A method for controlling a process of manufacturing an item includes making available an electronic control device (ECD) operatively associated with a processing apparatus and a central processing unit (CPU) connected to the ECD by a telecommunications network, transmitting, by the CPU, an encrypted message representative of a digital model of the item to be manufactured to the ECD, decrypting, by the ECD, the encrypted message to store the clear text digital model of the item, sending, by the ECD, an item processing start message with the digital model of the item to the processing apparatus, sending a message indicative of a status of advancement of processing of the item to the CPU, and, following reception of an item processing end message, sending to the ECD a message for deleting the clear text digital model of the item stored in the ECD.

An organic light emitting diode (OLED) automatic production equipment is provided. The OLED automatic production equipment includes a vapor deposition device, a printing device, a sputtering device, a flexible packaging device, and a thin film packaging device. The thin film packaging device is in communication with the vapor deposition device, the printing device, the sputtering device, the flexible packaging device, and the like. Processors of the vapor deposition device, the printing device, the sputtering device, and the flexible packaging device are configured to perform two-way communication with a processor of the thin film packaging device.

A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

A method of optimizing an additive manufacturing (AM) process includes receiving at least one design parameter of the AM process, receiving information relating to uncertainty in at least one other parameter of the AM process, performing uncertainty quantification in the optimization processor based on the at least one design parameters and uncertainty information to identify a shape error in an object being produced, updating the at least one design parameter of the AM process and utilizing the updated at least one design parameter in the AM process. A system for optimizing an AM process includes a design processor to produce at least one design parameter for an object to be manufactured, and an optimization processor to receive the at least one design parameter and uncertainty information to identify a shape error in the object to be manufactured and update the design parameters based on the shape error, prior or during the manufacturing process.

A method may include controlling, by a computing device, a directed energy deposition material addition (DED MA) technique based at least in part on a thermal model. The thermal model may define a plurality of default operating parameters for the DED MA technique. The method also may include detecting, by at least one sensor, at least one parameter related to the DED MA technique. Further, the method may include, 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, and controlling, by the computing device, the DED MA technique based at least in part on the updated value.

An organic light emitting diode (OLED) automatic production equipment is provided. The OLED automatic production equipment includes a vapor deposition device, a printing device, a sputtering device, a flexible packaging device, and a thin film packaging device. The thin film packaging device is in communication with the vapor deposition device, the printing device, the sputtering device, the flexible packaging device, and the like. Processors of the vapor deposition device, the printing device, the sputtering device, and the flexible packaging device are configured to perform two-way communication with a processor of the thin film packaging device.

A method for modeling additive manufacturing of a part, includes (i) constructing a model for estimating output of a simulated additive manufacturing process based upon part design, energy equation and at least one additional relationship selected from the group consisting of phase field equation, concentration equation and stress equation; (ii) entering process operating parameters into the model to produce an output; (iii) comparing the output to acceptance criteria to determine whether the output is acceptable or unacceptable; (iv) for acceptable output, adding operating parameters which resulted in the acceptable output to a process map for additive manufacturing the part; and (v) repeating steps (ii) through (iv) for different operating parameters until the process map is complete.