A Strategy to Optimize the SPC Scheme for Mass Production of HDD Arm with ClusteringTechnique and Three-Way Control Chart
Consider a mass production of HDD arms where
hundreds of CNC machines are used to manufacturer the HDD arms.
According to an overwhelming number of machines and models of
arm, construction of separate control chart for monitoring each HDD
arm model by each machine is not feasible. This research proposed a
strategy to optimize the SPC management on shop floor. The
procedure started from identifying the clusters of the machine with
similar manufacturing performance using clustering technique. The
three way control chart ( I - MR - R ) is then applied to each
clustered group of machine. This proposed research has
advantageous to the manufacturer in terms of not only better
performance of the SPC but also the quality management paradigm.
[1] Noble, C. E. (1951) Variations in conventional control charts. Ind. Qual.
Cont. 8, pp. 17-22.
[2] Neuhart, J. B. (1987) Effects of correlated sub-samples in statistical
process control. IIE Trans. 19, pp. 208-214.
[3] Hahn, G. J. and Cochrum, M. B. (1987) Adapting control charts to meet
practical needs: A chemical process application. J. Appl. Statist. 10, pp.
35-52.
[4] Wetherill, G. B. and Brown, D. W. (1991) Statistical Process Control:
Theory and Practice Chapman and Hall, London, UK.
[5] Porter, L. J. and Caulcutt, R. (1992) Control chart design: A review of
statistical practice. Qual. Reliab. Eng. Int. 8 , pp. 113-122.
[6] Woodall, W. H. and Thomas, E. V. (1995) Statistical process control
with several components of common cause variability. IIE Trans. 27, pp.
757-764.
[7] Laubscher, N. (1996) A variance component model for statistical process
control. S. Afr. Statist. J. 30, pp. 27-47.
[8] Khuri, A. I. (2000) Designs for Variance Components Estimation: Past
and Present. International Statistical Review. 68, pp. 311-322.
[9] Yashchin, E. (1995) Likelihood Ratio Methods for Monitoring
Parameters of a Nested Random Effect Model. Journal of the American
Statistical Association. 90, pp. 729-738.
[10] Yashchin, E. (1994) Monitoring Variance Components. Technometric.
36, pp. 379-393.
[11] Wheeler, D. (1995) Advanced Topics in Statistical Process Control SPC
Press, Knoxville, TN.
[12] Wheeler, D. and Chambers, D. (1992) Understanding Statistical Process
Control, 2nd. SPC Press, Knoxville, TN.
[13] Calzada, M. E. and Scariano, Stephen M. (2004) Average Run Length
Computations for the Three-Way Chart. Communications in Statistics -
Simulation and Computation. 33, pp.505 - 524.
[1] Noble, C. E. (1951) Variations in conventional control charts. Ind. Qual.
Cont. 8, pp. 17-22.
[2] Neuhart, J. B. (1987) Effects of correlated sub-samples in statistical
process control. IIE Trans. 19, pp. 208-214.
[3] Hahn, G. J. and Cochrum, M. B. (1987) Adapting control charts to meet
practical needs: A chemical process application. J. Appl. Statist. 10, pp.
35-52.
[4] Wetherill, G. B. and Brown, D. W. (1991) Statistical Process Control:
Theory and Practice Chapman and Hall, London, UK.
[5] Porter, L. J. and Caulcutt, R. (1992) Control chart design: A review of
statistical practice. Qual. Reliab. Eng. Int. 8 , pp. 113-122.
[6] Woodall, W. H. and Thomas, E. V. (1995) Statistical process control
with several components of common cause variability. IIE Trans. 27, pp.
757-764.
[7] Laubscher, N. (1996) A variance component model for statistical process
control. S. Afr. Statist. J. 30, pp. 27-47.
[8] Khuri, A. I. (2000) Designs for Variance Components Estimation: Past
and Present. International Statistical Review. 68, pp. 311-322.
[9] Yashchin, E. (1995) Likelihood Ratio Methods for Monitoring
Parameters of a Nested Random Effect Model. Journal of the American
Statistical Association. 90, pp. 729-738.
[10] Yashchin, E. (1994) Monitoring Variance Components. Technometric.
36, pp. 379-393.
[11] Wheeler, D. (1995) Advanced Topics in Statistical Process Control SPC
Press, Knoxville, TN.
[12] Wheeler, D. and Chambers, D. (1992) Understanding Statistical Process
Control, 2nd. SPC Press, Knoxville, TN.
[13] Calzada, M. E. and Scariano, Stephen M. (2004) Average Run Length
Computations for the Three-Way Chart. Communications in Statistics -
Simulation and Computation. 33, pp.505 - 524.
@article{"International Journal of Mechanical, Industrial and Aerospace Sciences:63989", author = "W. Chattinnawat", title = "A Strategy to Optimize the SPC Scheme for Mass Production of HDD Arm with ClusteringTechnique and Three-Way Control Chart", abstract = "Consider a mass production of HDD arms where
hundreds of CNC machines are used to manufacturer the HDD arms.
According to an overwhelming number of machines and models of
arm, construction of separate control chart for monitoring each HDD
arm model by each machine is not feasible. This research proposed a
strategy to optimize the SPC management on shop floor. The
procedure started from identifying the clusters of the machine with
similar manufacturing performance using clustering technique. The
three way control chart ( I - MR - R ) is then applied to each
clustered group of machine. This proposed research has
advantageous to the manufacturer in terms of not only better
performance of the SPC but also the quality management paradigm.", keywords = "Three way control chart. I - MR - R ,between/within variation, HDD arm.", volume = "2", number = "6", pages = "825-4", }