Recently, improvement of productivity of the paper cup forming machine has being conducted by increasing manufacturing speed. However, rapid manufacturing speed imposes high load on cams and cam followers. It accelerates wear and cracking, and increases paper cup failure. In this study, a failure diagnosis algorithm was suggested using vibration data measured from cam driving parts. Among various paper cup forming processes, a test bed imitating the bottom paper attaching process was manufactured. Accelerometers were installed on the test bed to collect data. To diagnose failure from measured data, the K-NN (K-Nearest Neighbor) classifier was used. To find a decision boundary between normal and abnormal state, learning data were collected from normal and abnormal state, and normal and abnormal cams. A few representative features such as mean and variance were selected and transformed to the relevant form for the classifier. Classification experiments were performed with the developed classifier and data gathered from the test bed. According to assigned K values, a successful classification result was obtained which means appropriate failure recognition.

A paper cup forming machine performs the entire process to produce paper cups. Recently, as the demand for paper cups in various fields increases, the need for rapid and timely paper cup forming also increases. However, the more rapid the manufacturing speed is, the higher the possibility of forming failure. Frequent fault occurrences cause a time-consuming and costly repair process and reduces manufacturing efficiency. Among various fault factors in this research, position deviation of the paper from the original position, which induces a jamming and process stop, was selected and a novel deviation detecting system using multiple photo sensors was suggested. Before operating the position detecting system, the performance of the photo sensors was evaluated with respect to response speed and photo beam precision. A deviation detecting mechanism was designed. The developed deviation detecting system was integrated with the paper cup forming machine and experimented with using base papers. It was conformed that the suggested system could be used to diagnose paper deviation failure.