射出成型產品樣式多元且幾何結構複雜，為了生產出品質優良的產品，往往需要使用多段射出的設定，以控制熔膠的流動行為，使熔膠在流經模穴不同區域時能配合模具幾何特性，避免產品出現流痕、燒焦與包風等缺陷，並改善翹曲變形等問題。此外，過去針對多段射出成型與產品品質之關係的研究相對較少，因此本研究將透過多段射出成型的設定，並以產品重量與外觀作為品質指標進行深入的探討。
由於現今射出成型產業試模之參數主要仰賴技師的經驗而設置，這種以主觀的方式進行參數設置會導致產品品質較不一致，因此，本研究於機台安裝射嘴壓力感測器與哥林柱應變感測器，使用兩個模外感測器以較低的成本實現製程監控，探討多段射出成型對產品品質之影響，並建立一套具參考性的多段射出成型製程參數優化流程與方法，以提升產品品質的一致性。射出成型在長時間連續生產下容易受到環境溫度、材料批次等外部因素的干擾，進而影響產品品質，因此，本研究以優化後的多段射出成型製程參數進行連續生產，並建立自適應品質控制系統，比較有無使用系統下的產品品質，此外，採用過往文獻之單段射出成型參數搭配本研究開發的自適應品質控制系統進行比較，最後於不同日期執行第二次相同自適應品質控制實驗，以驗證系統的有效性。
實驗結果顯示，將感測曲線的特徵值定義為線上品質因子，可以用來決定適當的製程參數，依序優化第一段射出速度、第二段射出速度、第三段射出速度、V/P切換位置、保壓壓力、保壓時間與鎖模力，藉由多段射出的設置可以解決產品外觀缺陷。本研究所建立的自適應品質控制系統可以維持多段與單段射出於長時間連續生產下的產品品質，第一次多段與單段射出成型使用系統之實驗，重量變異係數分別降至0.058%與0.042%，第二次實驗分別降至0.046%與0.056%。使用多段射出成型結合自適應品質控制除了能有效降低產品品質變異，也能避免產品外觀產生缺陷，同時達到維持產品重量與外觀品質的目標。

Injection molded products often exhibit diverse designs and complex geometries. Multi-stage injection molding settings are usually adopted to control melt flow behavior to ensure high product quality. This approach allows the molten plastic to match the varying geometric characteristics of the mold cavity as it fills different regions, thereby preventing defects such as flow marks, burn marks, and air traps, while reducing warpage.
This study installed a nozzle pressure sensor and a tie-bar strain sensor on the injection molding machine, utilizing two external sensors to achieve low-cost process monitoring. An optimization procedure and method were established for multi-stage injection molding process parameters to improve product quality consistency. Injection molding is susceptible to external factors during long-term continuous production, which can affect product quality. Therefore, this study conducted continuous production using optimized multi-stage molding process parameters and developed an adaptive quality control system to compare product quality with and without the implementation of the system. Furthermore, to verify the system's effectiveness, single-stage injection molding parameters from a previous study were adopted with the adaptive quality control system developed. Finally, the second experiments using the same adaptive quality control method were conducted on a different date to validate the results further.
The results show that online quality indexes can be used to determine appropriate process parameters, sequentially optimizing first-stage injection speed, second-stage injection speed, third-stage injection speed, V/P switchover point, packing pressure, packing time and clamping force. Through multi-stage injection settings, product appearance defects can be effectively resolved. The adaptive quality control system developed in this study effectively maintains consistent product quality during continuous production in both multi-stage and single-stage injection molding processes. In the first experiment using the system for both multi-stage and single-stage injection molding, the weight coefficient of variation was reduced to 0.058% and 0.042%, respectively. The second experiment reduced it to 0.046% and 0.056%, respectively. The use of multi-stage injection molding combined with adaptive quality control not only effectively reduces product quality variation but also prevents the occurrence of appearance defects.

