Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems

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@Article{DANGELO:2020:FGCS,

• author = "Gianni D'Angelo and Francesco Palmieri",
• title = "Knowledge elicitation based on genetic programming for non destructive testing of critical aerospace systems",
• journal = "Future Generation Computer Systems",
• volume = "102",
• pages = "633--642",
• year = "2020",
• ISSN = "0167-739X",
• DOI = "doi:10.1016/j.future.2019.09.007",
• URL = "http://www.sciencedirect.com/science/article/pii/S0167739X19306193",
• keywords = "genetic algorithms, genetic programming, Evolutionary algorithm, Genetic algorithms (GA), Genetic programming (GP), Symbolic regression (SR), Artificial intelligence, Machine learning, Non-destructive testing (NDT), Eddy-current testing (ECT), Composite materials, Carbon-fiber reinforced plastic (CFRP), Carbon-fiber reinforced aluminum (FRA)",
• abstract = "In non-destructive testing of aerospace structures' defects, the tests reliability is a crucial issue for guaranteeing security of both aircrafts and passengers. Most of the widely recognized approaches rely on precision and reliability of testing equipment, but also the methods and techniques used for processing measurement results, in order to detect defects, may heavily influence the overall quality of the testing process. The effectiveness of such methods strongly depends on specific field knowledge that is definitely not easy to be formalized and codified within the results processing practices. Although many studies have been conducted in this direction, such issue is yet an open-problem. This work describes the use of Genetic Programming for the diagnosis and modeling of aerospace structural defects. The resulting approach aims at extracting such knowledge by providing a mathematical model of the considered defects, which can be used for recognizing other similar ones. Eddy-Current Testing has been selected as a case study in order to assess both the performance and functionality of the whole framework, and a publicly available dataset of specific measures for aircraft structures has been considered. The experimental results put into evidence the effectiveness of the proposed approach in building reliable models of the aforementioned defects, so that it can be considered a successful option for building the knowledge needed by tools for controlling the quality of critical aerospace systems",

}

Genetic Programming entries for Gianni D'Angelo Francesco Palmieri

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