Design of IoT-Based Manufacturing Quality Control Systems with Exponentially Weighted Moving Average Chart
Keywords:
statistical process control, internet of things, exponentially weighted moving average, digital caliper, web-based systemAbstract
Quality control is needed for each manufacturing industry, so that product quality remains stable. Product variation is one of the factors that can reduce product quality, so quality control is needed. Control chart is one of the tools in statistical quality control that can be used to detect changes in product variations. One of the control charts that has a high sensitivity to the average shift is Exponentially Weighted Moving Average (EWMA) control chart. But in its implementation, the company still uses a manual process, starting from the process of data collection, data entry, to data processing into a control chart. Manual process can cause data collection errors and delays in quality decision. For EWMA control chart, the complicated calculation contributes additional difficulties for quality control implementation. The Internet of Things (IoT) is a concept that combines a device with other devices using internet connectivity, so that the distribution of data and information flow becomes faster and more accurate. Based on the IoT concept, a quality control design system is proposed that integrates data retrieval, data processing, control chart computation, and data display so that it can improve speed and accuracy in the process of making EWMA control chart. Based on testing, this system has been able to improve the accuracy of data retrieval and processing, speed up the process of making control charts, and integrate data collection, recording, and processing activities.
References
Ćwikła, G. (2013) ‘Methods of Manufacturing Data Acquisition for Production Management - A Review’, Advanced Materials Research, 837, pp. 618–623. doi: 10.4028/www.scientific.net/amr.837.618.
Lee, I. and Lee, K. (2015) ‘The Internet of Things (IoT): Applications, investments, and challenges for enterprises’, Business Horizons. ‘Kelley School of Business, Indiana University’, 58(4), pp. 431–440. doi: 10.1016/j.bushor.2015.03.008.
Mishra, D. et al. (2018) ‘A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0’, Journal of Manufacturing Processes, 36, pp. 373–397. doi: 10.1016/j.jmapro.2018.10.016.
Mitra, A. (2008) Introduction to quality control and improvement. 3rd ed. A John Wiley Sons Inc., New Jersey.
Montgomery, D. (2009) Introduction to Statistical Quality Control. 6th edn. John Wiley & Sons, New York.
Xu, L. Da, He, W. and Li, S. (2014) ‘Internet of things in industries: A survey’, IEEE Transactions on Industrial Informatics, 10(4), pp. 2233–2243. doi: 10.1109/TII.2014.2300753.
