Year: 2025
Type: article
Abstract: Surface finish is a critical indicator of machining quality and plays an important role in determining the functional performance, dimensional accuracy, and service life of machined components. In CNC machining, surface roughness is influenced by the interaction of cutting parameters, tool dynamics, workpiece characteristics, and machining-induced vibrations. Conventional optimization approaches generally depend on post-machining surface measurements and may provide limited information about the dynamic behaviour occurring during the cutting process. The present study proposes a vibration-based approach for optimizing CNC machining parameters to improve surface finish and enhance machining performance. The study investigates the influence of key machining parameters, including spindle speed, feed rate, and depth of cut, on cutting-induced vibrations and surface roughness. Vibration signals are acquired during machining using an appropriate sensing system and analysed in the time and frequency domains to identify characteristic vibration patterns associated with different machining conditions. The corresponding surface roughness, particularly the arithmetic average roughness (Ra), is measured after machining and correlated with the recorded vibration characteristics. Statistical and optimization techniques are subsequently employed to establish the relationship between machining parameters, vibration behaviour, and surface quality and to identify the parameter combination associated with improved surface finish. The proposed methodology provides a non-destructive and potentially real-time approach for assessing machining stability and predicting surface quality. The study is expected to demonstrate that vibration characteristics can serve as effective indicators of machining performance and can be integrated into parameter optimization and intelligent CNC manufacturing systems. The findings may contribute to reducing experimental trial-and-error, improving surface quality, enhancing process stability, and supporting data-driven optimization of CNC machining operations.
Language: English
Subfield: Mechanical Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7220720637