Affiliations: [a] Scispot, Kitchener, ON, Canada
| [b]
Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute, Agra, Uttar Pradesh, India
| [c]
Department of Electrical Engineering, Faculty of Engineering, Dayalbagh Educational Institute, Agra, Uttar Pradesh, India
Correspondence:
[*]
Address for correspondence: Gurumukh Das, Assistant Professor, Department of Mechanical Engineering, Faculty of Engineering, Dayalbagh Educational Institute, Agra –282 005, Uttar Pradesh, India. E-mail:[email protected].
Abstract: BACKGROUND:
Quantitatively analyzing the impact of UV radiation and noise during welding operations is essential to assess the exposure, identify potential hazards, and develop targeted safety protocols to ensure worker safety and adherence to safety regulations, especially in developing countries with inadequate adherence to safety standards and resources. OBJECTIVES:
This study employs machine learning for predicting ultraviolet radiation and noise levels during welding, emphasizing worker safety. The focus is on the Indian foundry sector to gauge actual exposure vis-á-vis safety standards. MATERIALS AND METHODS:
Ultraviolet radiation and noise emitted during the welding of a ferrous alloy were collected from three foundries in Agra, India. Five machine learning (ML) algorithms were applied for data analysis and prediction of UV radiation and noise levels, and a relative performance comparison was carried out on the compiled data against safety standards. RESULTS:
Out of all the ML algorithms applied, the Support Vector Machine regression algorithm (RMSE = 356.93) obtained the best performance on UV radiation data, and the Random Forest algorithm (RMSE = 11.4) was found to give the best results for the noise level prediction task. CONCLUSIONS:
This work represents the first known application of machine learning techniques for predicting UV radiation and noise levels in arc welding processes. The results show the efficacy of algorithms such as SVM regression and Random Forest for the problem. Further, the datasets and ML algorithms implemented in the work will be made openly available to support further research endeavors in this and related areas.
