TY - JOUR
T1 - Recognition and classification of protrusion features on thin-wall parts for mold flow analysis
AU - Lai, Jiing Yih
AU - Song, Pei Pu
AU - Hsiao, An Sheng
AU - Tsai, Yao Chen
AU - Hsu, Chia Hsiang
N1 - Publisher Copyright:
© 2019, Springer-Verlag London Ltd., part of Springer Nature.
PY - 2021/4
Y1 - 2021/4
N2 - Thin-wall plastic parts exist in many products and are frequently manufactured by injection molding. The inner surface of a thin-wall part has many functional and structural features, typically divided into depressions and protrusions. While recognition of protrusion features is important in mold flow analysis, this recognition is difficult owing to the complexity and variety of the designed shapes. The purpose of this study was to develop a method for the detection and classification of protrusion features on thin-wall plastic parts. In the proposed algorithm, the inner and outer faces of a part model were detected first. Auxiliary faces, including translational, wall, and bottom faces, were recognized next. With auxiliary faces available, protrusion faces can thus be recognized and grouped in accordance with their neighboring relationship. A feature classification algorithm was finally implemented to classify five types of protrusions, namely tubes, ribs, columns, polygon blocks, and irregular extrusions. A detailed description of the procedures in each step of the proposed algorithm is provided. Twelve CAD models and analysis results are also presented to demonstrate the feasibility of the proposed protrusion recognition algorithm.
AB - Thin-wall plastic parts exist in many products and are frequently manufactured by injection molding. The inner surface of a thin-wall part has many functional and structural features, typically divided into depressions and protrusions. While recognition of protrusion features is important in mold flow analysis, this recognition is difficult owing to the complexity and variety of the designed shapes. The purpose of this study was to develop a method for the detection and classification of protrusion features on thin-wall plastic parts. In the proposed algorithm, the inner and outer faces of a part model were detected first. Auxiliary faces, including translational, wall, and bottom faces, were recognized next. With auxiliary faces available, protrusion faces can thus be recognized and grouped in accordance with their neighboring relationship. A feature classification algorithm was finally implemented to classify five types of protrusions, namely tubes, ribs, columns, polygon blocks, and irregular extrusions. A detailed description of the procedures in each step of the proposed algorithm is provided. Twelve CAD models and analysis results are also presented to demonstrate the feasibility of the proposed protrusion recognition algorithm.
KW - B-rep model
KW - Feature recognition
KW - Protrusion recognition
KW - Recognition of inner and outer faces
KW - Thin-wall part
UR - http://www.scopus.com/inward/record.url?scp=85074053165&partnerID=8YFLogxK
U2 - 10.1007/s00366-019-00859-1
DO - 10.1007/s00366-019-00859-1
M3 - 期刊論文
AN - SCOPUS:85074053165
SN - 0177-0667
VL - 37
SP - 833
EP - 854
JO - Engineering with Computers
JF - Engineering with Computers
IS - 2
ER -