https://www.scipedia.com/wd/index.php?action=history&feed=atom&title=Nouri_et_al_2022a 2026-04-25T10:47:34Z Revision history for this page on the wiki MediaWiki 1.27.0-wmf.10 https://www.scipedia.com/wd/index.php?title=Nouri_et_al_2022a&diff=262692&oldid=prev 2022-11-25T15:06:30Z

<p>Move page script moved page <a href="/public/Nouri_et_al_1970a" class="mw-redirect" title="Nouri et al 1970a">Nouri et al 1970a</a> to <a href="/public/Nouri_et_al_2022a" title="Nouri et al 2022a">Nouri et al 2022a</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 15:06, 25 November 2022</td> </tr><tr><td colspan='2' style='text-align: center;' lang='en'><div class="mw-diff-empty">(No difference)</div> </td></tr></table>

Move page script https://www.scipedia.com/wd/index.php?title=Nouri_et_al_2022a&diff=262045&oldid=prev 2022-11-23T11:42:04Z

<p>JSanchez moved page <a href="/public/Draft_Sanchez_Pinedo_762758478" class="mw-redirect" title="Draft Sanchez Pinedo 762758478">Draft Sanchez Pinedo 762758478</a> to <a href="/public/Nouri_et_al_1970a" class="mw-redirect" title="Nouri et al 1970a">Nouri et al 1970a</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 11:42, 23 November 2022</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=Nouri_et_al_2022a&diff=262044&oldid=prev 2022-11-23T11:41:55Z

<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 11:41, 23 November 2022</td> </tr><tr><td colspan="2" class="diff-lineno" id="mw-diff-left-l6" >Line 6:</td> <td colspan="2" class="diff-lineno">Line 6:</td></tr> <tr><td class='diff-marker'>&#160;</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>== Abstract ==</div></td><td class='diff-marker'>&#160;</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>== Abstract ==</div></td></tr> <tr><td class='diff-marker'>&#160;</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>&lt;pdf&gt;Media:Draft_Sanchez Pinedo_7627584782264_abstract.pdf&lt;/pdf&gt;</div></td><td class='diff-marker'>&#160;</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>&lt;pdf&gt;Media:Draft_Sanchez Pinedo_7627584782264_abstract.pdf&lt;/pdf&gt;</div></td></tr> <tr><td colspan="2">&#160;</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">&#160;</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">&#160;</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;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_7627584782264_paper.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:262042:newid:262044 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Nouri_et_al_2022a&diff=262042&oldid=prev 2022-11-23T11:41:53Z

<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 11:41, 23 November 2022</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'>&#160;</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'>&#160;</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'>&#160;</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>Casting is one of the most used processes to form metals like aluminium. A casting part can contain several defects that threaten its resistance. Shrinkage porosity is one of the major anomalies that designers try to avoid. For this purpose, rounds of numerical simulations should be performed with operating on a selection of parameters in order to minimize the presence of porosity in the casting part. In general, these approaches are time-cost with dependence on the complexity of the study case and the needed accuracy. In this paper, a methodology of data-driven porosity prediction for 3D parts is proposed in order to minimize the time-cost. A supervised learning algorithm is implemented to learn nodal porosity prediction using decision trees based method. A dataset is generated from a casting simulation software with operating on a selection of parameters. The training is realised on critical features vector extracted from nodal thermal history. Model order reduction method is used to interpolate thermal fields allover the parameter space. This interpolation is sufficiently accurate with minor errors. Promising results of shrinkage porosity prediction on a 3D study case are obtained. An evaluation of these results is performed with reference to the simulations results. This solution can contribute to open perspectives for more data-driven solutions that optimize the time-cost in the design stage.</div></td><td class='diff-marker'>&#160;</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>Casting is one of the most used processes to form metals like aluminium. A casting part can contain several defects that threaten its resistance. Shrinkage porosity is one of the major anomalies that designers try to avoid. For this purpose, rounds of numerical simulations should be performed with operating on a selection of parameters in order to minimize the presence of porosity in the casting part. In general, these approaches are time-cost with dependence on the complexity of the study case and the needed accuracy. In this paper, a methodology of data-driven porosity prediction for 3D parts is proposed in order to minimize the time-cost. A supervised learning algorithm is implemented to learn nodal porosity prediction using decision trees based method. A dataset is generated from a casting simulation software with operating on a selection of parameters. The training is realised on critical features vector extracted from nodal thermal history. Model order reduction method is used to interpolate thermal fields allover the parameter space. This interpolation is sufficiently accurate with minor errors. Promising results of shrinkage porosity prediction on a 3D study case are obtained. An evaluation of these results is performed with reference to the simulations results. This solution can contribute to open perspectives for more data-driven solutions that optimize the time-cost in the design stage.</div></td></tr> <tr><td colspan="2">&#160;</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">&#160;</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 colspan="2">&#160;</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;">&lt;pdf&gt;Media:Draft_Sanchez Pinedo_7627584782264_abstract.pdf&lt;/pdf&gt;</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:262040:newid:262042 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Nouri_et_al_2022a&diff=262040&oldid=prev 2022-11-23T11:41:52Z

<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 11:41, 23 November 2022</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">&#160;</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;">&#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; &#160; </ins></div></td></tr> <tr><td colspan="2">&#160;</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;">==Summary==</ins></div></td></tr> <tr><td class='diff-marker'>&#160;</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'>&#160;</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">&#160;</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;">Casting is one of the most used processes to form metals like aluminium. A casting part can contain several defects that threaten its resistance. Shrinkage porosity is one of the major anomalies that designers try to avoid. For this purpose, rounds of numerical simulations should be performed with operating on a selection of parameters in order to minimize the presence of porosity in the casting part. In general, these approaches are time-cost with dependence on the complexity of the study case and the needed accuracy. In this paper, a methodology of data-driven porosity prediction for 3D parts is proposed in order to minimize the time-cost. A supervised learning algorithm is implemented to learn nodal porosity prediction using decision trees based method. A dataset is generated from a casting simulation software with operating on a selection of parameters. The training is realised on critical features vector extracted from nodal thermal history. Model order reduction method is used to interpolate thermal fields allover the parameter space. This interpolation is sufficiently accurate with minor errors. Promising results of shrinkage porosity prediction on a 3D study case are obtained. An evaluation of these results is performed with reference to the simulations results. This solution can contribute to open perspectives for more data-driven solutions that optimize the time-cost in the design stage.</ins></div></td></tr> <!-- diff cache key mw_drafts_scipedia-sc_mwd_:diff:version:1.11a:oldid:262039:newid:262040 --> </table>

JSanchez https://www.scipedia.com/wd/index.php?title=Nouri_et_al_2022a&diff=262039&oldid=prev 2022-11-23T11:41:50Z

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