Optimizing machining operations using tool wear information

In recent years, there has been a phenomenal growth of computational power available for data collection and analysis on the shop floor. Consequently, it is now possible to formulate useful models to optimize the performance of some manufacturing systems using either on-line tool condition monitoring or indirect measurements related to the tool status. In this work, models are developed to minimize machining costs and processing times on transfer lines and cellular manufacturing systems. In addition, the use of acoustic emission (AE) for tool status monitoring is investigated. This dissertation begins with a survey of recent developments in tool condition monitoring by acoustic emission. The nature of acoustic emission generated during single point cutting of a variety of materials under different cutting conditions is investigated. A special circuit and novel sensor arrangement is proposed and implemented, which allows the signal to be gathered from both the workpiece and the tool-holder during turning. This signal is processed off-line, and the relationship between the signal detected and tool wear is analyzed. Following this exploration of tool condition monitoring, problems related to the optimization of processing costs and times in cellular manufacturing systems and transfer lines are formulated. These models utilize tool life relationships to determine the tradeoff between gains obtained by faster processing and the corresponding increase in setups resulting from shorter tool lives. Finally, an adaptive scheme for determining cutting speeds using tool-life models and on-line tool wear data is proposed