A New Scheme for Monitoring and Diagnosis of Multistage Manufacturing Processes Using Product Quality Measurements



Hossein Davari Ardakani Brian Phillips Behrad Bagheri Jay Lee


The development of robust monitoring systems for assuring the consistency and stability of multistage manufacturing processes necessitates the use of add-on sensors and advanced data collection, storage, and analysis platforms to deal with the high-dimensional data collected from machines and products in multiple stages. In many cases, such an approach may not be feasible due to high implementation costs and the challenges of obtaining the process parameters and analyzing them effectively. This paper proposes an alternative approach for health monitoring and diagnosis of multistage manufacturing processes based on product quality measurements in a sensor-less environment. In the presented work, the available data consists of product quality parameters measured from multiple product types along with the manufacturing route associated with each product. A Gamma distribution is fit to the data for each parameter within a moving time window. Using the distribution fits, a metric is developed to represent the performance of each machine in a stage compared to its peers producing the same product. This metric is then aggregated across all the products produced by the machine to generate the final metric reflecting the overall performance of the machine. This performance metric is first calculated for the machines in the last stage. After flagging the underperforming machines in the last stage, the samples from those machines are removed from the data set and the remaining samples are used to calculate the similar metric for the prior stage. The suggested approach assumes the random distribution of products from one stage to the next to facilitate the implementation of a comparison-based approach. This approach is tested on a data set collected from a manufacturing plant. The results demonstrate the effectiveness of such approach for monitoring and diagnosis of multistage manufacturing processes when the data is not available from within the process.

How to Cite

Davari Ardakani, H. ., Phillips, B., Bagheri, B. ., & Lee, J. . (2015). A New Scheme for Monitoring and Diagnosis of Multistage Manufacturing Processes Using Product Quality Measurements. Annual Conference of the PHM Society, 7(1). https://doi.org/10.36001/phmconf.2015.v7i1.2721
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Smart Manufacturing, Manufacturing Process Monitoring, Process Control

Asadzadeh, S., & Aghaie, A. (2012). Improving the product reliability in multistage manufacturing and service operations. Quality and Reliability Engineering International, 28(4), 397-407.

Ceglarek, D., & Shi, J. (1996). Fixture failure diagnosis for autobody assembly using pattern recognition. Journal of Manufacturing Science and Engineering, 118(1), 55- 66.

Ceglarek, D., Shi, J., & Wu, S. (1994). A knowledge-based diagnostic approach for the launch of the auto-body assembly process. Journal of Manufacturing Science and Engineering, 116(4), 491-499.

Cheng, S., Azarian, M. H., & Pecht, M. G. (2010). Sensor systems for prognostics and health management. Sensors, 10(6), 5774-5797.

Hu, S. J., & Wu, S. (1992). Identifying sources of variation in automobile body assembly using principal component analysis. Transactions of NAMRI/SME, 20, 311-316.

Liu, S. C., & Hu, S. J. (1997). Variation simulation for deformable sheet metal assemblies using finite element methods. Journal of Manufacturing Science and Engineering, 119(3), 368-374.

Lucas, J. M., & Saccucci, M. S. (1990). Exponentially weighted moving average control schemes: Properties and enhancements. Technometrics, 32(1), 1-12.

Montgomery, D. C. (2007). Introduction to statistical quality control John Wiley & Sons.

Neubauer, A. S. (1997). The EWMA control chart: Properties and comparison with other quality-control procedures by computer simulation. Clinical Chemistry, 43(4), 594-601.

Page, E. (1954). Continuous inspection schemes. Biometrika, , 100-115.

Rato, T. J., & Reis, M. S.Statistical process control of multivariate systems with autocorrelation. Computer aided chemical engineering (pp. 497-501) Elsevier. DOI:http://dx.doi.org/10.1016/B978-0-444-53711- 9.50100-0

Reynolds, M. R., Amin, R. W., & Arnold, J. C. (1990). CUSUM charts with variable sampling intervals. Technometrics, 32(4), 371-384.

Reynolds, M. R., Amin, R. W., Arnold, J. C., & Nachlas, J. A. (1988). Charts with variable sampling intervals. Technometrics, 30(2), 181-192.

Roberts, S. (1959). Control chart tests based on geometric moving averages. Technometrics, 1(3), 239-250.

Shi J. (2010). Stream of variation modeling and analysis for multistage manufacturing processes. CRC Press.

Shu, L., & Tsung, F. (2000). Multistage process monitoring and diagnosis. Management of Innovation and Technology, 2000. ICMIT 2000. Proceedings of the 2000 IEEE International Conference On,2 881-886.

Siegel, D., & Lee, J. (2011). An auto-associative residual processing and K-means clustering approach for anemometer health assessment. International Journal of Prognostics and Health Management Volume 2, 117.

Tsung, F., Li, Y., & Jin, M. (2006). Statistical process control for multistage manufacturing and service operations: A review. Service Operations and Logistics, and Informatics, 2006. SOLI'06. IEEE International Conference On, 752-757.

Wolbrecht, E., D'ambrosio, B., Paasch, R., & Kirby, D. (2000). Monitoring and diagnosis of a multistage manufacturing process using Bayesian networks. Ai Edam, 14(01), 53-67.

Zhou, S., Ding, Y., Chen, Y., & Shi, J. (2003). Diagnosability study of multistage manufacturing processes based on linear mixed-effects models. Technometrics, 45(4)

Zhou, S., Huang, Q., & Shi, J. (2003). State space modeling of dimensional variation propagation in multistage machining process using differential motion vectors. Robotics and Automation, IEEE Transactions On, 19(2), 296-309.
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