Development of image based flank wear and surface roughness wear detection system for milling application.

This paper presents the image-based tool wear detection using an image processing approach for face milling operation to prevent the workpiece surface roughness which is affected by the worn cutting tool. Based on the previous research, it was found that lack of monitoring of detection tool wear percentage to determine the condition of the cutting tool which the cutting tool needs a replacement or can continue used for the machining process. When the workpiece machining length increases, the machining time increases. The cutting requirement will increase based on their tool lifespan which will increase the percentage of tool wear rapidly. Therefore, the tool wear monitoring procedures applied after several milling processes such as image-based tool wear percentage detection by using an image processing approach for uncoated carbide tools, tool life criteria based on flank wear according to ISO 8688-1:1989 for uncoated carbide tools, and surface roughness quality according to ISO 4288:1996 for mild steel (AISI 1045) workpiece. The results indicated that the percentage of wear increase up to 50 percent with the cutting length and machining time. The surface roughness of the test material also increased linearly with increases in cutting length and machining time. In conclusion, the image-based tool wears percentage value detection by using an image processing approach has a potentially sustainable and economical expenditure for determining tool life criteria to sustain the machining performance. [ABSTRACT FROM AUTHOR]