	US 12,275,038 B2
	Paint repair process by scenario
Nicholas D. Richardson, Mahtomedi, MN (US); Brett R. Hemes, Woodbury, MN (US); Mark W. Orlando, Chesterfield Township, MI (US); Juan A. Munoz, Inver Grove Heights, MN (US); Sarah M. Mullins, St. Paul, MN (US); and Matthew H. Purdin, Minneapolis, MN (US)
Assigned to 3M Innovative Properties Company, St. Paul, MN (US)
Appl. No. 17/425,035
Filed by 3M INNOVATIVE PROPERTIES COMPANY, St. Paul, MN (US)
PCT Filed Aug. 23, 2019, PCT No. PCT/IB2019/057107 § 371(c)(1), (2) Date Jul. 22, 2021, PCT Pub. No. WO2020/161534, PCT Pub. Date Aug. 13, 2020.
Claims priority of provisional application 62/801,310, filed on Feb. 5, 2019.
Prior Publication US 2022/0126319 A1, Apr. 28, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. B05D 5/00 (2006.01); B05D 3/12 (2006.01); B25J 9/16 (2006.01); B25J 11/00 (2006.01); G01N 21/88 (2006.01); G05B 13/02 (2006.01); G05B 19/408 (2006.01); G05B 19/418 (2006.01)

CPC B05D 5/005 (2013.01) [B05D 3/12 (2013.01); B25J 9/163 (2013.01); B25J 11/0065 (2013.01); G05B 13/0265 (2013.01); G05B 19/4083 (2013.01); G05B 19/41875 (2013.01); B25J 11/0075 (2013.01); G01N 21/88 (2013.01); G05B 2219/45013 (2013.01); G05B 2219/45065 (2013.01); Y02P 90/02 (2015.11)]

5 Claims

1. A method of automated abrasive repair, comprising:

receiving, by one or more processors, pre-inspection data comprising:

(i) coordinates of a defect in a substrate such that a robot manipulator can bring an end effector mounted on the robot manipulator into close proximity with the defect, wherein the end effector comprises:

i. end effector sensors including at least one camera to collect data comprising at least one of fringe pattern projection, deflectometry, and intensity measurements of diffuse reflected or normal white light; and

ii. automated abrasive paint repair devices comprising at least one of a sanding tool, a buffing/polishing tool, and a compliant force flange;

(ii) at least one of a defect type from a classifier, approximate volumetric information describing the defect, and substrate material information;

moving the robot manipulator to bring the end effector into close proximity with the defect based on the coordinates of the defect;

collecting local in-situ inspection data comprising at least one of fringe pattern projection, deflectometry, and intensity measurements of diffuse reflected or normal white light by triggering the end effector sensors;

receiving, by the one or more processors, parameters describing characteristics of the defect determined from the pre-inspection data and the local in-situ inspection data;

selecting, by the one or more processors, at least one of a sanding process, a buffing process, a polishing process, or combinations thereof to repair the defect based on a parameter selection algorithm that selects parameters from stored empirically derived rules and stored parameters according to empirically engineered parametric functions of the received parameters;

instructing, by the one or more processors, the automated abrasive repair devices to execute the selected process;

repairing the defect by executing, with the automated repair devices, the selected process; and

providing, by the one or more processors, the local in-situ inspection data to a machine learning unit;

using, by the machine learning unit, the local in situ inspection data to create learning updates by updating the stored empirically derived rules, wherein the updated stored empirically derived rules are used to improve future automated abrasive repair actions; and

after repairing the defect:

collecting post-inspection data comprising at least one of fringe pattern projection, deflectometry, and intensity measurements of diffuse reflected or normal white light by triggering the end effector sensors;

receiving, by the one or more processors, the post-inspection data and evaluating the repair of the defect; and

providing, by the one or more processors, the post-inspection data to the machine learning unit along with the pre-inspection data and the local in-situ inspection data; and

using, by the machine learning unit, the post-inspection data, the pre-inspection data, and the local in situ inspection data to create learning updates by updating the stored empirically derived rules, wherein the updated stored empirically derived rules are used to improve future automated abrasive repair actions.