With the advancement of modern technology, numerous advanced methods for measuring surface roughness have emerged. These are categorized into contact and non-contact methods, such as optical, ultrasonic, and machine vision measurements. By researching and integrating various characteristics, including cutting parameters, vibration, and force signals, with workpiece texture images, a comprehensive model for predicting surface roughness is formed. These novel multidimensional feature prediction equations significantly enhance practicality and accuracy in assessing the surface quality of machined workpieces, marking a clear advancement over traditional measurement techniques.
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Guo et al. (2024) studied this question.
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