Machine Learning Inversion of Layer-Wise Plasticity and Interfacial Cohesive Parameters in Multilayer Thin Films.

Baorui Liu, Shuyue Liu, Kaiwei Xing, Zhifei Tan, Jianru Wang, Peng Cao

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Article in Materials (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Baorui LiuScience and Technology on Reliability and Environment Engineering Laboratory, Beijing Institute of Structure and Environment Engineering, Beijing 100076, China.

Shuyue LiuCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.ORCID 0009-0008-6885-7218

Kaiwei XingCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.

Zhifei TanDepartment of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Kowloon, Hong Kong.ORCID 0000-0002-6506-4465

Jianru WangAcademy of Aerospace Solid Propulsion Technology, Xi'an 710025, China.

Peng CaoCollege of Architecture and Civil Engineering, Beijing University of Technology, Beijing 100124, China.ORCID 0000-0001-5645-734X

Funding

National Key Research and Development Program of China 2021YFB3801700

6 · The paper itself

Abstract

This study proposes a fast material parameter evaluation method for multilayer thin-film structures based on machine learning technology to solve the problems of long time and low efficiency in the traditional material parameter inversion process. Nanoindentation experiments are first conducted to establish an experimental basis across film stacks. A two-dimensional elasto-plastic model of the indentation process is then built to generate a large set of load-depth curves, which serve as training data for a machine learning model. Trained on simulated curves and validated against measurements, the model enables fast inverse identification of layer-wise plastic parameters and interfacial cohesive properties. The experimental results show that the method has high accuracy and efficiency in the inversion of interlayer cohesion parameters, and the correlation coefficient R

Indexed as

cohesion parametermachine learningnanoindentationparameter back analysis

Identifiers

PMID41227934

DOI10.3390/ma18214976

PMCPMC12611003

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.