Optimal Orientation Selection Research Articles

Description Logic Ontology-Supported Part Orientation for Fused Deposition Modelling

Fused deposition modelling (FDM) is well-known as an inexpensive and the most commonly used additive manufacturing process. In FDM, build orientation is one of the critical factors that affect the quality of the printed part. However, the activity of determining a build orientation for an FDM part, i.e., part orientation for FDM, usually relies on the knowledge and experience of domain experts. This necessitates an approach that enables the capture, representation, reasoning, and reuse of the data and knowledge in this activity. In this paper, a description logic (DL) ontology-supported part orientation approach for FDM is presented. Firstly, a set of top-level entities are created to construct a DL ontology for FDM part orientation. Then a DL ontology-supported alternative orientation generation procedure, a DL ontology-supported factor value prediction procedure, and a DL ontology-supported optimal orientation selection procedure are developed successively. After that, the application of the presented approach is illustrated via part orientation on six FDM parts. Finally, the effectiveness and efficiency of the presented approach are demonstrated through theoretical predictions and printing experiments and the advantages of the approach are demonstrated via an example. The demonstration results suggest that the presented approach has satisfying effectiveness and efficiency and provides a semantic enrichment model for capturing and representing FDM part orientation data and knowledge to enable automatic checking, reasoning, query, and further reuse.

Occlusion-aware depth estimation for light field using multi-orientation EPIs

Epipolar plane images (EPIs) contain special linear structures that reflect the disparity of a 3D point and are widely used in light field depth estimation. However, previous EPI-based approaches only utilize horizontal and vertical EPIs to estimate local disparities and ignore diagonal directions. In order to make full use of the regular grid light field images, we develop a strategy to extract epipolar plane images in all available directions. Based on the multi-orientation EPIs, a specific EPI in which the point is not occluded is found and used to calculate robust depth estimation. We also design a novel framework to estimate the depth information which combines the local depth with edge orientation. The multi-orientation EPIs and optimal orientation selection are proved to be effective in detecting and excluding occlusions. Experimental results show that the proposed method outperforms state-of-the-art depth estimation methods, especially near occlusion boundaries.

Reduction of beam number for IMRT treatment using optimal orientation selection

Reduction of beam number for IMRT treatment using optimal orientation selection

Reduction of beam number for IMRT treatment using optimal orientation selection

Reduction of beam number for IMRT treatment using optimal orientation selection