Balaji et al 2023a - Revision history

https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Balaji_et_al_2023a Balaji et al 2023a - Revision history 2026-04-06T17:03:01Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Balaji_et_al_2023a&diff=287246&oldid=prev JSanchez: JSanchez moved page Draft Sanchez Pinedo 451912023 to Balaji et al 2023a 2023-11-09T13:35:58Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_451912023" class="mw-redirect" title="Draft Sanchez Pinedo 451912023">Draft Sanchez Pinedo 451912023</a> to <a href="/public/Balaji_et_al_2023a" title="Balaji et al 2023a">Balaji et al 2023a</a></p> <table class="diff diff-contentalign-left" data-mw="interface"> <tr style='vertical-align: top;' lang='en'> <td colspan='1' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='1' style="background-color: white; color:black; text-align: center;">Revision as of 13:35, 9 November 2023</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

JSanchez https://www.scipedia.com/wd/index.php?title=Balaji_et_al_2023a&diff=287223&oldid=prev JSanchez at 13:29, 9 November 2023 2023-11-09T13:29:47Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:29, 9 November 2023</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l3" >Line 3:</td> <td colspan="2" class="diff-lineno">Line 3:</td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Process-induced deformations result from internal residual stress caused by the anisotropic properties of thermoset composite parts. The study’s focus is diagnosing the polymerization process, or curing, and considering how uncertainties in boundary conditions affect cure kinetics. This is achieved through a Particle Filter approach, utilizing a Bayesian framework. This framework recursively estimates the evolving cure state’s posterior distribution based on observed measurements from Differential Calorimetry Scanning tests and thermocouples. The algorithm simultaneously estimates the cure state parameters and predicts part temperature and process-induced deformations, which are closely tied to cure behaviour. This is accomplished using diffusion cure kinetics and analytical deformation models. Furthermore, it introduces an augmented cure state formulation to address uncertainties in cure boundary conditions, which conventional models overlook. The developed stochastic approach adeptly captures uncertainties related to cure evolution while providing comparable deformation predictions with minimal computational costs and memory usage. Experimental measurements of process-induced deformations in C-shaped thermosetting parts, made of unidirectional 8552/AS4 fibres and cured following the Manufacturing Recommended Curing Cycle, are validated using the developed algorithm. After validation, the proposed model is employed to predict outcomes, which are then utilized to determine the optimal curing cycle using a Genetic Algorithm.</div></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"><div>Process-induced deformations result from internal residual stress caused by the anisotropic properties of thermoset composite parts. The study’s focus is diagnosing the polymerization process, or curing, and considering how uncertainties in boundary conditions affect cure kinetics. This is achieved through a Particle Filter approach, utilizing a Bayesian framework. This framework recursively estimates the evolving cure state’s posterior distribution based on observed measurements from Differential Calorimetry Scanning tests and thermocouples. The algorithm simultaneously estimates the cure state parameters and predicts part temperature and process-induced deformations, which are closely tied to cure behaviour. This is accomplished using diffusion cure kinetics and analytical deformation models. Furthermore, it introduces an augmented cure state formulation to address uncertainties in cure boundary conditions, which conventional models overlook. The developed stochastic approach adeptly captures uncertainties related to cure evolution while providing comparable deformation predictions with minimal computational costs and memory usage. Experimental measurements of process-induced deformations in C-shaped thermosetting parts, made of unidirectional 8552/AS4 fibres and cured following the Manufacturing Recommended Curing Cycle, are validated using the developed algorithm. After validation, the proposed model is employed to predict outcomes, which are then utilized to determine the optimal curing cycle using a Genetic Algorithm.</div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"></ins></div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">== Full Paper ==</ins></div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;"><pdf>Media:Draft_Sanchez Pinedo_4519120239.pdf</pdf></ins></div></td></tr>  </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Balaji_et_al_2023a&diff=287221&oldid=prev JSanchez at 13:29, 9 November 2023 2023-11-09T13:29:46Z

<p></p> <table class="diff diff-contentalign-left" data-mw="interface"> <col class='diff-marker' /> <col class='diff-content' /> <col class='diff-marker' /> <col class='diff-content' /> <tr style='vertical-align: top;' lang='en'> <td colspan='2' style="background-color: white; color:black; text-align: center;">← Older revision</td> <td colspan='2' style="background-color: white; color:black; text-align: center;">Revision as of 13:29, 9 November 2023</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l1" >Line 1:</td> <td colspan="2" class="diff-lineno">Line 1:</td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">                                </ins></div></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">==Abstract==</ins></div></td></tr> <tr><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td><td class='diff-marker'> </td><td style="background-color: #f9f9f9; color: #333333; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #e6e6e6; vertical-align: top; white-space: pre-wrap;"></td></tr> <tr><td colspan="2"> </td><td class='diff-marker'>+</td><td style="color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #a3d3ff; vertical-align: top; white-space: pre-wrap;"><div><ins style="font-weight: bold; text-decoration: none;">Process-induced deformations result from internal residual stress caused by the anisotropic properties of thermoset composite parts. The study’s focus is diagnosing the polymerization process, or curing, and considering how uncertainties in boundary conditions affect cure kinetics. This is achieved through a Particle Filter approach, utilizing a Bayesian framework. This framework recursively estimates the evolving cure state’s posterior distribution based on observed measurements from Differential Calorimetry Scanning tests and thermocouples. The algorithm simultaneously estimates the cure state parameters and predicts part temperature and process-induced deformations, which are closely tied to cure behaviour. This is accomplished using diffusion cure kinetics and analytical deformation models. Furthermore, it introduces an augmented cure state formulation to address uncertainties in cure boundary conditions, which conventional models overlook. The developed stochastic approach adeptly captures uncertainties related to cure evolution while providing comparable deformation predictions with minimal computational costs and memory usage. Experimental measurements of process-induced deformations in C-shaped thermosetting parts, made of unidirectional 8552/AS4 fibres and cured following the Manufacturing Recommended Curing Cycle, are validated using the developed algorithm. After validation, the proposed model is employed to predict outcomes, which are then utilized to determine the optimal curing cycle using a Genetic Algorithm.</ins></div></td></tr>  </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Balaji_et_al_2023a&diff=287220&oldid=prev JSanchez: Created blank page 2023-11-09T13:29:44Z

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