Predictive modeling represents an emerging field that combines existing and novel methodologies aimed to rapidly understand physical mechanisms and concurrently develop new materials, processes, and structures. In the current study, previously unexplored predictive modeling in a key-enabled technology, the laser-based manufacturing, aims to automate and forecast the effect of laser processing on material structures. The focus is centered on the performance of representative statistical and machine learning algorithms in predicting the outcome of laser processing on a range of materials. Results on experimental data showed that predictive models were able to satisfactorily learn the mapping between the laser’s input variables and the observed material structure. These results are further integrated with simulation data aiming to elucidate the multiscale physical processes upon laser–material interaction. As a consequence, we augmented the adjusted simulated data to the experiment and substantially improved the predictive performance due to the availability of an increased number of sampling points. In parallel, an information-theoretic metric, which identifies and quantifies the regions with high predictive uncertainty, is presented, revealing that high uncertainty occurs around the transition boundaries. Our results can set the basis for a systematic methodology toward reducing material design, testing, and production cost via the replacement of expensive trial-and-error based manufacturing procedures with a precise pre-fabrication predictive tool.
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14 November 2020
Research Article|
November 10 2020
Predictive modeling approaches in laser-based material processing
Special Collection:
Machine Learning for Materials Design and Discovery
Maria-Christina Velli
;
Maria-Christina Velli
1
Institute of Electronic Structure and Laser, Foundation for Research and Technology—Hellas
, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
2
Department of Physics, University of Crete
, P.O. Box 2208, 71003 Heraklion, Greece
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George D. Tsibidis
;
George D. Tsibidis
a)
1
Institute of Electronic Structure and Laser, Foundation for Research and Technology—Hellas
, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
a)Authors to whom correspondence should be addressed: tsibidis@iesl.forth.gr, pantazis@iacm.forth.gr, and stratak@iesl.forth.gr
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Alexandros Mimidis
;
Alexandros Mimidis
1
Institute of Electronic Structure and Laser, Foundation for Research and Technology—Hellas
, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
3
Department of Materials Science, University of Crete
, P.O. Box 2208, 71003 Heraklion, Greece
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Evangelos Skoulas
;
Evangelos Skoulas
1
Institute of Electronic Structure and Laser, Foundation for Research and Technology—Hellas
, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
3
Department of Materials Science, University of Crete
, P.O. Box 2208, 71003 Heraklion, Greece
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Yannis Pantazis
;
Yannis Pantazis
a)
4
Institute of Applied and Computational Mathematics, Foundation for Research and Technology—Hellas
, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
a)Authors to whom correspondence should be addressed: tsibidis@iesl.forth.gr, pantazis@iacm.forth.gr, and stratak@iesl.forth.gr
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Emmanuel Stratakis
Emmanuel Stratakis
a)
1
Institute of Electronic Structure and Laser, Foundation for Research and Technology—Hellas
, N. Plastira 100, Vassilika Vouton, 70013 Heraklion, Greece
2
Department of Physics, University of Crete
, P.O. Box 2208, 71003 Heraklion, Greece
a)Authors to whom correspondence should be addressed: tsibidis@iesl.forth.gr, pantazis@iacm.forth.gr, and stratak@iesl.forth.gr
Search for other works by this author on:
a)Authors to whom correspondence should be addressed: tsibidis@iesl.forth.gr, pantazis@iacm.forth.gr, and stratak@iesl.forth.gr
Note: This paper is part of the special collection on Machine Learning for Materials Design and Discovery
J. Appl. Phys. 128, 183102 (2020)
Article history
Received:
June 12 2020
Accepted:
October 25 2020
Citation
Maria-Christina Velli, George D. Tsibidis, Alexandros Mimidis, Evangelos Skoulas, Yannis Pantazis, Emmanuel Stratakis; Predictive modeling approaches in laser-based material processing. J. Appl. Phys. 14 November 2020; 128 (18): 183102. https://doi.org/10.1063/5.0018235
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