US 12,467,874 B2
Qualitative or quantitative characterization of a coating surface
Philipp Isken, Altenberge (DE); Sandra Bittorf, Witten (DE); Oliver Kroehl, Cologne (DE); Claudia Bramlage, Essen (DE); Markus Vogel, Kamp-Lintfort (DE); Stefan Silber, Krefeld (DE); Gaetano Blanda, Haltern am See (DE); Olivia Lewis, Berlin (DE); and Daniel Haake, Potsdam (DE)
Assigned to EVONIK OPERATIONS GMBH, Essen (DE)
Filed by EVONIK OPERATIONS GMBH, Essen (DE)
Filed on Sep. 16, 2021, as Appl. No. 17/476,983.
Claims priority of application No. 20196660 (EP), filed on Sep. 17, 2020.
Prior Publication US 2022/0082508 A1, Mar. 17, 2022
Int. Cl. G01N 21/88 (2006.01); B25J 9/16 (2006.01); G01N 21/84 (2006.01); G05B 19/418 (2006.01); G06F 16/51 (2019.01); G06N 20/00 (2019.01); G06T 7/00 (2017.01); G06T 7/11 (2017.01); G16C 20/30 (2019.01); G16C 20/70 (2019.01); G16C 60/00 (2019.01)
CPC G01N 21/8851 (2013.01) [B25J 9/1697 (2013.01); G01N 21/8422 (2013.01); G01N 21/8806 (2013.01); G05B 19/41875 (2013.01); G06F 16/51 (2019.01); G06N 20/00 (2019.01); G06T 7/0004 (2013.01); G06T 7/001 (2013.01); G06T 7/11 (2017.01); G16C 60/00 (2019.02); G01N 2021/8427 (2013.01); G01N 2021/8854 (2013.01); G01N 2021/8861 (2013.01); G01N 2021/8864 (2013.01); G01N 2021/8874 (2013.01); G01N 2021/888 (2013.01); G01N 2021/8887 (2013.01); G05B 2219/37206 (2013.01); G05B 2219/37451 (2013.01); G05B 2219/45013 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/30108 (2013.01); G06T 2207/30156 (2013.01); G16C 20/30 (2019.02); G16C 20/70 (2019.02); Y02P 90/02 (2015.11)] 15 Claims
OG exemplary drawing
 
1. A computer-implemented method for providing a coating composition-related prediction program, the method comprising:
providing a database comprising associations of one or more of qualitative and quantitative characterizations of coating surfaces and one or more parameters selected from a group comprising one or more of: one or more of components of a coating composition used for producing a respective coating surface; one or more of relative and absolute amounts of one or more of the components; manufacturing-process parameters of the coating composition; and application-process parameters used for creating the coating surfaces;
training a machine learning model on the associations of the coating surface characterizations with the one or more parameters in the database for providing a predictive model having learned to correlate one or more of the qualitative and the quantitative characterizations of one or more coating surfaces with one or more of the parameters stored in association with respective coating surface characterizations; and one or more of:
providing a composition-quality-prediction program which comprises a first predictive model, the composition-quality-prediction program being configured for using the first predictive model for predicting the properties of a coating surface to be produced from one or more input parameters selected from a group comprising one or more of: one or more components of the coating composition to be used for producing the coating surface; one or more of relative and absolute amounts of one or more of the components; manufacturing-process parameters to be used for preparing the coating composition; application-process parameters to be used for creating the coating surface; and
providing a composition-specification-prediction program which comprises a second predictive model, the composition-specification-prediction program being configured for using the second predictive model for predicting, based on an input specifying at least a desired coating surface characterization, one or more output parameters related to the coating composition predicted to generate the coating surface having input surface characterizations, the one or more output parameters being selected from a group comprising one or more of: one or more components of the coating composition; one or more of relative and absolute amounts of one or more of the components; manufacturing-process parameters to be used for preparing the coating composition; and application-process parameters for creating the coating surface,
wherein the training the machine learning model comprises using an active learning module for selecting a candidate coating composition specification from a set of candidate composition specifications which provides the strongest learning effect for the first predictive model and the second predictive model, the learning effect being provided as a result of the preparation of the selected candidate composition, empirical measurement of properties of the selected candidate composition, and retraining of one or more of the first predictive model and the second predictive model on training data comprising the empirically measured properties of the selected and prepared candidate composition,
wherein the selection of the candidate coating composition specification by the active learning module comprises
for each of the multiple candidate coating composition specifications:
predicting properties of a coating surface of the specified candidate coating composition using the first predictive model of the composition-quality-prediction program; and
determining an uncertainty score being indicative of the uncertainty of the first model with respect to the predicting; and
using the one of the multiple candidate coating compositions having the maximum uncertainty score as the selected candidate coating composition specification.