A semi-automatic mold cost estimation framework based upon geometry similarity

The automation of cost estimation for manufacturing processes is a challenging task in computer-aided manufacturing. In this paper, we introduce a two-step analogy and mathematical approach to estimate the cost of injection molding. In the analogy step, data of molds are partitioned into homogeneous groups based on mold type and mold design. In the prediction step, regression models based upon geometry, topology, and other inherent shape properties are constructed within each group. The variables in the regression models within each group are extracted automatically from one orthographic two-dimensional (2D) image of the injection-molded part. Mean and variance estimates are calculated on a subset of relevant molds so that the risk of an inaccurate bid can be assessed on a subset of relevant molds.