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Keywords

Data analysis and industrial automation, manufacturing quality control, non-contact temperature measurement, production optimisation, sensor accuracy

Document Type

Research Article

Abstract

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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Publication Date

31-3-2025

First Page

56

Last Page

67

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