TY - GEN
T1 - Quality defect analysis of injection molding based on gradient enhanced Kriging model
AU - Wang, Zhuocheng
AU - Bo, Cuimei
AU - Sun, Zheng
AU - Li, Jun
AU - Gao, Furong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.
AB - In plastic injection molding (PIM), the process parameters affect the quality and productivity of molded parts. In this paper, we use orthogonal experiment design, numerical simulation, and metamodeling method to analyze the quality defect of process. The orthogonal experiment is to generate sampling points from the design space at different parameter levels and to determine key factors that affect product quality. For the sampling points, the numerical simulation is implemented to calculate the objective responses. Based on the sampling points and their corresponding response, a gradient enhanced Kriging (GEK) surrogate model strategy is applied to construct the response predictors to calculate the objective responses in the global design space. Last, we can analyze the surrogate model to look for available process parameters to improve product quality and production efficiency.
UR - http://www.scopus.com/inward/record.url?scp=85146260467&partnerID=8YFLogxK
U2 - 10.1109/IAI55780.2022.9976740
DO - 10.1109/IAI55780.2022.9976740
M3 - 会议稿件
AN - SCOPUS:85146260467
T3 - 4th International Conference on Industrial Artificial Intelligence, IAI 2022
BT - 4th International Conference on Industrial Artificial Intelligence, IAI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 4th International Conference on Industrial Artificial Intelligence, IAI 2022
Y2 - 24 August 2022 through 27 August 2022
ER -