No abstract

No abstract

The aerospace industry is now beginning to adopt Additive Manufacturing (AM), both for new aircraft design and to help improve aircraft availability (aircraft sustainment). However, MIL-STD 1530 highlights that to certify airworthiness, the operational life of the airframe must be determined by a damage tolerance analysis. MIL-STD 1530 also states that in this process, the role of testing is merely to validate or correct the analysis. Consequently, if AM-produced parts are to be used as load-carrying members, it is important that the d a / d N versus ΔK curves be determined and, if possible, a valid mathematical representation determined. The present paper demonstrates that for AM Ti-6Al-4V, AM 316L stainless steel, and AM AerMet 100 steel, the d a / d N versus ΔK curves can be represented reasonably well by the Hartman-Schijve variant of the NASGRO crack growth equation. It is also shown that the variability in the various AM d a / d N versus Δ K curves is captured reasonably well by using the curve determined for conventionally manufactured materials and allowing for changes in the threshold and the cyclic fracture toughness terms.

A critical element for the design, characterization, and certification of materials and products produced by additive manufacturing processes is the ability to accurately and efficiently model the associated materials and processes. This is necessary for tailoring these processes to endow the associated products with proper geometrical and functional features. In an effort to address these needs in a computationally elegant and at the same time physically realistic manner, this paper presents the development of a methodology for simulating particle-based additive manufacturing processes which employs the Discrete Element Method (DEM). The details of the DEM-based methodology are presented first and the approach is demonstrated on a pair of test problems involving laser sintering of metal powders. The paper concludes with a discussion on how this approach may be generalized to broader classes of additive manufacturing systems, and details are given regarding future work which must be accomplished in order to further develop the present methodology.

No abstract

In this paper, we investigate the construction and identification of a new random field model for representing the constitutive behavior of laminated composites. Here, the material is modeled as a random hyperelastic medium characterized by a spatially dependent, stochastic and anisotropic strain energy function. The latter is parametrized by a set of material parameters, modeled as non-Gaussian random fields. From a probabilistic standpoint, the construction is first achieved by invoking information theory and the principle of maximum entropy. Constraints related to existence theorems in finite elasticity are, in particular, accounted for in the formulation. The identification of the parameters defining the random fields is subsequently addressed. This issue is attacked as a two-step problem where the mean model is calibrated in a first step, by imposing a match between the linearized model and nominal values proposed in the literature. The remaining parameters controlling the fluctuations are next estimated by solving an inverse problem in which principal component analysis and the maximum likelihood method are combined. The whole framework is illustrated considering an experimental database where multi-axial measurements are performed on a carbonepoxy laminate. This work constitutes a first step towards the development of an integrated framework that will support decision making under uncertainty for the design, certification and qualification of composite materials and structures.

One crucial component of the additive manufacturing software toolchain is a class of geometric algorithms known as “slicers.” The purpose of the slicer is to compute a parametric toolpath defined at the mesoscale and associated g-code commands, which direct an additive manufacturing system to produce a physical realization of a three-dimensional input model. Existing slicing algorithms operate by application of geometric transformations upon the input geometry in order to produce the toolpath. In this paper we introduce an implicit slicing algorithm that computes mesoscale toolpaths from the level sets of heuristics-based or physics-based fields defined over the input geometry. This enables computationally efficient slicing of arbitrarily complex geometries in a straight forward fashion. The calculation of component “infill” is explored, as a process control parameter, due to its strong influence on the produced component’s functional performance. Several examples of the application of the proposed implicit slicer are presented. It is demonstrated — via proper experimentation — that the implicit slicer can produce a mesoscale structure leading to objects of superior functional performance such as greatly increased stiffness and ultimate strength without an increase of mass. We conclude with remarks regarding the strengths of the implicit approach relative to existing explicit approaches, and discuss future work required in order to extend the methodology.

No abstract