Timely part procurement is vital to the maintenance and performance of deployed military equipment. Yet, logistical hurdles can delay this process, which can compromise efficiency and mission success for the warfighter. Point-of-need part procurement through additive manufacturing (AM) is a means to circumvent these logistical challenges. An Integrated Computational Materials Engineering framework is presented as a means to validate and quantify the performance of AM replacement parts. Statistical modeling using a random forest network and finite element modeling were to inform the build design. Validation was performed by testing coupons extracted from each legacy replacement parts, as well as the new additively manufactured replacement parts through monotonic tensile and combined tension-torsion fatigue testing. Destructive full hinge assembly tests were also performed as part of the experimental characterization. Lastly, the collected experimental results were used to iterate on the part design to showcase the advantages AM offers for meeting new service requirements.