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標題:Machine Calibration Effect on the Optimization through Design of experiments (DOE) in Injection Mold
學年
學期
發表日期2020/04/28
作品名稱Machine Calibration Effect on the Optimization through Design of experiments (DOE) in Injection Mold
作品名稱(其他語言)
著者Rui-Ting Xu; Chao-Tsai Huang; Po-Hsuan Chen; Wen-Ren Jong; Rong-Yeu Chang
作品所屬單位
出版者
會議名稱SPE Annual Meeting, ANTEC2020
會議地點Online
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 revision and to fabrication. However, even using CAE, it doesn’t guarantee the quality factors obtained from CAE can be applied to real experiments. Moreover, the design of experiment (DOE) method has been utilized into injection molding product development. Today, there are still some challenges when people using DOE in injection molding. In this study, we have designed one injection molding system to define quality factor based on a circle plate. Then, we have tried to perform a series virtual DOE testing for injection molding using CAE to optimize the process condition. Furthermore, we also performed real DOE experiment to verify the virtual DOE concept. Finally, we will discuss about the machine calibration effect on the accuracy of quality comparison. Results shows that before machine calibrated, both virtual CAE-DOE and real DOE optimization can provide better quality for injection parts. However, there is some difference between the virtual and real DOE results. To find out why the difference between the virtual and real DOE results happened, we have investigated the machine feature and tried to calibrate it. After machine calibrated, the difference between the virtual and real DOE results has been improved by 58%.
關鍵字
語言中文
收錄於
會議性質國際
校內研討會地點
研討會時間20200428~20200505
通訊作者
國別中華民國
公開徵稿
出版型式
出處SPE Technical Papers, ANTEC2020-054, p.1-6
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