Additive manufacturing is a growing field dominated by printing processes that soften and re-solidify material, depositing this material layer by layer to form the printed shape. Increasingly, researchers are pursuing new materials to enable fabrication of a wider variety of associated capabilities. This includes fabrication with high-viscosity materials of many new classes of material compositions, such as doping for magnetic or electrically conducting polymers. These additives complicate the materials deposition process by requiring complex, non-linear calibration to synchronize these new candidate materials with the additive manufacturing software and hardware. In essence, additive manufacturing is highly dependent on identifying the delicate balance between materials properties, hardware, and software-which is currently realized via a time-consuming and costly iterative calibration process. This thesis is concerned with reducing this cost of calibration, in particular by providing a time-based metric based on material viscosity for material retraction at the conclusion of each extrusion. It presents a novel non-contact method of determining the material retraction rate (during reversal of extrusion), by measuring the variation in laser intensity resulting from the deformation of the material reservoir due to change in material pressure. Commercially available laser measurement systems cost more than $20,000 and are limited to 1 μm at a 300 ms (3 Hz) sampling rate. The experimental setup presented in this thesis costs less than $100 and is capable of taking measurements of 1 - 2 μm at a 0.535 ms (1870 Hz) sampling rate. For comparison, the stepper motor driving the material extruder operates at 0.667 ms (1500 Hz). Using this experimental setup, an inverse correlation is shown to exist between the viscosity of a material and the rate at which the material is retracted. Using this correlation and a simplified material analysis process, one can approximate the retraction time necessary to calibrate new materials, thereby significantly improving initial estimated calibration settings, and thus reducing the number of calibration iterations required to ready a new material for additive manufacturing. In addition, the insight provided into the material response can also be used as the basis for future research into minimizing the calibration process.