Rapid design of tool-wear condition monitoring systems for turning processes using novelty detection

Condition monitoring systems of manufacturing processes have been recognised in recent years as one of the key technologies that provide the competitive advantage in many manufacturing environments. It is capable of providing essential means to reduce cost, increase productivity, improve quality and prevent damage to the machine or work-piece. Turning operations are considered one of the most common manufacturing processes in industry. It is used to manufacture different round objects such as shafts, spindles and pins. Despite recent development and intensive engineering research, the development of tool wear monitoring systems in turning is still on-going challenge. In this paper, force signals are used for monitoring tool wear in a feature fusion model. A novel approach for the design of condition monitoring systems for turning operations using novelty detection algorithm is presented. The results prove that the developed system can be used for rapid design of condition monitoring systems for turning operations to predict tool-wear.