[1] Yi-Sheng Chen, Kuo-Tsai Wu, Ming-Hong Tsai, Sheng-Jye Hwang, Huei-Huang Lee, Hsin-Shu Peng and Hsiao-Yeh Chu, “Adaptive process control of the changeover point for injection molding process,” Journal of Low Frequency Noise, Vibration and Active Control, Vol. 40, no. 1, pp. 383-394 (2021).

[2] Ming-Hong Tsai, Jia-Chen Fan-Jiang, Guan-Yan Liou, Feng-Jung Cheng, Sheng-Jye Hwang, Hsin-Shu Peng and Hsiao-Yeh Chu, “Development of an online quality control system for injection molding process,” Polymers, Vol. 14, no. 8, p. 1607 (2022).

[3] Jia-Chen Fan-Jiang, Chi-Wei Su, Guan-Yan Liou, Sheng-Jye Hwang, Huei-Huang Lee, Hsin-Shu Peng and Hsiao-Yeh Chu, “Study of an online monitoring adaptive system for an injection molding process based on a nozzle pressure curve,” Polymers, Vol. 13, no. 4, p. 555 (2021).

[4] Chi-Wei Su, Wei-Jie Su, Feng-Jung Cheng, Guan-Yan Liou, Sheng-Jye Hwang, Hsin-Shu Peng and Hsiao-Yeh Chu, “Optimization process parameters and adaptive quality monitoring injection molding process for materials with different viscosity,” Polymer Testing, Vol. 109, p. 107526 (2022).

[5] Guan-Yan Liou, Wei-Jie Su, Feng-Jung Cheng, Chen-Hsiang Chang, Ren-Ho Tseng, Sheng-Jye Hwang, Hsin-Shu Peng and Hsiao-Yeh Chu, “Optimize injection-molding process parameters and build an adaptive process control system based on nozzle pressure profile and clamping force,” Polymers, Vol. 15, no. 3, p. 610 (2023).

[6] Feng-Jung Cheng, Chen-Hsiang Chang, Chien-Hung Wen, Sheng-Jye Hwang, Hsin-Shu Peng and Hsiao-Yeh Chu, “Out-of-mold sensor-based process parameter optimization and adaptive process quality control for hot runner thin-walled injection-molded parts,” Polymers, Vol. 16, no. 8, p. 1057 (2024).

[7] Ren-Ho Tseng, Chien-Hung Wen, Chen-Hsiang Chang, Yu-Hao Chen, Chieh-Hsun Tsai and Sheng-Jye Hwang, “Nozzle Pressure-and Screw Position-Based CAE Scientific Process Parameter Setup for Injection Molding Process,” Polymers, Vol. 17, no. 2, p. 198 (2025).

[8] Chen-Hsiang Chang, Chien-Hung Wen, Ren-Ho Tseng, Chieh-Hsun Tsai, Yu-Hao Chen, Sheng-Jye Hwang and Hsin-Shu Peng, “Scientific Molding and Adaptive Process Quality Control with External Sensors for Injection Molding Process,” Technologies, Vol. 13, no. 3, p. 97 (2025).

[9] Jian-Yu Chen, Kai-Jie Yang and Ming-Shyan Huang, “Online quality monitoring of molten resin in injection molding,” International Journal of Heat and Mass Transfer, Vol. 122, pp. 681-693 (2018).

[10] Hao-Hsuan Tsou, Chung-Ching Huang, Ting-Wei Zhao and Zhi-Hao Wang, “Design and validation of sensor installation for online injection molding sidewall deformation monitoring,” Measurement, Vol. 205, p. 112200 (2022).

[11] Shia-Chung Chen, Bi-Lin Tsai, Cheng-Chang Hsieh, Nien-Tien Cheng, En-Nien Shen and Ching-Te Feng, “Prediction of Part Shrinkage for Injection Molded Crystalline Polymer via Cavity Pressure and Melt Temperature Monitoring,” Applied Sciences, Vol. 13, no. 17, p. 9884 (2023).

