Machine Calibration Effect on the Optimization through Design of experiments (DOE) in Injection Molding
出版日期:2020-05-05 00:00:00
著者:Rui-Ting Xu; Chao-Tsai (CT) Huang; Po-Hsuan Chen; Wen-Ren Jong; Rong-Yeu Chang
著錄名稱、卷期、頁數:SPE Technical Papers, ANTEC2020-054(6 pages)
摘要:Quality issue is one of the most important concerns in injection molding. However, before executing mass production, how to retain good quality is one of the crucial factors in injection molding. To retain good quality, it is commonly using CAE to assist from original design to revise and to fabricate. However, even using CAE, it doesn’t guarantee the quality factors obtained from CAE can be applied to real experiments directly. Moreover, the design of experiment (DOE) method has been utilized into injection molding product development for a long time. Today, there are still some challenges by using DOE in injection molding. In this study, a circle plate was selected as the system.. Then, the system was used to perform a series virtual DOE testing for injection molding using CAE to optimize the process conditions. Furthermore, some real DOE experiments were perormed to verify the virtual DOE concept. Finally, the machine calibration effect on the accuracy of quality is discussed. Results show that before machine calibrated, both virtual CAE-DOE and real DOE optimization can provide better quality for injection parts comparing to the original design. However, there is some difference between the virtual CAE-DOE and real DOE results. To find out why the difference happened between the virtual and real DOE results, the machine features have been investigated and the machine was calibrated. After machine calibrated, the difference between the virtual CAE-DOE and real DOE results has been improved by 58%.
關鍵字:Injection molding;CAE;design of experiment (DOE);machine calibration
語言:en_US
期刊性質:國外
收錄於:EI
通訊作者:黃招財
審稿制度:是
國別:USA
出版型式:電子版