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Title: | LAYER DEPOSITION PATH GENERATION IN ADDITIVE MANUFACTURING | Authors: | XU HAO | ORCID iD: | orcid.org/0000-0001-5166-2678 | Keywords: | Intelligent manufacturing, process planning, algorithm design, machine learning, automatic partitioning, automatic filling | Issue Date: | 16-Aug-2023 | Citation: | XU HAO (2023-08-16). LAYER DEPOSITION PATH GENERATION IN ADDITIVE MANUFACTURING. ScholarBank@NUS Repository. | Abstract: | As for additive manufacturing (AM) process planning, based on standard triangle language (STL) format file transformed from computer-aided design (CAD) model, slicing process is executed first. Then, each sliced layer can be segmented into a series of subregions, and such stage is named as partitioning process. Subsequently, the deposition paths are planned for each subregion with essential deposition parameters prescribed, the stage of which is called filling process. Four aspects of work were preformed and demonstrated in this thesis to address the issues. Firstly, for rectifying the distortion and residual tiny shapes (RTS) emerged in the existing work, rectifier and RTS solver are designed. Secondly, a series of enhancement design (e.g., edge identifier, vertex density adapter, vertex classifier, curve filter, etc.) are proposed which promotes the processing efficiency and robustness of the existing work. Thirdly, focusing on porous layer, a type of auto-segmentation approach and program are developed. Fourthly, based on the output of auto-segmentation program, a few types of auto-filling programs are developed. The research inspires the automatic process planning of AM by hierarchical design, which also expands novel hybrid concept on layer deposition path generation. | URI: | https://scholarbank.nus.edu.sg/handle/10635/246267 |
Appears in Collections: | Master's Theses (Open) |
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