(untitled) (Jurnal Mekanikal, 2024)
The surface quality of final product in machining is governed by many intimately related factors such as tool conditions and machining parameters. Amongst these factors, tool wear is essentially one of the most prominent influences on dimensional accuracy, surface roughness and tool life. Since the measurement of tool wear in manufacturing is still done manually, automated and intelligent measurement of wear are gaining more interest in the perspective of reducing human interference and hence, the accurate assessment of tool condition. This research work proposes a fast and reliable image processing method in measuring the nose wear of cutting inserts. Two image digitization methods were used, which are flatbed scanner (CanoScan5600F) and 3-D metrology system (Alicona InfiniteFocus). A sub-pixel edge detection algorithm was developed in the segmentation of nose area to improve the measurement accuracy. Scanning of tool nose was conducted before and after the machining for the measurement of wear area. Results show that about 5% to 6% of average absolute deviations were obtained from the measurement of nose wear area (Ap)and nose flank wear (VBc(max)) using images from InfiniteFocus and scanner.