J]. Journal of Manufacturing Science and Engineering, 2008, 130(5): 051010.
XIONG Caihua, RONG Yimin, TANG Yong, et al. Fixturing model and analysis[J]. International journal of computer applications in technology, 2007, 28(1): 34–45.
RONG Yiming, HU Wei. Locating error analysis and tolerance assignment for computer-aided fixture design[J]. International Journal of Production Research, 2001, 39(150): 3529–3545.
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Supported by National Natural Science Foundation of China (Grant Nos. 51205286, 51275348)
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ZHU Limin, born in 1988, is currently a graduate student, majoring in computational mathematics at School of Science, Tianjin University, China. She is now participating in projects at Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, China.
HE Gaiyun, born in 1965, is currently a professor at Tianjin University, China. She received her PhD degree from Tianjin University, China, in 2006. Her research interests include modern manufacturing quality control, evaluation methods of geometric errors and CAD/CAM/CAI integration technology.
SONG Zhanjie, received his PhD degree in probability theory and mathematical statistics, from School of Mathematical Science, Nankai University, China, in 2006. He is currently a Professor at School of Science and a Fellow at Liuhui Center for Applied Mathematics, Tianjin University, China. His current research interests are in approximation of deterministic signals, reconstruction of random signals and statistical analysis of random processes.
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Zhu, L., He, G. & Song, Z. Improved quality prediction model for multistage machining process based on geometric constraint equation. Chin. J. Mech. Eng. 29, 430–438 (2016). https://doi.org/10.3901/CJME.2016.0106.003
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DOI: https://doi.org/10.3901/CJME.2016.0106.003