A total of 354 porcine cranial fracture samples have been produced in controlled biomechanical-impact experiments. Subsequently, the cranial-fracture diagrams were uploaded to a fracture printing interface (FPI). The FPI automatically extracts characteristic features of the fracture patterns. Depending on specimen age and the user's particular request, the FPI can predict one or more class labels. To date, the class labels are impact energy level (high or low); head constraint condition (entrapped or free-fall); and impact surface (rigid, compliant, or carpet). The current research evaluated the performance of the FPI in various sub-tasks. The experimentally controlled data enabled researchers to use classification accuracy as a measure of performance for the FPI. Overall, the FPI achieved reasonably high levels of accuracy in categorizing cranial fractures into classes based on constraint condition, impact, surface, and impact energy. For a fixed impact energy level, the FPI showed an accuracy of 81-85 percent in predicting constraint condition for a compliant surface and 92-94 percent for a rigid surface. For a free-fall on a rigid surface, the FPI accurately predicted the associated impact energy level (high or low) 86-95 percent of the time. A pilot study was also conducted on 100 human pediatric cranial fracture patterns from forensic cases of child deaths. The initial study has shown that the models and algorithms from the FPI developed from porcine data could be transformed in classifying human pediatric cranial fracture patterns into categories of homicides or high-energy accidents with an accuracy of 75 percent. Implications for criminal justice policy and practice are discussed. Appended listing of scholarly products