Identificação da formação de aresta postiça de corte no corte ortogonal do aço ABNT 1050 por meio de sinais de vibração e pressão sonora

Abstract

This study proposed and validated an experimental methodology for identifying the formation of the built-up edge (BUE), a recurrent phenomenon in machining that compromises the surface quality of workpieces and reduces tool life. The investigation was based on the analysis of fragments adhered to the smooth surface and tip of the chip, generated in orthogonal cutting tests on AISI 1050 steel. The approach combined the microstructural analysis of the chips with dynamic vibration signals (Y and Z axes) and sound pressure (SP) signals, allowing the correlation between the physical events of the process and the formation or detachment of the BUE. A special workpiece was designed to enhance the observation of the phenomenon, featuring a smooth cutting entry and a limited effective length to control chip generation. The microhardness analysis confirmed the formation of the BUE, showing an increase of 18% in the chip body compared to the as-received material and up to 80% in the regions containing BUE fragments. Most of the fragments exhibited lengths between 0.1 mm and 0.4 mm, representing 75% of occurrences in the tests with one cross-section and 92% in the tests with three sections. The spectral analysis of the signals revealed the “silence” phenomenon, interpreted as the momentary interruption of cutting caused by the detachment of the BUE, mainly observed in the Z-axis signals. The coincidence between the fragment positions and the silence events averaged 65% in the Z-axis (with peaks of 83%) and 47% in the Y-axis (with peaks of 71%). The SP signal was not effective for pointwise analysis. The Principal Component Analysis (PCA) did not yield satisfactory results for direct BUE identification due to the low variability among the signals. Conversely, the Continuous Wavelet Transform (CWT) demonstrated high accuracy, with an average coincidence of 84% in the Z-axis, 72% in the Y-axis, and 80% in the SP signal. Therefore, the proposed methodology proved to be effective, accessible, and promising for BUE monitoring, contributing to advances in quality control of machining processes.