BENEFIT USING STATISTICAL PROCESS CONTROL (SPC) FOR PROCESS CONTROL IN TEXTILE MANUFACTURING : A REVIEW
DOI:
https://doi.org/10.36706/jmse.v8i1.54Keywords:
Statistical process control, industrial Control Systems, control charts, manufacturing, quality, hybrid systemsAbstract
The history of technological developments affects the way we can provide services to customers quickly. One of the methods of industrial Control Systems is the statistical process control method (SPC) for short-term production. This review aims to explain some of the advantages of the SPC method, namely in terms of monitoring multistage processes and fault diagnosis has become a necessity. by Entering process control. The SPC and hybrid approaches and their results help in building a sound understanding of process control with a sense of a different approach which helps in analyzing processes that further assist in making the right decisions.
Downloads
References
R. Sanchez-Marquez and J. Jabaloyes Vivas, “Multivariate SPC Methods for Controlling Manufacturing Processes Using Predictive Models – A Case Study in the Automotive Sector,” Computers in Industry, vol. 123, 2020, doi: 10.1016/j.compind.2020.103307.
S. A. H. Lim, J. Antony, and S. Albliwi, “Statistical Process Control (SPC) in the Food Industry - A Systematic Review and Future Research Agenda,” Trends in Food Science and Technology, vol. 37, no. 2, pp. 137–151, 2014, doi: 10.1016/j.tifs.2014.03.010.
A. Sánchez-Fernández, F. J. Baldán, G. I. Sainz-Palmero, J. M. Benítez, and M. J. Fuente, “Fault Detection Based on Time Series Modeling and Multivariate Statistical Process Control,” Chemometrics and Intelligent Laboratory Systems, vol. 182, no. June, pp. 57–69, 2018, doi: 10.1016/j.chemolab.2018.08.003.
S. Rukhsar, Y. U. Khan, O. Farooq, M. Sarfraz, and A. T. Khan, “Patient-Specific Epileptic Seizure Prediction in Long-Term Scalp EEG Signal Using Multivariate Statistical Process Control,” Irbm, vol. 40, no. 6, pp. 320–331, 2019, doi: 10.1016/j.irbm.2019.08.004.
Y. H. Choi, G. Y. Na, and J. Yang, “Fuzzy-Inference-Based Decision-Making Method for the Systematization of Statistical Process Capability Control,” Computers in Industry, vol. 123, p. 103296, 2020, doi: 10.1016/j.compind.2020.103296.
A. R. Tôrres, A. D. P. de Oliveira, S. Grangeiro, and W. D. Fragoso, “Multivariate Statistical Process Control in Annual Pharmaceutical Product Review,” Journal of Process Control, vol. 69, pp. 97–102, 2018, doi: 10.1016/j.jprocont.2018.06.001.
S. Gopi, A. Suresh, and A. John Sathya, “Value Stream Mapping & Manufacturing Process Design for Elements in an Auto-Ancillary Unit-A Case Study,” Materials Today: Proceedings, vol. 22, pp. 2839–2848, 2019, doi: 10.1016/j.matpr.2020.03.416.
E. Fourie, J. L. Aleixandre-Tudo, M. Mihnea, and W. du Toit, “Partial Least Squares Calibrations and Batch Statistical Process Control to Monitor Phenolic Extraction in Red Wine Fermentations under Different Maceration Conditions,” Food Control, vol. 115, no. March, p. 107303, 2020, doi: 10.1016/j.foodcont.2020.107303.
N. Bostijn, W. Dhondt, C. Vervaet, and T. De Beer, “PAT-Based Batch Statistical Process Control of a Manufacturing Process for a Pharmaceutical Ointment,” European Journal of Pharmaceutical Sciences, vol. 136, no. June, p. 104946, 2019, doi: 10.1016/j.ejps.2019.05.024.
M. S. Reynolds, S. P. Spencer, A. Dunaway, D. Buckingham, and T. Bartman, “Scientific Approach to Assess If Change Led to Improvement—Methods for Statistical Process Control Analysis in Quality Improvement,” Journal of Emergency Nursing, vol. 47, no. 1, pp. 198–205, 2021, doi: 10.1016/j.jen.2020.09.002.
H. N. Teixeira, I. Lopes, A. C. Braga, P. Delgado, and C. Martins, “Screwing Process Analysis Using Multivariate Statistical Process Control,” Procedia Manufacturing, vol. 38, pp. 932–939, 2019, doi: 10.1016/j.promfg.2020.01.176.
P. J. Kolesar, “The Relevance of Research on Statistical Process Control to the Total Quality Movement,” Journal of Engineering and Technology Management, vol. 10, no. 4, pp. 317–338, 1993, doi: 10.1016/0923-4748(93)90027-G.
M. Ren, J. Chen, P. Shi, G. Yan, and L. Cheng, “Statistical Information Based Two-Layer Model Predictive Control with Dynamic Economy and Control Performance for Non-Gaussian Stochastic Process,” Journal of the Franklin Institute, no. xxxx, pp. 1–22, 2021, doi: 10.1016/j.jfranklin.2021.01.007.
N. A. Roberts, K. Alexander, D. Wyld, and M. Janda, “Statistical Process Control Assessed Implementation Fidelity of Patient-Reported Outcome Measures (PROMs) in Routine Care,” Journal of Clinical Epidemiology, vol. 127, pp. 76–86, 2020, doi: 10.1016/j.jclinepi.2020.06.022.
D. S. Keller, T. Reif de Paula, G. Yu, H. Zhang, A. Al-Mazrou, and R. P. Kiran, “Statistical Process Control (SPC) to Drive Improvement in Length of Stay after Colorectal Surgery,” American Journal of Surgery, vol. 219, no. 6, pp. 1006–1011, 2020, doi: 10.1016/j.amjsurg.2019.08.029.
C. H. Kuo and H. P. Huang, “Colored Timed Petri Net Based Statistical Process Control and Fault Diagnosis to Flexible Manufacturing Systems,” Proceedings - IEEE International Conference on Robotics and Automation, vol. 4, no. April, pp. 2741–2746, 1997, doi: 10.1109/robot.1997.606701.
