NDLI: Failure Mode Clustering is Electronic Assemblies Using Sammon’s Mapping With Supervised Training of Perceptrons

Content Provider	The American Society of Mechanical Engineers (ASME) Digital Collection
Author	Lall, Pradeep Gupta, Prashant Goebel, Kai
Copyright Year	2011
Abstract	An anomaly detection and failure mode classification method has been developed for electronic assemblies with multiple failure modes. The presented prognostic health management method targets the pre-failure space of the electronic assembly life to trigger repair or replacement of impending failures. Presently, health monitoring systems focus on reactive diagnostic detection of failure modes. Examples of diagnostic detection include the built in self test and on-board diagnostics. In this paper, damage pre-cursors from time-spectral measurements of the electronic assemblies has been measured under applied vibration and shock stimulus. The time-evolution of spectral content of the damage pre-cursors has been studied using joint time frequency analysis in a full-field manner on the printed circuit assembly. Frequency moments have been used to build a feature vector. Evolution of the feature vector with damage initiation and progression has been studied under shock and vibration. The feature vector from multiple locations in the board assemblies has been mapped into a de-correlated feature space using Sammon’s mapping. Several chip-scale packages have been studied, with SAC305 and SAC405 leadfree second-level interconnects. Transient strain has been measured during the drop-event using digital image correlation and high-speed cameras operating at 100,000 fps. Continuity has been monitored simultaneously for failure identification. In addition, explicit finite element models have been developed and various kinds of failure modes have been simulated such as solder ball cracking, trace fracture, package falloff and solder ball failure. The neural net has been trained using simulated data-sets created from error-seeded models with specific failure modes. The neural net has then been used to identify and classify the failure modes in board assemblies experimentally. Supervised learning of multilayer neural net in conjunction with parity has been used to identify the hard-separation boundaries between failure mode clusters in the de-correlated feature space. The assemblies have been cross-sectioned to verify the identified failure modes. Cross-sections indicate that the experimentally measured failures modes correlate well with the position of the cluster in the de-correlated feature space.
Starting Page	911
Ending Page	925
Page Count	15
File Format	PDF
ISBN	9780791854976
DOI	10.1115/IMECE2011-65833
Volume Number	Volume 11: Nano and Micro Materials, Devices and Systems; Microsystems Integration
Conference Proceedings	ASME 2011 International Mechanical Engineering Congress and Exposition
Language	English
Publisher Date	2011-11-11
Publisher Place	Denver, Colorado, USA
Access Restriction	Subscribed
Subject Keyword	Time-frequency analysis Solders Vibration Artificial neural networks Maintenance Parity (physics) Separation (technology) Transients (dynamics) Fracture (process) Errors Cross section (physics) Finite element model Monitoring systems Manufacturing Damage Shock (mechanics) Failure Printed circuit board assemblies Failure mechanisms
Content Type	Text
Resource Type	Article

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