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

Area of Science:

• Engineering
• Occupational Health
• Acoustics

Background:

• Computer Numerical Control (CNC) wood milling machines increase productivity but generate excessive noise pollution, posing health risks.
• Occupational noise exposure in woodworking environments is a significant concern.

Purpose of the Study:

• To investigate the influence of machining parameters on Noise Pollution Level (NPL) in CNC wood milling.
• To optimize CNC wood milling parameters to minimize noise emissions and enhance worker safety.

Main Methods:

• Response Surface Methodology (RSM) with Box-Behnken Design (BBD) was used to model NPL based on cutting speed, feed rate, depth of cut, and step over.
• Statistical analyses including ANOVA and regression modeling were performed.
• A Genetic Algorithm (GA) was employed for optimizing the identified parameters.

Main Results:

• Noise Pollution Levels (NPL) ranged from 97.4 dB to 103.8 dB, exceeding the NIOSH recommended limit of 85 dB.
• Cutting speed, feed rate, and depth of cut significantly impacted NPL (p < 0.05).
• Optimized parameters predicted a reduced NPL of 96.2 dB, validated experimentally at 95.8 dB.

Conclusions:

• Machining parameter optimization effectively reduces NPL in CNC wood milling.
• The integrated RSM and GA approach offers a reliable method for minimizing occupational noise exposure.
• This research contributes to enhanced worker safety in woodworking industries.