Post-build detection, in-situ sensing, and prevention of defects in powder bed fusion additive manufacturing (PBFAM) are of significant interest to both researchers and end-users of the technology. However, these efforts have been stifled by a lack of an ability to reliably produce voids which are characteristic of natural ones. The goal of our work is to develop and implement control methods for producing defects characteristic of lack of fusion, keyholing, and spatter particles becoming entrained in the meltpool. Opposed to injecting flaws by altering the CAD inputted into the printer, in-situ parameter perturbations methods are designed which recreate the effect of spatter-melt interactions, spatter-laser interactions, melt pool coalescence of neighboring tracks, and rapid variations in energy input. These methods are implemented on a laser powder bed fusion machine, as it builds Ti-6Al-4V components, with otherwise optimized (default) processing parameters. After the build, the size, distribution, and morphology of the purposefully induced defects are confirmed with high-resolution X-ray computed tomography, demonstrating the ability to produce defects of known size and morphology at known locations. Statistical frameworks relating deviations in processing parameters to the morphologies and locations of each of the flaw types are developed, further improving the precision of induced flaws’ locations and sizes.