## 4 Conclusions  
This research presents a comprehensive model for determining noise levels for evaluation in manufacturing environments. The proposed framework employs the ray tracing method and integrates a user interface application developed in MATLAB, which has been validated against real-world data. The configuration of noise levels and their spatial distribution is directly associated with identified noise sources, as outlined in the factory floor plans. Additionally, the user interface facilitates the display of predicted safety recommendations, enabling effective guidance for workers regarding exposure to varying noise levels. This approach not only enhances the understanding of noise dynamics within manufacturing settings but also serves as a crucial tool for promoting occupational safety. The simulation algorithm yields significant insights regarding noise levels within the factory floor plan, identifying both hazardous zones and areas deemed safe for worker occupancy. However, an average discrepancy of 3.06 dB was observed compared to actual measurements, attributed to the irregularities of noise sources and limitations in dosage estimation. To enhance the accuracy of this model, it is recommended to conduct longer-duration recordings of noise levels. The simulated values represent a worst-case scenario, presuming that all machinery operates at full capacity (100%), an occurrence that is unlikely but plausible. The fidelity of the simulation is further demonstrated by the alignment of data trends with real-world measurements, as illustrated in the line graph. Notably, the MATLAB ray tracing engine has not been previously utilised by researchers for noise analysis in industrial settings. This is largely due to the engine's initial design focus on higher-frequency waves. However, by extracting individual rays and applying Sound Pressure Level (SPL) formulas to them, the engine was effectively adapted for sound wave analysis. This enhancement broadens the versatility of the ray tracing engine, enabling its application in various scenarios, including noise simulation and predictive modelling. Moreover, applying this method to forecast future noise levels following changes to the factory layout would be advantageous. This capability would allow the factory to maintain compliance with allowable noise limits, thereby reducing hazard exposure and  
Figure 13: Comparison between SPLs from site and simulation. potentially decreasing the incidence of noise-induced hearing loss among workers. By enabling noise prediction, factories can optimize their floor plans and strategically plan for the integration of additional machinery within existing operations. Users would be able to visualise predicted noise levels, facilitating informed decision making regarding layout adjustments. However, a primary limitation of the proposed model lies in estimating machinery dosage, which can vary significantly from factory to factory. The accurate assessment of this dosage requires a detailed recording of machinery usage over time, highlighting the need for comprehensive data collection to enhance model precision. Conceptualization, P.K., M.S.A.K. and S.N.M. ; methodology, P.K. and M.S.A.K. ; software, P.K. ; validation, M.S.A.K. and S.N.M. ; formal analysis, P.K. ; investigation, P.K. ; resources, M.S.A.K. and S.N.M. ; data curation, P.K. ; writing--original draft preparation, P.K. ; writing--review and editing, M.S.A.K. and S.N.M. ; visualization, P.K. ; supervision, M.S.A.K. and S.N.M. ; project administration, M.S.A.K. ; funding acquisition, M.S.A.K. All authors have read and agreed to the published version of the manuscript. This research was funded by the MINISTRY OF HIGHER EDUCATION, MALAYSIA, under the Fundamental Research Grant Scheme (FRGS) under grant number FP051-2019A. Not applicable. Data Availability Statement: Data are contained within the article.