CINECA IRIS Institutional Research Information System

IRIS è la soluzione IT che facilita la raccolta e la gestione dei dati relativi alle attività e ai prodotti della ricerca. Fornisce a ricercatori, amministratori e valutatori gli strumenti per monitorare i risultati della ricerca, aumentarne la visibilità e allocare in modo efficace le risorse disponibili.

Many types of defectiveness can appear during the manufacturing of carbon fiber reinforced plastics (CFRP), putting at risk both safety and the quality of products.Therefore,aprotocoltochecktheintegrityofCFRPsisanimportantindustrial requirement. It should involve non-destructive testing (NDT)/non-destructive evaluation (NDE), in order to be the least invasive as possible. When exploiting ultrasonic testing, there is not a one-to-one correspondence between the type of defect and the trend of the resulting signal. Thus, visual inspection of ultrasonic signals can be a really hard task, needing a considerable experience or a suitable computing support. In the latter case, it rises the problem of ill-posedness, precisely because of the complex correspondence between defects and signal trends. The scientific literature presents a number of studies aiming to approach this problem, focusingonheuristictechniques,butcharacterizedbyhigh-computationalcomplexity. Conversely, for real-time applications, fast procedures are needed, with a low computational complexity. Experience in soft computing, even in frameworks different than NDT/NDE, can be valuable for implementing such low-time-execution algorithms. This is particularly true with respect to the handling of data affected by uncertainty and/or imprecision caused by sampling and noising of signals. Due to its nature, it is convenient to approach the classification problem as a fuzzy matter, where ultrasonic signals resulting from the same kind of defect (i.e., same class of defectiveness) have similar statistic values. That is because classification problem can be seen as a fuzzy geometrical problem, where each class is taken into account as a specific family of fuzzy sets (fuzzy hyper-rectangles) inside a fuzzy unit hypercube. Thus, an ultrasonic signal depicting an unknown defect can be mapped as a pointintotheunithyper-cubeanditbeclassifiedtherebymeansofitsdistancefrom the hyper-rectangles.

Many types of defectiveness can appear during the manufacturing of carbon fiber reinforced plastics (CFRP), putting at risk both safety and the quality of products. Therefore, a protocol to check the integrity of CFRPs is an important industrial requirement. It should involve non-destructive testing (NDT)/non-destructive evaluation (NDE), in order to be the least invasive as possible. When exploiting ultrasonic testing, there is not a one-to-one correspondence between the type of defect and the trend of the resulting signal. Thus, visual inspection of ultrasonic signals can be a really hard task, needing a considerable experience or a suitable computing support. In the latter case, it rises the problem of ill-posedness, precisely because of the complex correspondence between defects and signal trends. The scientific literature presents a number of studies aiming to approach this problem, focusing on heuristic techniques, but characterized by high-computational complexity. Conversely, for real-time applications, fast procedures are needed, with a low computational complexity. Experience in soft computing, even in frameworks different than NDT/NDE, can be valuable for implementing such low-time-execution algorithms. This is particularly true with respect to the handling of data affected by uncertainty and/or imprecision caused by sampling and noising of signals. Due to its nature, it is convenient to approach the classification problem as a fuzzy matter, where ultrasonic signals resulting from the same kind of defect (i.e., same class of defectiveness) have similar statistic values. That is because classification problem can be seen as a fuzzy geometrical problem, where each class is taken into account as a specific family of fuzzy sets (fuzzy hyper-rectangles) inside a fuzzy unit hypercube. Thus, an ultrasonic signal depicting an unknown defect can be mapped as a point into the unit hyper-cube and it be classified there by means of its distance from the hyper-rectangles.

Powered by IRIS-about IRIS-Utilizzo dei cookie

 Copyright © 2022