Prediction of noise emission in the machining of wood materials by means of an artificial neural network

Abstract

Background: Noise produced during machining of wood materials can be a source of harm to workers and an environmental hazard. Understanding the factors that contribute to this noise will aid the development of mitigation strategies. In this study, an artificial neural network (ANN) model was developed to model the effects of wood species, cutting width, number of blades, and cutting depth on noise emission in the machining process. Methods: A custom application created with MATLAB codes was used for the development of the multilayer feed-forward ANN model. Model performance was evaluated by numerical indicators such as MAPE, RMSE, and R2. Results: The ANN model performed well with acceptable deviations. The MAPE, RMSE, and R2 values were 0.553%, 0.600, and 0.9824, respectively, in the testing phase. Furthermore, this study predicted the intermediate values not provided from the experimental study. The model predicted that lower noise emissions would occur with decreased cutting width and cutting depth. Conclusions: ANNs are quite effective in predicting the noise emission. Practitioners relying on the ANN approach for investigating the effects of various factors on noise emission can save time and costs by reducing the number of experimental combinations studied to generate predictive models.