Assessing occupational hazards in welding operations: A machine learning-based approach for worker safety in Indian foundries.
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 w...
| Publicado en: | Work Vol. 79; no. 1; pp. 405 - 416 |
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| Autores principales: | , , |
| Formato: | pictorial research tables/charts Journal Article |
| Publicado: |
Sage Publications Inc.
2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179666996&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179666996 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10519815 3RC jtl: Work issn: 10519815 maglogo: N pubinfo: dt: 2024 vid: 79 iid: 1 pid: 344 pub: Sage Publications Inc. place: Thousand Oaks, California artinfo: ui: 179666996 175686409 179666996 179666996 10.3233/WOR-230465 179666996 ppf: 405 ppct: 11 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Assessing occupational hazards in welding operations: A machine learning-based approach for worker safety in Indian foundries. aug: au: Garg, Mudit Das, Gurumukh Vuppuluri, Prem Prakash affil: Scispot, Kitchener, ON, Canada sug: subj: Occupational Hazards Evaluation Blue Collar Workers Occupational Safety Standards Machine Learning Ultraviolet Rays Adverse Effects Noise Adverse Effects Occupational Exposure Risk Factors Risk Assessment Human Male Female India Algorithms Comparative Studies Descriptive Statistics Random Forest Radiation Injuries Hearing Loss, Noise-Induced Occupational Health Work Environment Information Resources World Wide Web Male Female ab: 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. pubtype: Academic Journal doctype: pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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