Digital-Twin-Assisted Additive Manufacturing for Value Chain Networks

Abstract

An autonomous additive manufacturing platform includes sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part. An adaptive intelligence system is configured to receive the sensor data from the sensors. The adaptive intelligence system includes a machine learning system configured to input the sensor data as training data into one or more machine learning models. The machine learning models are configured to transform the sensor data into simulation data. A digital twin system is configured to create a part twin based on the simulation data. The part twin provides for representation of the part and simulation of a possible future state of the part via the simulation data. An artificial intelligence system is configured to execute simulations on the digital twin system. The machine learning models are utilized to make classifications, predictions, and other decisions relating to the part.

Claims

1. An autonomous additive manufacturing platform comprising: a plurality of sensors positioned in, on, and/or near a part and configured to collect sensor data related to the part, wherein the sensor data is substantially real-time sensor data; and an adaptive intelligence system connected to the plurality of sensors and configured to receive the sensor data from the plurality of sensors, wherein the adaptive intelligence system includes: a machine learning system configured to input the sensor data into one or more machine learning models, wherein the sensor data is used as training data for the machine learning models, and wherein the machine learning models are configured to transform the sensor data into simulation data; a digital twin system configured to create a part twin based on the simulation data, wherein the part twin provides for substantially real-time representation of the part and provides for simulation of a possible future state of the part via the simulation data; and an artificial intelligence system configured to execute simulations on the digital twin system, wherein the machine learning models are utilized by the artificial intelligence system to make classifications, predictions, and other decisions relating to the part.

2. The autonomous additive manufacturing platform of claim 1 wherein the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting part shrinkage.

3. The autonomous additive manufacturing platform of claim 1 wherein the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting part warpage.

4. The autonomous additive manufacturing platform of claim 1 wherein the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for calculating necessary changes to an additive manufacturing process of the autonomous additive manufacturing platform to compensate for part shrinkage and warpage.

5. The autonomous additive manufacturing platform of claim 1 wherein the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for testing compatibility of 3D printed parts with other parts or with a 3D printer.

6. The autonomous additive manufacturing platform of claim 1 wherein the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for predicting deformations or failure in a 3D printed part.

7. The autonomous additive manufacturing platform of claim 1 wherein the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the part twin for optimizing a build process of the autonomous additive manufacturing platform to minimize occurrence of deformations.

8. The autonomous additive manufacturing platform of claim 1 wherein: the digital twin system is configured to create a product twin based on the simulation data, the product twin provides for substantially real-time representation of a product including the part and provides for simulation of a possible future state of the product via the simulation data, and the machine learning models trained by the machine learning system are utilized by the artificial intelligence system to execute simulations on the product twin for predicting at least one of cost of the product and price of the product.

9. A computer-implemented method for facilitating manufacture and delivery of a 3D printed product to a customer using a set of manufacturing nodes of a distributed manufacturing network, the method comprising: receiving one or more product requirements from the customer; tokenizing and storing the product requirements in a distributed ledger system; determining one or more manufacturing nodes, printers, processes, and materials based on the product requirements; generating a quote including pricing and delivery timelines; and upon acceptance of the quote by the customer, manufacturing, and delivering the 3D printed product to the customer.

10. The method of claim 9 further comprising rating one or more of the set of manufacturing nodes based on a customer satisfaction score for meeting customer requirements.

11. The method of claim 9 wherein the determining includes matching a customer order with one of the set of manufacturing nodes based on factors including at least one of printer capabilities, locations of the customer and the manufacturing nodes, available capacity at each node, pricing requirements, timing requirements, and a customer satisfaction score.

12. The method of claim 9 wherein at least one node of the set of manufacturing nodes includes a 3D printer.