US 12,138,867 B2
System and method for a heating operation process control
Keith D. Humfeld, Federal Way, WA (US); Geoffrey A. Butler, Seattle, WA (US); and Karl M. Nelson, Issaquah, WA (US)
Assigned to THE BOEING COMPANY, Arlington, VA (US)
Filed by THE BOEING COMPANY, Chicago, IL (US)
Filed on Oct. 20, 2021, as Appl. No. 17/505,875.
Claims priority of provisional application 63/196,309, filed on Jun. 3, 2021.
Claims priority of provisional application 63/136,742, filed on Jan. 13, 2021.
Prior Publication US 2022/0219411 A1, Jul. 14, 2022
Int. Cl. G05B 13/02 (2006.01); B29C 70/44 (2006.01); B29C 70/54 (2006.01); G06N 3/08 (2023.01)
CPC B29C 70/44 (2013.01) [B29C 70/54 (2013.01); G05B 13/0265 (2013.01); G06N 3/08 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method comprising:
obtaining sensor data indicating measured temperatures within a heating vessel during a first portion of a heating operation, wherein the sensor data includes tool temperature values and interior temperature values, wherein a tool temperature value represents a temperature measurement of a portion of a tool within the heating vessel, and wherein an interior temperature value represents a temperature measurement of ambient conditions within the heating vessel;
determining a plurality of sets of thermal stack parameters from a plurality of sets of candidate thermal stack parameters, wherein each set of candidate thermal stack parameters is descriptive of a respective configuration of an in-process thermal stack modeled by a first machine learning model to generate one or more estimated tool temperature values, wherein the in-process thermal stack comprises the tool and a part coupled to the tool, and wherein for each set of candidate thermal stack parameters from the plurality of sets of candidate thermal stack parameters:
providing input to the first machine learning model, wherein the input indicates the set of candidate thermal stack parameters and a time sequence of the interior temperature values; and
obtaining output from the first machine learning model, wherein the output indicates one or more estimated tool temperature values based on the input; and
selecting, as the plurality of sets of thermal stack parameters, a subset of the plurality of sets of candidate thermal stack parameters for which the one or more estimated tool temperature values corresponds to the tool temperature values indicated by the sensor data;
determining a temperature profile for a second portion of the heating operation, wherein the temperature profile is determined, via a second machine learning model, based on the plurality of sets of thermal stack parameters and one or more process specifications of the in-process thermal stack;
sending, based on the temperature profile, one or more commands to the heating vessel; and
modifying, based on the one or more commands, one or more components of the heating operation of the heating vessel.