Mary Kathryn Thompson

Design for Additive Manufacturing: Trends, opportunities, considerations, and constraints

The past few decades have seen substantial growth in Additive Manufacturing (AM) technologies. However, this growth has mainly been process-driven. The evolution of engineering design to take advantage of the possibilities afforded by AM and to manage the constraints associated with the technology has lagged behind. This paper presents the major opportunities, constraints, and economic considerations for Design for Additive Manufacturing. It explores issues related to design and redesign for direct and indirect AM production. It also highlights key industrial applications, outlines future challenges, and identifies promising directions for research and the exploitation of AM’s full potential in industry.

Considerations for the incorporation of measured surfaces in finite element models.

This work discusses some of the benefits, techniques, challenges, and considerations associated with the incorporation of measured surfaces in finite element (FE) models including how much surface data to measure and import into the model, the shape of the surface geometry to create, the presence and effect of surface layers and impurities, the required mesh density for rough surfaces, the nature of the element formulations and material properties at small length scales, the differences between measurement and FE coordinate systems, the limitations and idealizations of the FE method, issues associated with boundary conditions and their ability to impose or prevent conformal contact, and issues associated with the size of the pinball region and the contact stiffness relative to the nature of the surface. It also describes some current and future research directions that can be used to validate and expand existing techniques and to improve our understanding of surface phenomena.

A comparison of methods to evaluate the behavior of finite element models with rough surfaces

Finite element (FE) modeling of rough surfaces is becoming increasingly common. However, the quality of the assumptions being made in these models, and thus the quality of the models themselves, is often unclear. Decisions about the geometry of the surface to be modeled, including the size of the surface to be modeled, the lateral resolution of the measured surface data to be used, and the formulation of the probabilistic surface to be used, can have a significant effect on a models behavior. Similarly, varying model parameters, including the FE mesh density, can change the results by a factor of three or more. This work examines some of the metrics that can be used to evaluate the influence of these assumptions and parameters on FE models with rough surfaces and discusses the relative merits of each option. In particular, qualitative comparison of result plots, quantitative comparison and convergence of results parameters, qualitative and quantitative comparison of distributions of result values over various model dimensions, and more sophisticated comparison techniques inspired by image and signal processing are discussed.

Finite Element Modeling of Multi-Scale Thermal Contact Resistance

Many traditional macro scale finite element models of thermal contact systems have incorporated the effect of micro scale surface topography by applying a constant value of thermal contact conductance (TCC) per unit area to the regions in contact. However, it has been very difficult to determine an appropriate TCC value for a given system and analysts typically had to rely on experimental data or values from the literature. This work presents a method for predicting micro scale TCC per unit area by incorporating micro scale surface roughness in a multi-scale iterative thermal/structural finite element contact model. The resulting TCC value is then used in a macro scale thermal/structural contact model with apparent surface form to predict the thermal contact resistance and overall thermal resistance for a commercial power electronics module.Copyright

Characterizing Digital Light Processing (DLP) 3D Printed Primitives

The resolution and repeatability of 3D printing processes depends on a number of factors including the software, hardware, and material used. When printing parts with features that are near or below the nominal printing resolution, it is important to understand how the printer works. For example, what is the smallest unit shape that can be produced? And what is the reproducibility of that process? This paper presents a method for automatically detecting and characterizing the height, width, and length of micro scale geometric primitives produced via a digital light processing (DLP) 3D printing process. An upper limit, lower limit, and best estimate for each dimension is reported for each primitive. Additionally, the roughness, rectangularity, and tilt of the top of each primitive is estimated. The uncertainty of the best estimate is indicated using standard deviations for a series of primitives. The method generalizes to unseen primitives, and the results illustrate that the dimension estimates converge as the size of the primitives increases. The primitives’ rectangularity also increases as the size increases. Finally, the primitives specified with 5 to 68\(\mu m\) varying heights have been estimated to group into five different heights with fairly low variance of the best estimates of the heights. This reflects how the requested geometry is parsed and produced by the printer.

Functional, Technical and Economical Requirements Integration for Additive Manufacturing Design Education

Additive Manufacturing (AM) enables designers to consider the benefits of digital manufacturing from the early stages of design. This may include the use of part integration to combine all required functions, utilizing multiple materials, moving assemblies, different local properties such as colour and texture, etc. Cost analysis can also be factored in throughout the entire value chain, from design to the finishing operations in comparison to traditional processes and conventional ways of working. Therefore, the concept of Design for Additive Manufacturing (DfAM) is more than a geometrical issue on a CAD system, and not limited only to topological optimization or lattice integration.

Structural Analysis of a Simple Warren Truss Using Direct Generation

In this exercise, you will create a finite element model of a simple Warren truss using direct generation of the nodes and elements. The truss will be modeled using spar (truss) elements. This allows uniaxial tension and compression within the members, but no bending of the members. All joints are pinned and can rotate freely. The pin in the lower left corner of the truss is fixed in space. The lower right corner of the truss is supported by rollers. A downward force of is applied to the bottom center joint of the truss. Because there are no out-of-plane boundary conditions, the truss will be modeled in 2D. The goal of the analysis is to determine the displacements of the various joints in the truss and the component forces for the members of the truss. Given these assumptions and boundary conditions, the truss is statically determinate. All displacements and forces can be calculated by hand. Selected results are calculated at the end of the exercise for verification.

Top-Down Solid Modeling of a Pipe Flange Using Symmetry

In this exercise, you will create a finite element model of a pipe flange using top down solid modeling techniques. Although the flange itself is axisymmetric, the bolt circle is not. Instead, you will take advantage of the fact that the part is symmetric about two axes and model only 90 degrees of the pipe flange. The fluid inside the pipe is assumed to be at 95°C with a convective heat transfer coefficient of 1000 W/m 2 K. The pipe is cooled via natural convection with a heat transfer coefficient of 20 W/m 2 K. The surrounding air is at 25°C. The goal is to find the steady state distribution of the temperature and the thermal flux in the pipe flange.

Chapter 8 – Solution

This chapter provides an overview of the activities performed in the solution processor. You will learn how the term “solution” is used in ANSYS. You will learn how to apply boundary conditions and initial conditions to your model. You will learn how to set the solution options, initiate a solution, interpret the solution feedback provided by ANSYS, and terminate the solution if necessary. Finally, you will learn how to protect the results of your analysis and append new results to the results file.

Chapter 9 – Postprocessing

This chapter provides an overview of postprocessing in ANSYS. You will learn about the types of results that are and can be calculated in ANSYS, where those results are stored, and how to access them. Next, you will learn about various results display options. You will learn how to list, plot, graph, animate, operate on, and combine results, and how to save postprocessing graphics and information. Finally, you will learn about model verification and validation.