Acoustic emission (AE) is a common nondestructive evaluation tool that has been used to monitor fracture in materials and structures. The direct connection between AE events and their source, however, is difficult because of material, geometry and sensor contributions to the recorded signals. Moreover, the recorded AE activity is affected by several noise sources which further complicate the identification process. This article uses a combination of in situ experiments inside the scanning electron microscope to observe fracture in an aluminum alloy at the time and scale it occurs and a novel AE signal processing framework to identify characteristics that correlate with fracture events. Specifically, a signal processing method is designed to cluster AE activity based on the selection of a subset of features objectively identified by examining their correlation and variance. The identified clusters are then compared to both mechanical and in situ observed microstructural damage. Results from a set of nanoindentation tests as well as a carefully designed computational model are also presented to validate the conclusions drawn from signal processing.

Damage initiation and progression in precipitate hardened alloys are typically linked to the failure of second phase particles that result from the precipitation process. These particles have been shown to be stress concentrators and crack starters as a result of both particle debonding and fracture. In this investigation, a precipitate hardened aluminium alloy (Al 2024‐T3) is loaded monotonically to investigate the role the particles have in the progressive failure process. The damage process was monitored continuously by combining the acoustic emission method either with in situ scanning electron microscopy or X‐ray microcomputed tomography to obtain both surface and volume microstructural information. Particles were observed to fracture only in the elastic regime of the material response, while void growth at locations predominantly near particles were found to be associated with progressive failure in the plastic region of the macroscopic response. Experimental findings were validated by fracture simulations at the scale of particle‐matrix interface.

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The reliability and performance qualification of additively manufactured metal parts is critical for their successful and safe use in engineering applications. In current powder-bed fusion type metal additive manufacturing processes, local thermal accumulations affect material microstructure features, overall part quality and integrity, as well as bulk mechanical behavior. To address such challenges, the investigation presented in this manuscript describes a novel digital design approach combining topology optimization, process simulations, and lattice size optimization to address local thermal effects caused during manufacturing. Specifically, lattices are introduced in regions of topology optimized geometries where local thermal accumulations are predicted using the process simulations with the overall goal to mitigate high thermal gradients. The results presented demonstrate that the proposed digital design approach reduces local thermal accumulations while achieving target mechanical performance metrics. A discussion on how post-manufacturing heat treatment effects could be also considered, as well as comments on the computational implementation of the proposed approach are provided.