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Based on the experimental identification of the phase transformation surface, a modelling of shape memory alloys anisothermal behaviour is proposed.
SSMs offer an advantage over the previously discussed segmentation techniques because of the precise modelling of shape variations, which can be used to regularise the liver's appearance.
In this paper, a prescriptive deviation modelling method coupled with machine learning techniques is proposed to address the modelling of shape deviations in additive manufacturing.
The shape control system can be divided into pre-setting control system and closed-loop control system according to the control sequence, and the modelling of shape control system includes mechanism and intelligent models.
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The shape pattern recognition model is a basic model of shape theory.
The strip buckling model is a basic model of shape theory too.
In this paper, we focus on mid-level modeling of shape representation.
The nonlinear component is based on a capillary model of shape stability.
Martensite transformation kinetics is the principal factor underlying the constitutive model of shape memory alloys (SMAs).
A nonlinear thermomechanical constitutive model of shape memory polymer (SMP) is developed by modifying a linear model.
Another regression model of shape pre-setting control for the 6-high reversing cold rolling mill was established based on the results of the influence function method [111].
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