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A Prototype of an IoT-based Production Performance and Quality Monitoring System Using NodeMCU ESP8266 Erwin Sitompul; Trias Wulandari; Mia Galina
Techné : Jurnal Ilmiah Elektroteknika Vol. 21 No. 1 (2022)
Publisher : Fakultas Teknik Elektronika dan Komputer Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31358/techne.v21i1.306

Abstract

A production process requires performance and quality monitoring to maintain the product quantity and quality to a prescribed standard. With the product quality classified into three categories (OK, Repair, and Scrap), the quantity count in a finishing process of a car door rubber seal production at PT. XYZ is still done manually. This count is conducted by an operator during the work and is prone to inaccuracies. Furthermore, the count result must be manually processed by an administrator at the end of a work shift. This slow recapitulation process makes optimal production performance and quality monitoring (PPQM) not possible. In this paper, a prototype of an IoT-based system for PPQM is proposed as the solution. The prototype provides three pushbuttons for the operator to directly record the quality of each product, while the quantity is automatically added up. NodeMCU microcontroller sends the production data to Blynk IoT-platform via internet connection. The quasi-real-time data can later be monitored through Blynk mobile application. The application also displays a performance-and-quality rate (PQ rate) as a monitoring indicator. One infrared sensor is utilized to detect the work objects. The operator consistency to enter the data is maintained by the use of a clamp mechanism. Test results show that the infrared sensor works very well within the detection range of 8 mm. The pushbuttons must be pressed for at least 0.98 second, so the input can be correctly relayed by NodeMCU to the server. An MS Excel macro is developed so that the production data can be processed automatically and quickly. The simulation results show that the proposed system can successfully simplify and increases the accuracy of the production data record. Besides, it makes a quasi-real-time PPQM possible.