Fabrication, Mechanics, and Reliability Analysis for 3D Printed Lattice Designs

The use of 3D printing for lattice structures has led to advances in diverse applications benefitting from mechanically efficient designs. 3D printed lattices are often used to carry loads, however, printing defects and inconsistencies potentially hinder performance. Here, we investigate the design, fabrication, mechanics, and reliability of lattices with repeating cubic unit cells using probabilistic analysis. Lattices were designed with 500µm diameter beams and unit cell lengths from 0.8mm to 1.6mm. Lattices were printed with stereolithography and had average beam diameters from 509µm to 622µm, thereby demonstrating a deviation from design intentions. Mechanical experiments were conducted to quantify the exponential increase in yield stress for the relative density of lattices that facilitated probabilistic failure analysis. Sensitivity analysis demonstrated performance was most sensitive to fluctuations in beam diameter (74%) and less to lattice yield stress (8%) for lattices with 1.6mm unit cells while lattices with smaller 1.0mm unit cells were most sensitive to yield stress (48%) and to beam diameter (43%) fluctuations. These findings provide new insights linking design, fabrication, mechanics, and reliability analysis for improved system design that is crucial for engineers to consider as 3D printing becomes more widely adopted.

An elicitation process to quantify Bayesian networks for dam failure analysis

Bayesian networks support the probabilistic failure analysis of complex systems, e.g. dams and bridges, needed for a better understanding of the system reliability and for taking mitigation actions. Bayesian networks are useful in representing the interactions among system components graphically, while the quantitative strength of the interrelationships between the variables is measured using conditional probabilities. However, due to a lack of objective data it often becomes necessary to rely on expert judgment to provide subjective probabilities to quantify the model. This paper proposes an elicitation process that can be used to support the collection of valid and reliable data with the specific aim of quantifying a Bayesian Network, while minimizing the adverse impact of biases. To illustrate how this framework works, it is applied to a real-life case study regarding the safety of the Mountain Chute Dam and Generating Station, which is located on the Madawaska River in Ontario, Canada.