Process planning for die and mold machining based on pattern recognition and deep learning

Dies and molds are necessary elements in the manufacturing of current industrial products. There is increasing pressure to machine high quality complicated surfaces at low cost. The standardization of process planning is said to be a key to improving the efficiency of machining operations in practice. Thus, computer aided process planning (CAPP) systems are urgently needed to reduce the time and effort of preparing machining operations. However, it is difficult to generalize process planning that continues to depend on skillful experts and requires long preparation time for die and mold machining. On the other hand, to overcome issues that are difficult to generalize, it is well known that machine learning has the capability to estimate valid values according to past case data. Therefore, this study aims to develop a CAPP system that can determine machining process information for complicated surfaces of die and mold based on pattern recognition and deep learning, a kind of machine learning. A network architecture called 3D u-net is adapted to effectively analyze whole images by producing segmented regions. Using a voxel model representing targeted shape, it becomes easier to deal with the complicated surfaces of die and mold generally and three-dimensionally, as skilled experts pay attention to whole geometrical features. Cutting tool type and tool path pattern are treated as machining process information determined in a CAPP system. The results of case studies confirm that the developed CAPP system is effective in determining the machining process information even for complicated surfaces according to the implicit machining know-how.