Publication: Towards IR4.0 implementation for smart manufacturing and predictive maintenance
| datacite.subject.fos | oecd::Engineering and technology::Electrical engineering, Electronic engineering, Information engineering::Electrical and electronic engineering | |
| dc.contributor.author | Muhamad Fahmie bin Shahrulzahadie | |
| dc.date.accessioned | 2025-06-06T02:42:58Z | |
| dc.date.available | 2025-06-06T02:42:58Z | |
| dc.date.issued | 2024-08 | |
| dc.description.abstract | This thesis explores the development and implementation of a smart manufacturing system aimed at enhancing predictive maintenance and operational efficiency. Using Industry 4.0 technologies, the system integrates Firebase Authentication for secure access, Google Sheets for real-time data storage, and a user friendly GUI for data visualization. The research involved collecting and analysing FFT vibration and temperature data from a milling machine using an Arduino Nano Wi-Fi Microprocessor, ADXL345 vibration sensor, and RTD Probe Temperature Sensor. The FFT analysis identified the machine's natural frequency at 25Hz and established upper limits for vibration magnitudes. The results from three samples collected under load conditions indicated that while the machine generally operated within acceptable parameters, Sample 3 revealed anomalies with one point on the X axis and five points on the Y-axis exceeding the upper limits. Additionally, the temperature peaked at around 80 degrees, suggesting potential issues such as misalignment, imbalance, or inadequate cooling and lubrication. These findings underscore the necessity of continuous monitoring and proactive maintenance. The study demonstrates that integrating real-time data acquisition and cloud-based storage in manufacturing processes can significantly improve the ability to prevent machine failures, thereby enhancing overall operational efficiency and machinery longevity. This research lays a solid foundation for the broader implementation of smart manufacturing systems, contributing to the advancement of predictive maintenance practices in the industry. | |
| dc.identifier.uri | https://erepo.usm.my/handle/123456789/22072 | |
| dc.language.iso | en | |
| dc.title | Towards IR4.0 implementation for smart manufacturing and predictive maintenance | |
| dc.type | Resource Types::text::report::technical report | |
| dspace.entity.type | Publication | |
| oairecerif.author.affiliation | Universiti Sains Malaysia |