A New Synthetic Triple Sampling X̅ Control Chart

References

[1] P. D. Bourke, Detecting a shift in fraction nonconforming using run-length control charts with 100% inspection, J. Qual. Technol. 23 (1991), no. 3, 225–238. 10.1080/00224065.1991.11979328Search in Google Scholar

[2] P. Castagliola, G. Celano, S. Fichera and G. Nenes, The variable sample size t control chart for monitoring short production runs, Int. J. Adv. Manuf. Technol. 66 (2013), no. 9–12, 1353–1366. 10.1007/s00170-012-4413-8Search in Google Scholar

[3] P. Castagliola, G. Celano and S. Psarakis, Monitoring the coefficient of variation using EWMA charts, J. Qual. Technol. 43 (2011), no. 3, 249–265. 10.1080/00224065.2011.11917861Search in Google Scholar

[4] Z. L. Chong, M. B. C. Khoo and P. Castagliola, Synthetic double sampling np control chart for attributes, Comput. Indust. Eng. 75 (2014), 157–169. 10.1016/j.cie.2014.06.016Search in Google Scholar

[5] R. Croasdale, Control charts for a double-sampling scheme based on average production run lengths, Int. J. Prod. Res. 12 (1974), no. 5, 585–592. 10.1080/00207547408919577Search in Google Scholar

[6] J. J. Daudin, Double sampling X ¯ charts, J. Qual. Technol. 24 (1992), no. 2, 78–87. 10.1080/00224065.1992.12015231Search in Google Scholar

[7] H. Du Nguyen, K. P. Tran and T. N. Goh, Variable sampling interval control charts for monitoring the ratio of two normal variables, J. Testing Evaluation 48 (2020), no. 3, 2505–2529. 10.1520/JTE20190327Search in Google Scholar

[8] A. Haq and M. B. C. Khoo, A synthetic double sampling control chart for process mean using auxiliary information, Qual. Reliab. Eng. Int. 35 (2019), no. 6, 1803–1825. 10.1002/qre.2477Search in Google Scholar

[9] S. Haridy, N. L. Chong, M. B. C. Khoo, M. Shamsuzzaman and P. Castagliola, Synthetic control chart with curtailment for monitoring shifts in fraction non-conforming, European J. Indust. Eng. 16 (2022), no. 2, 194–214. 10.1504/EJIE.2022.121184Search in Google Scholar

[10] D. He, A. Grigoryan and M. Sigh, Design of double- and triple-sampling X ¯ control charts using genetic algorithms, Int. J. Prod. Res. 40 (2002), no. 6, 1387–1404. 10.1080/00207540110118415Search in Google Scholar

[11] M. B. C. Khoo, H. C. Lee, Z. Wu, C. H. Chen and P. Castagliola, A synthetic double sampling control chart for the process mean, IIE Trans. 43 (2010), no. 1, 23–38. 10.1080/0740817X.2010.491503Search in Google Scholar

[12] N. Kovvali, Theory and Applications of Gaussian Quadrature Methods, Springer, Cham, 2022. Search in Google Scholar

[13] M. R. Maleki, A. Salmasnia and S. Y. Saber, The performance of triple sampling X ¯ control chart with measurement errors, Qual. Technol. Quant. Manag. 19 (2022), no. 5, 587–604. 10.1080/16843703.2022.2040702Search in Google Scholar

[14] E. Mamzeridou and A. C. Rakitzis, Synthetic-type control charts for monitoring general inflated Poisson processes, Comm. Statist. Simulation Comput. 53 (2024), no. 6, 2763–2777. 10.1080/03610918.2022.2088791Search in Google Scholar

[15] F. N. Mim, M. B. C. Khoo, S. Saha and P. Castagliola, Revised triple sampling X ¯ control charts for the mean with known and estimated process parameters, Int. J. Prod. Res. 60 (2022), no. 16, 4911–4935. 10.1080/00207543.2021.1943035Search in Google Scholar

[16] D. C. Montgomery, Introduction to Statistical Quality Control, 7th ed., Wiley, New York, 2012. Search in Google Scholar

[17] P. C. Oprime, N. J. da Costa, C. I. Mozambani and C. L. Gonçalves, X-bar control chart design with asymmetric control limits and triple sampling, Int. J. Adv. Manuf. Technol. 104 (2019), no. 9, 3313–3326. 10.1007/s00170-018-2640-3Search in Google Scholar

[18] H. Sabahno, A. Amiri and P. Castagliola, Optimal performance of the variable sample sizes Hotelling’s T 2 control chart in the presence of measurement errors, Qual. Technol. Quant. Manag. 16 (2019), no. 5, 588–612. 10.1080/16843703.2018.1490474Search in Google Scholar

[19] H. Sabahno, A. Amiri and P. Castagliola, A new adaptive control chart for the simultaneous monitoring of the mean and variability of multivariate normal processes, Comput. Ind. Eng. 151 (2021), Article ID 106524. 10.1016/j.cie.2020.106524Search in Google Scholar

[20] T. Shan, L. Wu and X. Hu, A combined upper-sided synthetic S 2 chart for monitoring the process variance, Math. Probl. Eng. 2020 (2020), 1–6. 10.1155/2020/9102059Search in Google Scholar

[21] S. C. Shongwe, J.-C. Malela-Majika and P. Castagliola, The new synthetic and runs-rules schemes to monitor the process mean of autocorrelated observations with measurement errors, Comm. Statist. Theory Methods 50 (2021), no. 24, 5806–5835. 10.1080/03610926.2020.1737125Search in Google Scholar

[22] Q. Wan, M. Zhu and Y. Liu, Monitoring the process mean using a synthetic X ¯ control chart with two sampling intervals, J. Intell. Fuzzy Syst. 38 (2020), no. 4, 4191–4203. 10.3233/JIFS-190600Search in Google Scholar

[23] Z. Wu and T. A. Spedding, A synthetic control chart for detecting small shifts in the process mean, J. Qual. Technol. 32 (2000), no. 1, 32–38. 10.1080/00224065.2000.11979969Search in Google Scholar

[24] O. G. Xao, A. Abdul-Rahman and S. Abdullah, The in-control performance of the robust multivariate synthetic control charts for monitoring mean shift, J. Tech. Sci. Eng. 87 (2025), no. 1, 1–7. 10.11113/jurnalteknologi.v87.21511Search in Google Scholar

[25] W. C. Yeong, P. Y. Lee, S. L. Lim, K. W. Khaw and M. B. C. Khoo, A side-sensitive synthetic coefficient of variation chart, Qual. Reliab. Eng. Int. 37 (2021), no. 5, 2014–2033. 10.1002/qre.2843Search in Google Scholar