Development of decision support knowledge based system for tool wear diagnosis

This paper deals with the problems faced by small and medium sized metal cutting industries, with the perspective of tool monitoring. In a small or medium size metal cutting industry employing major metal cutting process, one of the primary problem is that of tool monitoring and wear diagnosis. The problem is of immediate concern especially in those industries where the processes or operations employed are flexible and production depends entirely on orders from customers. Due to a flexible manufacturing setup, almost all major metal cutting processers need to be carried out. However, it becomes increasingly difficult for such small or medium size metal cutting industries to employ skilled manpower for each operation as well as expert technicians to supervise the operation, and carry out fault diagnosis and tool monitoring. Also, the problem associated with tool monitoring is that human operator carrying out the monitoring has to rely either on observation such as ceasing of tool, rise in temperature, generation of fumes, noisy operation, vibrations, considerable change in shape etc, or by monitoring the quality of the finished product. Also, there can be instances where the operator does notice a symptom but does not have the expertise to identify the cause of the trouble. Errors in tool monitoring can lead to considerable damage both to the machine as well as the workpiece. On the other hand, if the tool is replaced before it reaches its useful life expectancy, it leads to unnecessary additional cost. A Decision Support Knowledge Based System (DSKBS) has therefore been developed in this paper with the above considerations. The DSKBS provides the user with a friendly environment to diagnose a particular tool wear and obtain the necessary repair or replacement instructions. The goal is to increase productivity, decrease cost of operation and enhance total quality and reliability of the operation.