Design of a Sensor System to Digitize Manual End Milling Machine to Collect Machine Data

Chan, Choon Kit and Chong, Shin Hau (2020) Design of a Sensor System to Digitize Manual End Milling Machine to Collect Machine Data. INTI JOURNAL, 2020 (38). ISSN e2600-7320

Text ij2020_38.pdf - Published Version Available under License Creative Commons Attribution. Download (352kB)

Abstract

In the manufacturing system, machines and manufacturing data are becoming more closely linked. In order to collect manufacturing data, there have been many new technologies that are in line with the Industrial Revolution 4.0 to replace existing systems and machines. The objective of this study is to design a sensor system that can collect and stream machine data from an end milling machine to generate a manufacturing report. A prototype of this system was designed using a Raspberry Pi Model 3 B+, PiCamera module, and infrared sensors for installing onto the machine, while Microsoft Azure and Power BI were used to stream and publish the data in a report form. The design was placed onto a milling machine where a machinist had done one milling operation for one workpiece, the sensors collected the data of X-position, Y-position, feed rate, and spindle speed, while deriving the percentage utilization of total time for cutting steps. The end result was a system that was integrated with the milling machine where the critical operation data are collected to produce a manufacturing report that displays the use of the machine. For future studies, different applications for this kind of sensor system can be used to digitize different manufacturing systems. Furthermore, an optimization and standardization testing for this kind of manufacturing system could be set in the future as well.

Item Type:	Article
Uncontrolled Keywords:	Internet of things, Operation management, Computer vision, Manufacturing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TS Manufactures
Divisions:	Faculty of Engineering & Quantity Surveying
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	11 Nov 2020 11:27
Last Modified:	21 Mar 2024 03:09
URI:	http://eprints.intimal.edu.my/id/eprint/1453

Actions (login required)

View Item