Real-time Tool Defect Detection Systems

To support the competition and to produce industrial economically, it seems mostly that manufacturing systems must exhibit a high level of disgruntlement in their operation. Hence, to create such flexibility and thereby develop a Flexible Manufacturing System (FMS), it is necessary to reduce the human intervention in production. Flexibility in manufacturing can be understood and applicable through various standards & interpretations. In this work, the defects in the tools will be analyzed using image processing tools could exploit defects in the tool at a flexible manufacturing environment. Information from the installed sensors on vibration, acoustic emissions, spindle speed and cutting force in machine can be employed together to infer indirectly about tool flaws; portions or position referenced for correspondence mapping thus locating areas of interest. This information is primarily obtained through sensors, and upon processing this data it gives us some insight as to how the tool functions abnormally or where potential defects may be. A more direct approach is the non-contact cutting tool monitoring especially that which typically relies on vision images using optical inspection. This is because using this method, tool defects can be accurately described by size and position. It is possible to use digital image processing technologies to enhance the precision of the inspection and identification of the cutting tool’s condition. Besides making the methodology less rigid, it improves the reliability of cutting tool damage, workpiece defects, and tool positioning. In this case, the inspection process is aided by the DIP in providing extensive visual information concerning the condition of the tool. When it comes to this technology method, real-time control and quick decisions are provided and the dependence on people is significantly reduced, increasing versatility and manufacturing system performance. Therefore, the development of competitive and genuinely versatile production settings entails the incorporation of such advanced monitoring approaches.