A machine learning approach is used for analysis of pin solder joints for electronic devices after tests based on cyclic thermal stresses. Metrological concepts, in particular budget uncertainty, are used to reinterpret expert's judgements, to individuate the variability causes of errors of a semi-automated evaluation process of x-ray images, aiming to replicate the expert's judgement. The actions are also described for reduction to an acceptable level of the error percentage with respect to the faulted specimens identification. In this way a tailored approach is set, which is able to remarkably reduce the errors and to evaluate the most significant contributions to the variability of results. The suggestions deriving from this study could be useful also for different applications.
A proposal of uncertainty assessment for data of environmental testing: Using a machine learning procedure / Giulio D'Emilia ; David Di Gasbarro ; Marcantonio Catelani ; Lorenzo Ciani. - STAMPA. - (2018), pp. 1-6. (Intervento presentato al convegno 2018 IEEE International Instrumentation and Measurement Technology Conference, I2MTC 2018 tenutosi a Houston; United States nel 14 May 2018 through 17 May 2018) [10.1109/I2MTC.2018.8409597].
A proposal of uncertainty assessment for data of environmental testing: Using a machine learning procedure
Marcantonio Catelani;Lorenzo Ciani
2018
Abstract
A machine learning approach is used for analysis of pin solder joints for electronic devices after tests based on cyclic thermal stresses. Metrological concepts, in particular budget uncertainty, are used to reinterpret expert's judgements, to individuate the variability causes of errors of a semi-automated evaluation process of x-ray images, aiming to replicate the expert's judgement. The actions are also described for reduction to an acceptable level of the error percentage with respect to the faulted specimens identification. In this way a tailored approach is set, which is able to remarkably reduce the errors and to evaluate the most significant contributions to the variability of results. The suggestions deriving from this study could be useful also for different applications.I documenti in FLORE sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.