| id |
cdrf2019_36 |
| authors |
Dan Luo, Joseph M. Gattas, and Poah Shiun Shawn Tan |
| year |
2020 |
| title |
Real-Time Defect Recognition and Optimized Decision Making for Structural Timber Jointing |
| doi |
https://doi.org/https://doi.org/10.1007/978-981-33-4400-6_4
|
| source |
Proceedings of the 2020 DigitalFUTURES The 2nd International Conference on Computational Design and Robotic Fabrication (CDRF 2020) |
| summary |
Non-structural or out-of-grade timber framing material contains a large proportion of visual and natural defects. A common strategy to recover usable material from these timbers is the marking and removing of defects, with the generated intermediate lengths of clear wood then joined into a single piece of fulllength structural timber. This paper presents a novel workflow that uses machine learning based image recognition and a computational decision-making algorithm to enhance the automation and efficiency of current defect identification and rejoining processes. The proposed workflow allows the knowledge of worker to be translated into a classifier that automatically recognizes and removes areas of defects based on image capture. In addition, a real-time optimization algorithm in decision making is developed to assign a joining sequence of fragmented timber from a dynamic inventory, creating a single piece of targeted length with a significant reduction in material waste. In addition to an industrial application, this workflow also allows for future inventory-constrained customizable fabrication, for example in production of non-standard architectural components or adaptive reuse or defect-avoidance in out-of-grade timber construction. |
| series |
cdrf |
| email |
|
| full text |
 file.pdf (1,685,682 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/09/29 07:51 |
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