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Predicting welding distortion in a panel structure with longitudinal stiffeners using inherent deformations obtained by inverse analysis method.

ABSTRACT: Welding-induced deformation not only negatively affects dimension accuracy but also degrades the performance of product. If welding deformation can be accurately predicted beforehand, the predictions will be helpful for finding effective methods to improve manufacturing accuracy. Till now, there are two kinds of finite element method (FEM) which can be used to simulate welding deformation. One is the thermal elastic plastic FEM and the other is elastic FEM based on inherent strain theory. The former only can be used to calculate welding deformation for small or medium scale welded structures due to the limitation of computing speed. On the other hand, the latter is an effective method to estimate the total welding distortion for large and complex welded structures even though it neglects the detailed welding process. When the elastic FEM is used to calculate the welding-induced deformation for a large structure, the inherent deformations in each typical joint should be obtained beforehand. In this paper, a new method based on inverse analysis was proposed to obtain the inherent deformations for weld joints. Through introducing the inherent deformations obtained by the proposed method into the elastic FEM based on inherent strain theory, we predicted the welding deformation of a panel structure with two longitudinal stiffeners. In addition, experiments were carried out to verify the simulation results.

SUBMITTER: Liang W

PROVIDER: S-EPMC4168243 | biostudies-other | 2014

REPOSITORIES: biostudies-other

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Project description:BackgroundOnline longitudinal surveys may be subject to potential biases due to sample attrition. This study was designed to identify potential predictors of attrition using a longitudinal panel survey collected during the COVID-19 pandemic.MethodsThree waves of data were collected using Amazon Mechanical Turk (MTurk), an online crowd-sourced platform. For each wave, the study sample was collected by referencing a US national representative sample distribution of age, gender, and race, based on US census data. Variables included respondents' demographics, medical history, socioeconomic status, COVID-19 experience, changes of health behavior, productivity, and health-related quality of life (HRQoL). Results were compared to pre-pandemic US norms. Measures that predicted attrition at different times of the pandemic were identified via logistic regression with stepwise selection.Results1467 of 2734 wave 1 respondents participated in wave 2 and, 964 of 2454 wave 2 respondents participated in wave 3. Younger age group, Hispanic origin (p ≤ 0.001) and higher self-rated survey difficulty (p ≤ 0.002) consistently predicted attrition in the following wave. COVID-19 experience, employment, productivity, and limited physical activities were commonly observed variables correlated with attrition with specific measures varying by time periods. From wave 1, mental health conditions, average daily hours worked (p = 0.004), and COVID-19 impact on work productivity (p < 0.001) were associated with a higher attrition rate at wave 2, additional to the aforementioned factors. From wave 2, support of social distancing (p = 0.032), being Republican (p < 0.001), and having just enough money to make ends meet (p = 0.003) were associated with predicted attrition at wave 3.ConclusionsAttrition in this longitudinal panel survey was not random. Besides commonly identified demographic factors that contribute to panel attrition, COVID-19 presented novel opportunities to address sample biases by correlating attrition with additional behavioral and HRQoL factors in a constantly evolving environment. While age, ethnicity, and survey difficulty consistently predicted attrition, other factors, such as COVID-19 experience, changes of employment, productivity, physical health, mental health, and financial situation impacted panel attrition during the pandemic at various degrees.

Project description:Welders are exposed to high concentrations of nanoparticles. Compared to larger particles, nanoparticles have been associated with more toxic effects at the cellular level, including the generation of more reactive oxygen species activity. Current methods for welding-fume aerosol exposures do not differentiate between the nano-fraction and the larger particles. The objectives of this work are to establish a method to estimate the respiratory deposition of the nano-fraction of selected metals in welding fumes and test this method in a laboratory setting. Manganese (Mn), Nickel (Ni), Chromium (Cr), and hexavalent chromium (Cr(VI)) are commonly found in welding fume aerosols and have been linked with severe adverse health outcomes. Inductively coupled plasma mass spectrometry (ICP-MS) and ion chromatography (IC) were evaluated as methods for analyzing the content of Mn, Ni, Cr, and Cr(VI) nanoparticles in welding fumes collected with nanoparticle respiratory deposition (NRD) samplers. NRD samplers collect nanoparticles at deposition efficiencies that closely resemble physiological deposition in the respiratory tract. The limits of detection (LODs) and quantitation (LOQs) for ICP-MS and IC were determined analytically. Mild and stainless steel welding fumes generated with a robotic welder were collected with NRD samplers inside a chamber. LODs (LOQs) for Mn, Ni, Cr, and Cr(VI) were 1.3 ?g (4.43 ?g), 0.4 ?g (1.14 ?g), 1.1 ?g (3.33 ?g), and 0.4 ?g (1.42 ?g), respectively. Recovery of spiked samples and certified welding fume reference material was greater than 95%. When testing the method, the average percentage of total mass concentrations collected by the NRD samplers was ~30% for Mn, ~50% for Cr, and ~60% for Ni, indicating that a large fraction of the metals may lie in the nanoparticle fraction. This knowledge is critical to the development of toxicological studies aimed at finding links between exposure to welding fume nanoparticles and adverse health effects. Future work will involve the validation of the method in workplace settings. [Supplementary materials are available for this article. Go to the publisher's online edition of Journal of Occupational and Environmental Hygiene for the following free supplemental resource: Digestion, extraction, and analysis procedures for nylon mesh screens.].

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Predicting welding distortion in a panel structure with longitudinal stiffeners using inherent deformations obtained by inverse analysis method.

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