Real-time adaptive control of additive manufacturing processes using machine learning

A method for real-time adaptive control of a post-design free form deposition process or a post-design joining process, the method comprising:

• a) providing an input design geometry for an object;
• b) providing a training data set, wherein the training data set comprises:
• (i) past process simulation data, past process characterization data, past in-process physical inspection data, or past post-build physical inspection data, for a plurality of objects that comprise at least one object that is different from the object to be physically fabricated that is provided in step (a);
• and
• (ii) training data generated through a repetitive process of randomly choosing values for each of one or more input process control parameters and scoring adjustments to the input process control parameters as leading to either undesirable or desirable outcomes, the outcomes based respectively on the presence or absence of defects detected in a fabricated object arising from the process control parameter adjustments;
• c) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties as the object is being physically fabricated;
• and
• d) providing a processor programmed to:
• (i) predict an optimal set of one or more process control parameters for initiating the free form deposition process or joining process, wherein the predicted optimal set of one or more process control parameters are derived using a machine learning algorithm that has been trained using the training data set of step (b);
• (ii) remove noise from the object property data provided by the one or more sensors prior to providing it to the machine learning algorithm;
• (iii) provide a real-time classification of detected object defects using the machine learning algorithm that has been trained using the training data set of step (b), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm, and wherein the real-time classification of detected object defects is output from the machine learning algorithm;
• and
• (iv) provide instructions to perform the post-design free form deposition process or post-design joining process to fabricate the object, wherein the machine learning algorithm adjusts the one or more process control parameters in real-time while physically performing the free form deposition process or the joining process.