Development of the Scientific Foundations of the Formation of the Surface Layer of Workpieces during Cutting Based on Nonlinear Dynamics, Fractal Analysis, and Neural Network Modeling

Key Points

• This research aims to develop a scientific basis for understanding how the surface layer forms during metal cutting and how to control its quality.
• Proposed a self-similarity criterion for assessing surface roughness via error of self-similarity.
• Utilized fractal dimension of vibroacoustic emission signals for real-time quality control in automated production.
• Developed a neural network model to enhance surface layer control in CNC machining.
• Introduced a diagnostic parameter (error of self-similarity) for assessing the quality of surface layers.
• Demonstrated the feasibility of real-time quality assessment using fractal dimensions of vibroacoustic signals.
• Proposed a neural network solution that optimizes the shaping process during CNC machining.