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Although 3-D printing or additive manufacturing (AM) holds great promise as a direct manufacturing technology, the geometric accuracy of AM built products remains a critical issue, especially for freeform products with complex geometric shapes. Efforts have long been attempted to improve the accuracy of AM built freeform products. But there is a lack of generic and prescriptive methodology transparent...
Geometric accuracy control is crucial to fulfill the promise of additive manufacturing (AM). The control of the out-of-plane deformation has been a challenge task for AM due to its complex underlying physics. We have been establishing a generic and prescriptive methodology to represent, predict and compensate 3D geometric deformation of AM built products based on a limited number of test cases. Built...
Predictive models for geometric shape deformation constitute an important component in geometric fidelity control for three-dimensional (3D) printing. However, model building is made difficult by the wide variety of possible process conditions and shapes. A methodology that can make full use of data collected on different shapes and conditions, and reduce the haphazard aspect of traditional statistical...
This paper presents an online self-tuning Smith Predictor for the First Order Plus Dead Time Model (FOPDT). It can tune time delay through oscillated input and output. Realtime phase difference detection is used to obtain the phase difference between the input and the output. An online tuner is used to minimize the phase difference. Once the phase difference is minimized, the exact time delay is completely...
Geometric fidelity of 3D printed products is critical for Additive Manufacturing (AM) or 3D printing to be a direct manufacturing technology. Shape deviations of AM built products can be attributed to multiple variation sources such as substrate geometry defect, disturbance in process variables, and material phase change. Three strategies have been reported to improve geometric quality in AM: (1)...
Energy consumption forecast is an essential component in making energy plan. In the light of the complexity and nonlinearity of energy consumption system, the gray forecast model and neural network model are respectively established by using the energy consumption historical data of certain province. Then their advantages and disadvantages are analyzed. Lastly, the method of optimal combination is...
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