Temperature Monitoring System for Rubber Cutter Extrusion Machine Using Mlx90614 Temperature Infrared Sensor with Generative Pre-Trained Transformer (GPT) Prescriptive Analysis
This study aims to enhance the temperature monitoring system in rubber cutter extrusion machines at Sun Yuen Rubber Manufacturing using the MLX90614 infrared temperature sensor. The existing temperature measurement approach suffers from low accuracy and is susceptible to human error, leading to...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
UTP Press
2025-03-01
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| Series: | Platform, a Journal of Engineering |
| Subjects: | |
| Online Access: | https://mysitasi.mohe.gov.my/uploads/get-media-file?refId=a1ad2710-22d5-401a-83cf-13fc4cc934f4 |
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| Summary: | This study aims to enhance the temperature monitoring system in rubber cutter extrusion machines at Sun Yuen Rubber
Manufacturing using the MLX90614 infrared temperature sensor. The existing temperature measurement approach
suffers from low accuracy and is susceptible to human error, leading to inconsistent product quality and reduced
productivity. The proposed solution leverages the MLX90614 sensor for its high accuracy and non-contact temperature
measurement capabilities. The study involves designing and implementing a new temperature monitoring system
integrating the MLX90614 sensor. This system’s performance will be compared to the current system to demonstrate
improvements. The methodology includes designing the temperature monitoring setup, testing its functionality, and
collecting temperature data. The data will be analysed using statistical methods to evaluate the system’s accuracy,
reliability, and consistency in maintaining the desired temperature range. Additionally, the study incorporates
Generative Pre-Trained Transformer (GPT) models for prescriptive analysis. The GPT model will provide insights and
recommendations for optimising the temperature control process, further enhancing product quality and operational
efficiency by analysing the collected data. Expected outcomes include a significant improvement in the accuracy and
reliability of temperature measurements, leading to better product quality and increased productivity. This study will
also support the company’s progression towards Industry 4.0 and ESG standards by integrating advanced technology
to optimise production. Overall, this study will deliver a robust temperature monitoring system that mitigates human
error and enhances operational efficiency. The integration of GPT-based prescriptive analysis will provide actionable
insights, driving continuous improvements in the rubber manufacturing process. |
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| ISSN: | 2636-9877 |