Advanced feature detection algorithms for incrementally formed sheet metal parts

Amar Kumar Behera; Bert Lauwers; Joost R. Duflou

doi:10.1016/S1003-6326(12)61725-7

Advanced feature detection algorithms for incrementally formed sheet metal parts

Amar Kumar Behera^*, Bert Lauwers, Joost R. Duflou

^*Corresponding author for this work

Research output: Contribution to journal › Article › peer-review

18 Citations (Scopus)

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.

Original language	English
Journal	Transactions of Nonferrous Metals Society of China (English Edition)
Volume	22
Issue number	SUPPL.2
DOIs	https://doi.org/10.1016/S1003-6326(12)61725-7
Publication status	Published - Dec 2012
Externally published	Yes

Keywords

algorithm
CAPP
expert system
feature detection
single point incremental forming

ASJC Scopus subject areas

Condensed Matter Physics
Geotechnical Engineering and Engineering Geology
Metals and Alloys
Materials Chemistry

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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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AU - Lauwers, Bert

AU - Duflou, Joost R.

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N2 - 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.

AB - 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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Advanced feature detection algorithms for incrementally formed sheet metal parts

Abstract

Keywords

ASJC Scopus subject areas

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