Towards intelligent setting of process parameters for layered manufacturing

The performance of a layered manufacturing (LM) process is determined by th e appropriate setting of process parameters. The study of the relationship between performance and process parameters is therefore an important area o f LM process planning research. The trend in modern industry is to move fro m conventional automation to intelligent automation. LM technology is essen tially an automated manufacturing technology that is evolving towards an in telligent automation technology. Slicing solid manufacturing (SSM) is a LM technique using paper as the working material and a CO2 laser as the cuttin g tool. In this manuscript, a back propagation (BP) learning algorithm of a n artificial neural network (ANN) is used to determine appropriate process parameters for the SSM method. Key process parameters affecting accuracy ar e investigated. Quantitative relationships between the input parameters and output accuracy are established by developing the BP neural network.