A method comprising: controlling, by a computing device, a directed energy deposition material addition (DED MA) technique based at least in part on a thermal model of the DED MA technique for a component, wherein the thermal model defines a plurality of default operating parameters for the DED MA technique; and characterized by: detecting, by at least a first sensor comprising a thermal camera to detect infrared radiation, at least one parameter comprising at least one of a melt pool geometry or a melt pool temperature; detecting, by at least a second sensor, at least a temperature of a component or a geometry of a component; responsive to determining, by the computing device, that a value of the at least one detected parameter is different from an expected value of a corresponding parameter predicted by the thermal model, determining, by the computing device and using a neuro-fuzzy algorithm, an updated value for at least one operating parameter for the DED MA technique, wherein the neuro-fuzzy algorithm includes at least two fuzzy if-then rules as nodes in an adaptive network; and controlling, by the computing device, the DED MA technique based at least in part on the updated value for the at least one operating parameter. controlling, by a computing device, a directed energy deposition material addition (DED MA) technique based at least in part on a thermal model of the DED MA technique for a component, wherein the thermal model defines a plurality of default operating parameters for the DED MA technique; and characterized by: detecting, by at least a first sensor comprising a thermal camera to detect infrared radiation, at least one parameter comprising at least one of a melt pool geometry or a melt pool temperature; detecting, by at least a second sensor, at least a temperature of a component or a geometry of a component; responsive to determining, by the computing device, that a value of the at least one detected parameter is different from an expected value of a corresponding parameter predicted by the thermal model, determining, by the computing device and using a neuro-fuzzy algorithm, an updated value for at least one operating parameter for the DED MA technique, wherein the neuro-fuzzy algorithm includes at least two fuzzy if-then rules as nodes in an adaptive network; and controlling, by the computing device, the DED MA technique based at least in part on the updated value for the at least one operating parameter. detecting, by at least a first sensor comprising a thermal camera to detect infrared radiation, at least one parameter comprising at least one of a melt pool geometry or a melt pool temperature; detecting, by at least a second sensor, at least a temperature of a component or a geometry of a component; responsive to determining, by the computing device, that a value of the at least one detected parameter is different from an expected value of a corresponding parameter predicted by the thermal model, determining, by the computing device and using a neuro-fuzzy algorithm, an updated value for at least one operating parameter for the DED MA technique, wherein the neuro-fuzzy algorithm includes at least two fuzzy if-then rules as nodes in an adaptive network; and controlling, by the computing device, the DED MA technique based at least in part on the updated value for the at least one operating parameter.