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		<title>Suto et al 2022a - Revision history</title>
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		<updated>2026-05-01T17:05:06Z</updated>
		<subtitle>Revision history for this page on the wiki</subtitle>
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	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Suto_et_al_2022a&amp;diff=254826&amp;oldid=prev</id>
		<title>Scipediacontent at 07:47, 20 July 2022</title>
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				<updated>2022-07-20T07:47:55Z</updated>
		
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				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;← Older revision&lt;/td&gt;
				&lt;td colspan='2' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 07:47, 20 July 2022&lt;/td&gt;
				&lt;/tr&gt;&lt;tr&gt;&lt;td colspan=&quot;2&quot; class=&quot;diff-lineno&quot; id=&quot;mw-diff-left-l7&quot; &gt;Line 7:&lt;/td&gt;
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&lt;tr&gt;&lt;td class='diff-marker'&gt;−&lt;/td&gt;&lt;td style=&quot;color:black; font-size: 88%; border-style: solid; border-width: 1px 1px 1px 4px; border-radius: 0.33em; border-color: #ffe49c; vertical-align: top; white-space: pre-wrap;&quot;&gt;&lt;div&gt;== &lt;del class=&quot;diffchange diffchange-inline&quot;&gt;Document &lt;/del&gt;==&lt;/div&gt;&lt;/td&gt;&lt;td class='diff-marker'&gt;+&lt;/td&gt;&lt;td style=&quot;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;&quot;&gt;&lt;div&gt;== &lt;ins class=&quot;diffchange diffchange-inline&quot;&gt;Full Paper &lt;/ins&gt;==&lt;/div&gt;&lt;/td&gt;&lt;/tr&gt;
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		<author><name>Scipediacontent</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Suto_et_al_2022a&amp;diff=253288&amp;oldid=prev</id>
		<title>Scipediacontent: Scipediacontent moved page Draft Content 528102256 to Suto et al 2022a</title>
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				<updated>2022-07-06T15:31:15Z</updated>
		
		<summary type="html">&lt;p&gt;Scipediacontent moved page &lt;a href=&quot;/public/Draft_Content_528102256&quot; class=&quot;mw-redirect&quot; title=&quot;Draft Content 528102256&quot;&gt;Draft Content 528102256&lt;/a&gt; to &lt;a href=&quot;/public/Suto_et_al_2022a&quot; title=&quot;Suto et al 2022a&quot;&gt;Suto et al 2022a&lt;/a&gt;&lt;/p&gt;
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				&lt;td colspan='1' style=&quot;background-color: white; color:black; text-align: center;&quot;&gt;Revision as of 15:31, 6 July 2022&lt;/td&gt;
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		<author><name>Scipediacontent</name></author>	</entry>

	<entry>
		<id>https://www.scipedia.com/wd/index.php?title=Suto_et_al_2022a&amp;diff=253287&amp;oldid=prev</id>
		<title>Scipediacontent: Created page with &quot; == Abstract ==  We propose a surrogate model for predicting in-plane nonlinear structural deformations of a compliant mechanism. Our model utilizing a 2-dimensional co-rotati...&quot;</title>
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				<updated>2022-07-06T15:31:13Z</updated>
		
		<summary type="html">&lt;p&gt;Created page with &amp;quot; == Abstract ==  We propose a surrogate model for predicting in-plane nonlinear structural deformations of a compliant mechanism. Our model utilizing a 2-dimensional co-rotati...&amp;quot;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&lt;br /&gt;
== Abstract ==&lt;br /&gt;
&lt;br /&gt;
We propose a surrogate model for predicting in-plane nonlinear structural deformations of a compliant mechanism. Our model utilizing a 2-dimensional co-rotational beam element extracts the essential deformation degrees-of-freedoms (DOFs) of bending flexible beams. The total number of DOFs of nodes at both ends of a 2-dimensional beam is six, while the number of deformation DOFs is three, i.e., axial extension, symmetric bending, and anti-symmetric bending. Therefore, it enables us to reduce the computational cost, from six to three, associated with the models by using the essential deformation DOFs of the co-rotational beam element. Moreover, it is difficult to predict the nonlinear responses of forces derived from displacements of a compliant mechanism due to bifurcation of the deformation-path. To overcome the problem, we generate the datasets by applying external forces and use the inverse response for constructing the surrogate models. In the numerical example, large-deformation behaviors of several types of compliant mechanisms are predicted by our surrogate models constructed by three typical learning algorithms: polynomial approximation, radial basis function, and neural network. The prediction performances and computational costs are investigated for verifying that they can be beneficial tools for designing a compliant mechanism with nonlinear elastic deformation behaviors.&lt;br /&gt;
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== Document ==&lt;br /&gt;
&amp;lt;pdf&amp;gt;Media:Draft_Content_528102256-2707_paper-5018-document.pdf&amp;lt;/pdf&amp;gt;&lt;/div&gt;</summary>
		<author><name>Scipediacontent</name></author>	</entry>

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