Extended Fabrication-Aware Convolution Learning Framework for Predicting 3D Shape Deformation in Additive Manufacturing

Additive manufacturing (AM) or three-dimensional (3D) printing is a promising technology that enables the direct fabrication of products with complex shapes without extra tooling and fixturing. However, control of 3D shape deformation in AM built products has been a challenging issue due to geometric complexity, product varieties, material phase changing and shrinkage, and interlayer bonding. One viable approach for accuracy control is through compensation of the product design to offset the geometric shape deformation. This work provides an analytical foundation to achieve optimal compensation for high-precision AM. We first present the optimal compensation policy or the optimal amount of compensation for two-dimensional (2D) shape deformation. By analyzing its optimality property, we propose the minimum area deviation (MAD) criterion to offset 2D shape deformation. This result is then generalized by establishing the minimum volume deviation (MVD) criterion and by deriving the optimal amount of compensation for 3D shape deformation. Furthermore, MAD and MVD criteria provide convenient quality measure or quality index for AM built products that facilitate online monitoring and feedback control of shape geometric accuracy.

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Offline Predictive Control of Out-of-Plane Shape Deformation for Additive Manufacturing

Journal of Manufacturing Science and Engineering ◽

10.1115/1.4033444 ◽

2016 ◽

Vol 138 (12) ◽

Cited By ~ 21

Author(s):

Yuan Jin ◽

S. Joe Qin ◽

Qiang Huang

Keyword(s):

Additive Manufacturing ◽

Shape Deformation ◽

Mathematical Framework ◽

Geometric Errors ◽

Accuracy Control ◽

Shape Accuracy ◽

Control Approach ◽

3D Shape ◽

Out Of Plane ◽

Plane Shape

Additive manufacturing (AM) is a promising direct manufacturing technology, and the geometric accuracy of AM built products is crucial to fulfill the promise of AM. Prediction and control of three-dimensional (3D) shape deformation, particularly out-of-plane geometric errors of AM built products, have been a challenging task. Although finite-element modeling has been extensively applied to predict 3D deformation and distortion, improving part accuracy based purely on such simulation still needs significant methodology development. We have been establishing an alternative strategy that can be predictive and transparent to specific AM processes based on a limited number of test cases. Successful results have been accomplished in our previous work to control in-plane (x–y plane) shape deformation through offline compensation. In this study, we aim to establish an offline out-of-plane shape deformation control approach based on limited trials of test shapes. We adopt a novel spatial deformation formulation in which both in-plane and out-of-plane geometric errors are placed under a consistent mathematical framework to enable 3D accuracy control. Under this new formulation of 3D shape deformation, we develop a prediction and offline compensation method to reduce out-of-plane geometric errors. Experimental validation is successfully conducted to validate the developed 3D shape accuracy control approach.

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Experimental calibration and compensation for the continuous effect of time, number of layers and volume of material on shape deformation in small-scale additive manufacturing of concrete

Additive Manufacturing ◽

10.1016/j.addma.2021.102228 ◽

2021 ◽

pp. 102228

Author(s):

Negar Ashrafi ◽

Shadi Nazarian ◽

Nicholas A. Meisel ◽

Jose Pinto Duarte

Keyword(s):

Additive Manufacturing ◽

Shape Deformation ◽

Small Scale ◽

Experimental Calibration ◽

Number Of Layers

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3D Shape Descriptor by Principal Component Analysis Embedding for Non-rigid 3D Shape Retrieval in A Learning Framework

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS) ◽

10.1109/ddcls52934.2021.9455676 ◽

2021 ◽

Author(s):

Chunmei Duan ◽

Meizhen Liu

Keyword(s):

Principal Component Analysis ◽

Principal Component ◽

Shape Descriptor ◽

Component Analysis ◽

Shape Retrieval ◽

3D Shape Retrieval ◽

3D Shape ◽

Learning Framework

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A Reverse Compensation Framework for Shape Deformation in Additive Manufacturing

Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing ◽

10.1115/msec2016-8815 ◽

2016 ◽

Cited By ~ 2

Author(s):

Kai Xu ◽

Tsz-Ho Kwok ◽

Yong Chen

Keyword(s):

Additive Manufacturing ◽

Computer Aided Design ◽

Shape Deformation ◽

Cad Model ◽

Complex Deformation ◽

Physical Measurements ◽

Building Process ◽

Aided Design ◽

Thermal Cooling ◽

And Control

Shape deformation is an important issue in additive manufacturing (AM) processes such as the projection-based Stereolithography. Volumetric shrinkage and thermal cooling during the photopolymerization process combined with other factors such as the layer-constrained building process lead to complex deformation that is difficult to predict and control. In this paper, a general reverse compensation method and related computation framework are presented to reduce the shape deformation of AM fabricated parts. During the reverse compensation process, the shape deformation is calculated based on physical measurements of shape deformation. A novel method for identifying the correspondence between the deformed shape and the given nominal computer-aided design (CAD) model is presented based on added markers. Accordingly, a new CAD model based on the shape deformation and related compensation is computed. The intelligently revised CAD model by going through the same building process can result in a fabricated part that is close to the nominal CAD model. Two test cases have been designed to demonstrate the effectiveness of the presented method and the related computation framework. The shape deformation in terms of L2- and L∞-norm based on measuring the geometric errors is reduced by 40–60%.

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