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1. (WO2018217903) REAL-TIME ADAPTIVE CONTROL OF ADDITIVE MANUFACTURING PROCESSES USING MACHINE LEARNING
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CLAIMS

WHAT IS CLAIMED IS:

1. A method for real-time adaptive control of a free form deposition process or a 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 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 machine learning algorithm that has been trained using the training data set of step (b); and

d) performing the free form deposition process or the joining process to fabricate the object, wherein real-time process characterization data is provided as input to the machine learning algorithm to adjust one or more process control parameters in real-time.

2. The method of claim 1, wherein steps (b) - (d) are performed iteratively and process characterization data, in-process inspection data, or post-build inspection data for each iteration is incorporated into the training data set.

3. The method of claim 1, wherein the free form deposition process or joining process is a stereolithography (SLA), digital light processing (DLP), fused deposition modeling (FDM), selective laser sintering (SLS), selective laser melting (SLM), electronic beam melting (EBM), or welding process.

4. The method of claim 1, wherein the machine learning algorithm comprises an artificial neural network algorithm, a Gaussian process regression algorithm, a logistical model tree algorithm, a random forest algorithm, a fuzzy classifier algorithm, a decision tree algorithm, a hierarchical clustering algorithm, a k-means algorithm, a fuzzy clustering algorithm, a deep Boltzmann machine learning algorithm, a deep convolutional neural network algorithm, a deep recurrent neural network, or any combination thereof.

5. The method of claim 1, wherein the one or more process control parameters are adjusted at a rate of at least 100 Hz.

6. The method of claim 1, wherein the method is implemented using either: (i) a single integrated system comprising a deposition or joining apparatus, a sensor, and a processor; or (ii) a distributed, modular system comprising a first deposition or joining apparatus, a first sensor, and a first processor, wherein the first deposition or joining apparatus, the first sensor, and the first processor are configured to share training data and real-time process characterization data via a local area network (LAN), an intranet, an extranet, or an internet.

7. The method of claim 1, wherein the training data set further comprises process characterization data, in-process inspection data, or post-build inspection data that is generated by a skilled operator while manually adjusting the input process control parameters.

8. The method of claim 1, wherein as part of the training of the machine learning algorithm, the machine learning algorithm randomly chooses values within a specified range for each of a set of one or more process control parameters, and incorporates the resulting process simulation data, process characterization data, in-process inspection data, or post-build inspection data into the training data set to improve a learned model that maps process control parameter values to process outcomes.

9. A system for controlling a free form deposition process or a joining process, the system comprising:

a) a first deposition or joining apparatus, wherein the deposition or joining apparatus is capable of fabricating an object based on an input design geometry;

b) one or more process characterization sensors, wherein the one or more process characterization sensors provide real-time data for one or more process parameters or object properties; and

c) a processor programmed to (i) provide a predicted optimal set of one or more input process control parameters, and (ii) to adjust one or more process control parameters in real-time based on a stream of real-time process characterization data provided by the one or more process characterization sensors, wherein the predictions and adjustments are derived using a machine learning algorithm that has been trained using a training data set.

10. The system of claim 9, wherein the first deposition or joining apparatus, the one or more process characterization sensors, and the processor are configured as: (i) a single integrated system; or (ii) as distributed system modules that share training data and real-time process characterization data via a local area network (LAN), an intranet, an extranet, or an internet.

11. The system of claim 9, wherein the training data set comprises process simulation data, process characterization data, in-process inspection data, or post-build inspection data for a plurality of objects that are the same as or different from the object of step (a).

12. The system of claim 9, wherein the one or more process characterization sensors comprise at least one laser interferometer, machine vision system, or sensor that detects electromagnetic radiation that is reflected, scattered, absorbed, transmitted, or emitted by the object.

13. The system of claim 9, wherein the one or more process control parameters are adjusted at a rate of at least 100 Hz.

14. A method for automated classification of object defects, the method comprising:

a) providing a training data set, wherein the training data set comprises fabrication process simulation data, fabrication process characterization data, in-process inspection data, or post-build inspection data for a plurality of design geometries that are the same as or different from that of the object;

b) providing one or more sensors, wherein the one or more sensors provide real-time data for one or more object properties;

c) providing a processor programmed to provide a classification of detected object defects using a machine learning algorithm that has been trained using the training data set of step (a), wherein the real-time data from the one or more sensors is provided as input to the machine learning algorithm and allows the classification of detected object defects to be adjusted in real-time.

15. The method of claim 14, further comprising removing noise from the object property data provided by the one or more sensors prior to providing it to the machine learning algorithm, wherein the noise is removed using a signal averaging algorithm, smoothing filter algorithm, Kalman filter algorithm, nonlinear filter algorithm, total variation minimization algorithm, or any combination thereof.

16. The method of claim 14, wherein the one or more sensors provide data on electromagnetic radiation that is reflected, scattered, absorbed, transmitted, or emitted by the object.

17. The method of claim 14, wherein the one or more sensors provide data on acoustic energy or mechanical energy that is reflected, scattered, absorbed, transmitted, or emitted by the object.

18. The method of claim 14, wherein the classification of detected object defects is adjusted at a rate of at least 100 Hz.

19. The method of claim 14, wherein the object defects are detected as differences between object property data and a reference data set that are larger than a specified threshold, and are classified using a one-class support vector machine (SVM) or autoencoder algorithm.

20. The method of claim 14, wherein the object defects are detected and classified using an unsupervised one-class support vector machine (SVM), autoencoder, clustering, or nearest neighbor (kNN) machine learning algorithm and a training data set that comprises object property data for defective and defect-free objects.