A band-type network model for the time-series problem used for IC leadframe dam-bar shearing process.

Progressive shearing process of a thin metal sheet is regarded as one of the most important fabrication processes in the 3C industry. In this process, the problem of punch wear is a time-series issue that would be changed along with the increase of time and deciding the size of the punched hole. This study proposes a neural network to modeling the time-series problem involving three kinds of presentations. First, the time-characteristic diagram is plotted based on the characteristic values that the network inferred. Second, the characteristic value of the un-examined time could be derived by network inferring. Third, a band-type diagram is built to present the possible variation range of the characteristic analyzed. Two cases of punch wearing band and punched hole bands for IC (integrated circuit) leadframe dam-bar shearing process are discussed to demonstrate the construction of the network model. The verification experiments show that the network inferring results are in agreement with the actual process, and derive more accurate predictions than that of the curve-fitting methods commonly used in engineering. [ABSTRACT FROM AUTHOR]