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

Experimental Design for the INL Sample Collection Operational Test

This document describes the test events and numbers of samples comprising the experimental design that was developed for the contamination, decontamination, and sampling of a building at the Idaho National Laboratory (INL). This study is referred to as the INL Sample Collection Operational Test. Specific objectives were developed to guide the construction of the experimental design. The main objective is to assess the relative abilities of judgmental and probabilistic sampling strategies to detect contamination in individual rooms or on a whole floor of the INL building. A second objective is to assess the use of probabilistic and Bayesian (judgmental + probabilistic) sampling strategies to make clearance statements of the form “X% confidence that at least Y% of a room (or floor of the building) is not contaminated. The experimental design described in this report includes five test events. The test events (i) vary the floor of the building on which the contaminant will be released, (ii) provide for varying or adjusting the concentration of contaminant released to obtain the ideal concentration gradient across a floor of the building, and (iii) investigate overt as well as covert release of contaminants. The ideal contaminant gradient would have high concentrations of contaminant in rooms near the release point, with concentrations decreasing to zero in rooms at the opposite end of the building floor. For each of the five test events, the specified floor of the INL building will be contaminated with BG, a stand-in for Bacillus anthracis. The BG contaminant will be disseminated from a point-release device located in the room specified in the experimental design for each test event. Then judgmental and probabilistic samples will be collected according to the pre-specified sampling plan. Judgmental samples will be selected based on professional judgment and prior information. Probabilistic samples will be selected in sufficient numbers to provide desired confidence for detecting contamination or clearing uncontaminated (or decontaminated) areas. Following sample collection for a given test event, the INL building will be decontaminated using Cl2O gas. For possibly contaminated areas (individual rooms or the whole floor of a building), the numbers of probabilistic samples were chosen to provide 95% confidence of detecting contaminated areas of specified sizes. The numbers of judgmental samples were chosen based on guidance from experts in judgmental sampling. For rooms that may be uncontaminated following a contamination event, or for whole floors after decontamination, the numbers of judgmental and probabilistic samples were chosen using a Bayesian approach that provides for combining judgmental and probabilistic samples to make a clearance statement of the form “95% confidence that at least 99% of the room (or floor) is not contaminated”. The experimental design also provides for making 95%/Y% clearance statements using only probabilistic samples, where Y < 99. For each test event, the numbers of samples were selected for a minimal plan (containing fewer samples) and a preferred plan (containing more samples). The preferred plan is recommended over the minimal plan. The preferred plan specifies a total of 1452 samples, 912 after contamination and 540 after decontamination. The minimal plan specifies a total of 1119 samples, 744 after contamination and 375 after decontamination. If the advantages of the “after decontamination” portion of the preferred plan are judged to be small compared to the “after decontamination” portion of the minimal plan, it is an option to combine the “after contamination” portion of the preferred plan (912 samples) with the “after decontamination” portion of the minimal plan (375 samples). This hybrid plan would involve a total of 1287 samples

Using light scattering to evaluate the separation of polydisperse nanoparticles

Appendix A Supplementary data The following are the supplementary data related to this article: Download Appendix A Supplementary data Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.aca.2015.06.027. Abstract The analysis of natural and otherwise complex samples is challenging and yields uncertainty about the accuracy and precision of measurements. Here we present a practical tool to assess relative accuracy among separation protocols for techniques using light scattering detection. Due to the highly non-linear relationship between particle size and the intensity of scattered light, a few large particles may obfuscate greater numbers of small particles. Therefore, insufficiently separated mixtures may result in an overestimate of the average measured particle size. Complete separation of complex samples is needed to mitigate this challenge. A separation protocol can be considered improved if the average measured size is smaller than a previous separation protocol. Further, the protocol resulting in the smallest average measured particle size yields the best separation among those explored. If the differential in average measured size between protocols is less than the measurement uncertainty, then the selected protocols are of equivalent precision. As a demonstration, this assessment metric is applied to optimization of cross flow (V x ) protocols in asymmetric flow field flow fractionation (AF4) separation interfaced with online quasi-elastic light scattering (QELS) detection using mixtures of polystyrene beads spanning a large size range. Using this assessment metric, the V x parameter was modulated to improve separation until the average measured size of the mixture was in statistical agreement with the calculated average size of particles in the mixture. While we demonstrate this metric by improving AF4V x protocols, it can be applied to any given separation parameters for separation techniques that employ dynamic light scattering detectors. Graphical abstract Highlights • We present a tool to assess relative accuracy among separation protocols. • This metric can be applied to any techniques using light scattering detection. • An improved separation protocol minimizes the average measured particle size. • A protocol with the smallest average measured particle size is the best separation. • Metric is demonstrated by improving AF4 cross flow protocols for polystyrene beads

National Bureau of Standards Reports

Abstract: This manual provides DATAPLOT code solution to a variety of commonly occurring graphical problems. A line-by-line explanation of code is given, along with illustrations and general discussion

Process Modeling (Engineering Statistics Handbook)

Created by Alan Heckert and James Filliben, this chapter of the National Institute of Standard and Technology (NIST) Engineering Statistics handbook presents information on the statistical modeling of an engineering process. It contains an introduction, discussion of the assumptions, information about data collection and analysis, a discussion of what can be concluded from different process models, and case studies. This final section is quite interesting. It offers four different studies, they consist of: load cell output, the Alaskan Pipeline, ultrasonic reference block and the thermal expansion of copper. Once students go through the theories presented, the case studies allow them to apply this knowledge

Process or Product Monitoring and Control (Engineering Statistics Handbook)

This chapter of the NIST Engineering Statistics handbook presents techniques for monitoring and controlling processes and signaling when corrective actions are necessary. It contains an introduction to process control, a discussion of acceptance sampling, introductions to control charts and time series modeling, tutorials for background information and case studies. This is a nice overview of the topic. It contains valuable graphs/charts and many of the tutorials could be used as classroom activities

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

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