Open Collections

Parsaeimotamed, Mahsa

University of British Columbia

This thesis investigated cutting power in circular sawing with two primary objectives: analyzing the effects of five main factors—wood species (Douglas-fir and Western maple), wood condition, rake angle, feed speed, and tool wear level—on cutting power and developing predictive models using sensor data. Exploratory data analysis (EDA) and regression modeling identified tool wear level and rake angle as the most impactful factors on cutting power, with significant interaction effects observed between feed speed and tool wear, feed speed and wood condition, tool wear and rake angle, and tool wear and wood condition. These findings provide valuable insights for optimizing the sawing process. To improve prediction accuracy, sensor-based models were developed using accelerometer, microphone, and acoustic emission (AE) sensors. Sensor data was preprocessed with discrete wavelet denoising to remove noise and enhance signal clarity. Subsequently, multiple time-domain and frequency-domain features were extracted for each sensor, with the most relevant selected using LASSO. Four sensor-based models—multiple linear regression (MLR), random forest (RF), fully connected neural network (FCNN), and 1D convolutional neural network (CNN)—were developed for predicting cutting power. Among the models, RF achieved the best performance (R²test = 0.910), surpassing both deep learning and linear models. Based on the feature importance analysis, microphone data emerged as the most predictive of cutting power among all three sensors, emphasizing its potential for real-time monitoring. In general, this study highlighted the value of sensor data integration to enhance cutting process efficiency and established a foundation for future research in real-time monitoring and predictive maintenance.

Text

2024-12-12

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/89865

Forestry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/