Research of Learning and Extracting Manufacturing Quality by Voxel Mapping
| Project/Area Number |
13650804
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| Research Category |
Grant-in-Aid for Scientific Research (C)
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| Allocation Type | Single-year Grants |
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| Section | 一般 |
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| Research Field |
Metal making engineering
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| Research Institution | Konan University |
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Principal Investigator |
NAGASAKA Yoshiyuki Konan University, Faculty of Business Administration, Professor, 経営学部, 教授 (00268236)
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| Project Period (FY) |
2001 – 2004
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| Project Status |
Completed (Fiscal Year 2004)
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| Budget Amount *help |
¥3,700,000 (Direct Cost: ¥3,700,000)
Fiscal Year 2004: ¥400,000 (Direct Cost: ¥400,000)
Fiscal Year 2003: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2002: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 2001: ¥1,400,000 (Direct Cost: ¥1,400,000)
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| Keywords | geometric feature / manufacturing quality / CAD / cost management / knowledge management / classification / ナノッジマネジメント / ボクセル / ニューラルネットワーク |
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| Research Abstract |
A methodology for extracting and learning geometric features has been studied. At first, an algorithm was developed to create voxels (volume pixels) easily from three-dimensional CAD data. A voxel is a picture element in a three-dimensional coordinate system. Using this algorithm, CAD data such as STL formatted data can be converted to very fine voxels in a few seconds. Then, distance values from the surface of each voxel are calculated. At the same time, a Ds (distance from surface) map can be obtained. In the next step, skeletons of shapes are extracted as polygons from the mapped data.
Voxels with the maximum Ds among neighboring voxels are selected as skeleton candidates. After the selection, the candidates are converted to straight lines or circular rings. They are then represented by several vectors and stored as a tree structure. A standard tree involves, for example, four levels and each branch has four descendants. Each parent branch has the same number of descendants. The attributes include such as the scaled volume, connection strength, and scaled X, Y, Z coordinates. These vectors are the input to a skeleton classifier, which is constructed on the basis of the back propagation neural network model. The proposed approach was implemented on a PC machine.
A viewer was developed to display the skeletons clearly in three-dimensional shapes. Several parts were selected to demonstrate the classification capability for this methodology. Here, the skeletons extracted directly from the three-dimensional shapes were used. The back propagation neural network model should be trained using some representative shapes. It was found that this method is practically useful through these experiments.
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Report
(5 results)
Research Products
(19 results)
