A method for real-time adaptive control of a free form deposition process, the method comprising: a) providing an input design geometry for an object; b) providing a first training data set, wherein the first training data set comprises process simulation data, process characterization data, in-process inspection data, or post-build inspection data, for a plurality of design geometries or portions thereof that are the same as or different from the input design geometry of step (a); c) providing a predicted optimal set or sequence of one or more process control parameters for fabricating the object, wherein the predicted optimal set of one or more process control parameters are derived using a first machine learning algorithm that has been trained using the first training data set of step (b); d) providing a second machine learning algorithm for detection and classification of defects, the second machine learning algorithm trained using a second training data set comprising post build-inspection data provided by a skilled operator and/or inspection data provided by an automated inspection tool; e) providing an automated object defect classification system configured to detect and classify defects in-process using the second machine learning algorithm and provide real-time process characterization data including in-process automated defect classification data; f) performing the free form deposition process to fabricate the object, wherein the real-time process characterization data is provided as input to the first machine learning algorithm to adjust one or more process control parameters in real-time, so as to correct a defect when first detected a) providing an input design geometry for an object; b) providing a first training data set, wherein the first training data set comprises process simulation data, process characterization data, in-process inspection data, or post-build inspection data, for a plurality of design geometries or portions thereof that are the same as or different from the input design geometry of step (a); c) providing a predicted optimal set or sequence of one or more process control parameters for fabricating the object, wherein the predicted optimal set of one or more process control parameters are derived using a first machine learning algorithm that has been trained using the first training data set of step (b); d) providing a second machine learning algorithm for detection and classification of defects, the second machine learning algorithm trained using a second training data set comprising post build-inspection data provided by a skilled operator and/or inspection data provided by an automated inspection tool; e) providing an automated object defect classification system configured to detect and classify defects in-process using the second machine learning algorithm and provide real-time process characterization data including in-process automated defect classification data; f) performing the free form deposition process to fabricate the object, wherein the real-time process characterization data is provided as input to the first machine learning algorithm to adjust one or more process control parameters in real-time, so as to correct a defect when first detected <EOS>