Chen, J.; Lin, J.; Zhang, M.; Lin, Q. Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression. Sensors2024, 24, 2117.
Chen, J.; Lin, J.; Zhang, M.; Lin, Q. Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression. Sensors 2024, 24, 2117.
Chen, J.; Lin, J.; Zhang, M.; Lin, Q. Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression. Sensors2024, 24, 2117.
Chen, J.; Lin, J.; Zhang, M.; Lin, Q. Predicting Surface Roughness in Turning Complex-Structured Workpieces Using Vibration-Signal-Based Gaussian Process Regression. Sensors 2024, 24, 2117.
Abstract
Surface roughness prediction is a pivotal aspect of the manufacturing industry, as it directly influences product quality and process optimization. This study introduces a predictive model for surface roughness in the turning of complex-structured workpieces, utilizing Gaussian Process Regression (GPR) informed by vibration signals. The model captures parameters from both the time and frequency domains of the turning tool, encompassing the mean, median, standard deviation (STD), and root mean square (RMS) values. The signal is from the time to frequency domain is executed using Welch's method, complemented by time-frequency domain analysis employing three levels of Daubechies Wavelet Packet Transform (WPT). The selected features are then utilized as inputs for the GPR model to forecast surface roughness. Empirical evidence indicates that the GPR model can accurately predict the surface roughness of turned complex-structured workpieces. This predictive strategy has the potential to improve product quality, streamline manufacturing processes, and minimize waste within the industry.
Keywords
surface roughness; vibration signal analysis; Gaussian Process Regression; Daubechies Wavelet Packet Transform; Complex-Structured Workpieces
Subject
Engineering, Mechanical Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
