Novel Tool Wear Monitoring of Cutting Tools Using Neural Network based Observers

ABSTRACT: This proposal seeks to develop a novel tool wear monitoring technology using a neural network-based observer. In this methodology simple models of the wear process and another related machining process, such as force, are developed. The model development does not require lengthy analysis or experimentation since the methodology inherently accounts for the uncertainty in the models. The monitoring methodology measures the related machining process signal, as well as other available signals, and a neural network-based observer is used to estimate model uncertainties and the tool wear state. This information can be used to ensure tools are changed in a timely manner. Preliminary simulation studies show the promise of the proposed monitoring technology and this proposal seeks to further develop this methodology and prove it with real machining data of Ti6Al4V. BENEFIT: The proposed tool wear monitoring methodology will allow manufactures to dramatically increase the productivity and quality of their manufacturing operations. The ability to cost-effectively monitor the state of cutting tools in real time will provide the means to prevent cutting tools from being taken out of service before they have been fully utilized or from being taken out of service after they are too worn to be reground or, worse yet, they wear to the point of breakage. The proposed technology will minimize tool changes and costly downtime, allow cutting tools to be utilized longer, and will minimize incidents of tool breakage that ruin parts, which is particularly costly in a finishing operation of a part that undergone days, or even weeks, of processing. The commercial impact will be tremendous since the proposed technology can be applied to any machining operation.