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Reduction of noise pollution in CNC wood milling through multi-parameter optimization using response surface methodology

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  • Shiva Souri
  • Farshad Rabiei

Abstract

Background: CNC (Computer Numerical Control) wood milling machines offer significant productivity advantages but are associated with excessive noise pollution, posing health risks to workers. This study investigates the influence of machining parameters on Noise Pollution Level (NPL) in CNC wood milling and aims to optimize these parameters to minimize noise emissions. Methods: A Response Surface Methodology (RSM) based on Box-Behnken Design (BBD) was employed to model the effects of cutting speed, feed rate, depth of cut, and step over on NPL. A total of 27 experimental runs were conducted. Statistical analysis, including ANOVA and regression modeling, was performed to determine the significance of each parameter. The model was further optimized using a Genetic Algorithm (GA). Results: The NPL observed across experiments ranged from 97.4 dB to 103.8 dB, with all values exceeding the NIOSH recommended limit of 85 dB. ANOVA results revealed that cutting speed, cutting speed squared, feed rate, and depth of cut had a statistically significant effect on NPL (p

Suggested Citation

  • Shiva Souri & Farshad Rabiei, 2025. "Reduction of noise pollution in CNC wood milling through multi-parameter optimization using response surface methodology," PLOS ONE, Public Library of Science, vol. 20(12), pages 1-17, December.
    • Handle: RePEc:plo:pone00:0332222
    DOI: 10.1371/journal.pone.0332222
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