Methods and apparatus for machine learning predictions of manufacture processes

A non-transitory memory device including instructions stored thereon for directing one or more computer processors to provide a graphical interface tool for providing predictions of manufacturing processes, the instructions comprising steps for:

• receiving by the graphical interface tool an electronic file including a digital model representative of a physical object to be manufactured;
• receiving by the graphical interface tool at least one manufacturing parameter taken from a group consisting of:
• an identification of a manufacturing machine, an identification of a fabrication material, or an identification of a manufacturing process;
• and
• returning by the graphical interface tool, in near real-time or faster, responsive information associated with manufacturing the physical object, the responsive information including information related at least to a predictive value generated by a predictive engine;
• wherein the predictive value generated by the predictive engine is generated by the predictive engine based at least in part upon (a) at least one of a set of parameters associated with the digital model or information derived from the set of parameters, and (b) one or more neural network machine learning models, at least one of the one or more neural network machine learning models trained using a collection of digital models of a domain of mechanical objects;
• wherein the at least one predictive value corresponds to one or more of the following:
• predicted cost, set-up time, cycle time, a number of Computer Numerical Control (CNC) operations, requirement of a mill machine, requirement of a lathe machine, requirement of a sheet metal process, a type of blank used, or a type of fixture used;
• and
• wherein at least one of the one or more neural network machine learning models is selected from an available set of a plurality of neural network machine learning models based upon the received manufacturing parameter.