Using Design of Experiments in Finite Element Modeling to Identify Critical Variables for Laser Powder Bed Fusion

Input of accurate material and simulation parameters is critical for accurate predictions in Laser Powder Bed Fusion (L-PBF) Finite Element Analysis (FEA). It is challenging and resource consuming to run experiments that measure and control all possible material properties and process parameters. In this research, we developed a 3-dimensional thermal L-PBF FEA model for a single track laser scan on one layer of metal powder above a solid metal substrate. We applied a design of experiments (DOE) approach which varies simulation parameters to identify critical variables in L-PBF. DOE is an exploratory tool for examining a large number of factors and alternative modeling approaches. It also determines which approaches can best predict L-PBF process performance.Mechanical Engineerin

Relationship Between Dispersion Metric and Properties of PMMA/SWNT Nanocomposites

Particle spatial dispersion is a crucial characteristic of polymer composite materials and this property is recognized as especially important in nanocomposite materials due to the general tendency of nanoparticles to aggregate under processing conditions. We introduce dispersion metrics along with a specified dispersion scale over which material homogeneity is measured and consider how the dispersion metrics correlate quantitatively with the variation of basic nanocomposite properties. We then address the general problem of quantifying nanoparticle spatial dispersion in model nanocomposites of single wall carbon nanotubes (SWNT) dispersed in poly(methyl methacrylate) (PMMA) at a fixed SWNT concentration of 0.5 % using a \u27coagulation\u27 fabrication method. Two methods are utilized to measure dispersion, UV-Vis spectroscopy and optical confocal microscopy. Quantitative spatial dispersion levels were obtained through image analysis to obtain a \u27relative dispersion index\u27 (RDI) representing the uniformity of the dispersion of SWNTs in the samples and through absorbance. We find that the storage modulus, electrical conductivity, and flammability containing the same amount of SWNTs, the relationships between the quantified dispersion levels and physical properties show about four orders of magnitude variation in storage modulus, almost eight orders of magnitude variation in electric conductivity, and about 70 % reduction in peak mass loss rate at the highest dispersion level used in this study. The observation of such a profound effect of SWNT dispersion indicates the need for objective dispersion metrics for correlating and understanding how the properties of nanocomposites are determined by the concentration, shape and size of the nanotubes

Graphical Techniques: By Problem Category (Engineering Statistics Handbook)

Created by Alan Heckert and James Filliben, this part of the National Institute of Standards and Technology (NIST) Engineering Statistics handbook describes different graphs and plots used in exploratory data analysis. More specifically, these graphs and plots consist of: univariate (y = c + e), time series (y = f(t) + e), one factor (y = f(x) + e), multi-factor/comparative (y = f(xp, x1, x2,...,xk) + e), multi-factor/screening (y = f(x1,x2,x3,...,xk) + e), regression (y = f(x1,x2,x3,...,xk) + e), interlab (y1,y2) = f(x) + e) and multivariate (y1,y2,...yp). Each section contains a sample plot, a definition, questions, related techniques, a case study and software. This is a great overview of a myriad of different graphical techniques

Choosing an Experimental Design (Engineering Statistics Handbook)

This section of the Engineering Statistics Handbook, created by authors Alan Heckert and James Filliben of the National Institute of Standards and Technology, describes in detail the process of choosing an experimental design to obtain the results you need. The basic designs an engineer needs to know about are described in detail. Overall, this is a great resource for anyone interested in either engineering or mathematics

Scatter Plot (Engineering Statistics Handbook)

This resource, created by authors James Filliben and Alan Heckert, provides an explanation of scatter plots, their use, purpose and interpretation. It uses examples of the various relationships described by scatter plots as well as case studies and related techniques. Overall, this is a solid representation of this graphing process and could be used by almost any statistics classroom

Process Improvement (Engineering Statistics Handbook)

Created by Alan Heckert and James Filliben, this chapter of the National Institute of Standards and Technology (NIST) Engineering Statistics handbook provides information on the process improvement of experimental design. It contains an introduction, a discussion of assumptions, a discussion of choice of experimental design, a discussion of the analysis of data, an advanced studies section and case studies. The case studies focus on very detailed examinations of these theories. More specifically they are: the eddy current probe sensitivity study and Sonoluminescent light intensity study. This is a nice lesson plan as it introduces the theories and then allows students to directly apply them to case studies

Full Factorial Example (Engineering Statistics Handbook)

This section in the Engineering Statistics Handbook takes a data set and walks the user through analysis and experimental design based on the data. The site is created by authors Alan Heckert and James Filliben, together they intertwine text and images, just like a textbook, to better illustrate this topic. Overall, this is a good resource for teachers and students interested in either mathematics or engineering

Kolmogorov-Smirnov Goodness-of-Fit Test (Engineering Statistics Handbook)

This page, created by James Filliben and Alan Heckert, part of the NIST Engineering Statistics handbook, describes the Kolmogorov-Smirnov goodness of fit test. It contains a graph of the empirical distribution function with the cumulative distribution function, a definition of the test, the questions it answers, the assumptions that it makes, and links to other goodness of fits tests and a case study. This is a nice introductory lesson to this statistical test