Advanced feature detection algorithms for incrementally formed sheet metal parts
New advanced algorithms for the detection of detailed features in parts formed by single point incremental forming (SPIF) were developed. The features were detected in STL part specifications that took into account the geometry, curvature, location, orientation and process parameters to detect 33 di...
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| Published in | Transactions of Nonferrous Metals Society of China Vol. 22; pp. s315 - s322 |
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| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier Ltd
01.12.2012
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| Subjects | |
| Online Access | Get full text |
| ISSN | 1003-6326 |
| DOI | 10.1016/S1003-6326(12)61725-7 |
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| Abstract | New advanced algorithms for the detection of detailed features in parts formed by single point incremental forming (SPIF) were developed. The features were detected in STL part specifications that took into account the geometry, curvature, location, orientation and process parameters to detect 33 different features within an expert CAPP system for SPIF. The detection process was facilitated by using multi-level edge segmentation routines that first created a frame of edge features. Within this frame, the remaining features were then detected using region growing algorithms. The results show successful detection for a number of test cases. A case study for a double curved hemisphere illustrates the generation of optimal tool paths using compensation for the detected features in the part. These tool paths lead to the improvement in the accuracy of the formed sheet metal parts. |
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| AbstractList | New advanced algorithms for the detection of detailed features in parts formed by single point incremental forming (SPIF) were developed. The features were detected in STL part specifications that took into account the geometry, curvature, location, orientation and process parameters to detect 33 different features within an expert CAPP system for SPIF. The detection process was facilitated by using multi-level edge segmentation routines that first created a frame of edge features. Within this frame, the remaining features were then detected using region growing algorithms. The results show successful detection for a number of test cases. A case study for a double curved hemisphere illustrates the generation of optimal tool paths using compensation for the detected features in the part. These tool paths lead to the improvement in the accuracy of the formed sheet metal parts. |
| Author | LAUWERS, Bert BEHERA, Amar Kumar DUFLOU, Joost R. |
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| Cites_doi | 10.1115/CIE1995-0785 10.1007/BF01176300 10.1007/BF01350820 10.4028/www.scientific.net/KEM.473.881 10.1016/S0010-4485(00)00076-2 10.1080/00207549208942949 10.1016/j.cirp.2008.03.030 10.1016/j.cad.2012.10.045 10.1016/S0965-9978(96)00050-6 10.4028/www.scientific.net/KEM.344.527 10.1109/70.897789 10.4028/www.scientific.net/KEM.504-506.919 10.1016/0010-4485(91)90027-T 10.4028/www.scientific.net/KEM.504-506.869 |
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| Keywords | feature detection CAPP expert system single point incremental forming algorithm |
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| Title | Advanced feature detection algorithms for incrementally formed sheet metal parts |
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