OPTIMIZING NOISE LEVEL TO ENHANCE INDUSTRIAL WORKERS PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

OPTIMIZING NOISE LEVEL TO ENHANCE INDUSTRIAL WORKERS PERFORMANCE USING ARTIFICIAL NEURAL NETWORK

A. R. Ismail, M. Y. M Yusof, B. M. Deros, M. M. Noor and K. Kaardigama

Abstract: Noise is one of the environmental factors that contribute significant effect on the worker performance in automotive industries. This paper presents an optimization of noise level towards the worker productivity rate at one of the Malaysian industry. An assembly automotive manufacturing industry was chosen to conduct the study by observing and measuring the noise level and worker’s productivity rate. The data then were analyzed by using Artificial Neural Network (ANN) analysis and it is commonly used to analyze and obtain the best linear relationship from the collected data. It is apparent that from the linear relationship obtained, the optimum value of production (value≈1) is attained when the noise level is 81.62 dBA. The optimum value production rate (value≈1) for one manual production line in that particular company is successfully achieved. Using ANN analysis, the optimum environmental factor managed to be predicted.

Keywords: Artificial Neural Network (ANN), Optimum, Productivity, Noise, Environmental.

Fulltext  PDF