[12] Kai-Fu Liew, Hsin-Shu Peng, Po-Wei Huang and Wei-Jie Su, “Injection barrel/nozzle/mold-cavity scientific real-time sensing and molding quality monitoring for different polymer-material processes,” Sensors, Vol. 22, no. 13, p. 4792 (2022).

[13] Quan Wang, Xiaomei Zhao, Jianpeng Zhang, Ping Zhang, Xinwei Wang, Chaofeng Yang, Jinrong Wang and Zhenghuan Wu, “Research on quality characterization method of micro-injection products based on cavity pressure,” Polymers, Vol. 13, no. 16, p. 2755 (2021).

[14] Ming-Shyan Huang, Jian-Yu Chen and Yu-Qi Xiao, “Quality monitoring of micro-shrinkage defects in thick-walled injection molded components,” Measurement, Vol. 201, p. 111733 (2022).

[15] Hao-Hsuan Tsou, Chung-Ching Huang, Yi-Cheng Chen and Syu-Yang Shih, “Online detection of residual stress near the gate using cavity pressure for injection molding,” Journal of Polymer Engineering, Vol. 43, no. 1, pp. 89-99 (2023).

[16] Ming‐Shyan Huang, Shih‐Chih Nian and Guan‐Ting Lin, “Influence of V/P switchover point, injection speed, and holding pressure on quality consistency of injection‐molded parts,” Journal of Applied Polymer Science, Vol. 138, no. 41, p. 51223 (2021).

[17] Jian-Yu Chen, Ping-Han Hung and Ming-Shyan Huang, “Determination of process parameters based on cavity pressure characteristics to enhance quality uniformity in injection molding,” International Journal of Heat and Mass Transfer, Vol. 180, p. 121788 (2021).

[18] Shih-Chih Nian, Yung-Chih Fang and Ming-Shyan Huang, “In-mold and Machine Sensing and Feature Extraction for Optimized IC-tray Manufacturing,” Polymers, Vol. 11, no. 8, p. 1348 (2019).

[19] Yung‐Hsiang Chang, Tzu‐Hsiang Wei, Shia‐Chung Chen and Ying‐Fan Lou, “The investigation on PVT control method establishment for scientific injection molding parameter setting and its quality control,” Polymer Engineering & Science, Vol. 60, no. 11, pp. 2895-2907 (2020).

[20] Jian‐Yu Chen, Liang‐Ci Wong and Ming‐Shyan Huang, “Quality monitoring and control for plasticization of acrylonitrile‐butadiene‐styrene regrind polymer in injection molding,” Polymer Engineering & Science, Vol. 64, no. 3, pp. 1057-1070 (2024).

[21] Ming‐Shyan Huang, Kun‐Cheng Ke and Chun‐Ying Liu, “Cavity pressure‐based holding pressure adjustment for enhancing the consistency of injection molding quality,” Journal of Applied Polymer Science, Vol. 138, no. 18, p. 50357 (2021).

[22] Xundao Zhou, Yun Zhang, Ting Mao and Huamin Zhou, “Monitoring and dynamic control of quality stability for injection molding process,” Journal of Materials Processing Technology, Vol. 249, pp. 358-366 (2017).

[23] Yuxuan Xu, Pengcheng Xie, Nanhong Fu, Xiaolong Jiao, Jinling Wang, Gang Liu, Xiyu Dou, Yan Zha, Kaifang Dang and Weimin Yang, “Self‐optimization of the V/P switchover and packing pressure for online viscosity compensation during injection molding,” Polymer Engineering & Science, Vol. 62, no. 4, pp. 1114-1123 (2022).

[24] Joshua Krantz, Zarek Nieduzak, Juliana Licata, Sarah O'Meara, Peng Gao and Davide Masato, “In‐mold rheology and automated process control for injection molding of recycled polypropylene,” Polymer Engineering & Science, Vol. 64, pp. 4112-4127 (2024).

[25] Hanjui Chang, Zhiming Su, Shuzhou Lu and Guangyi Zhang, “Intelligent predicting of product quality of injection molding recycled materials based on tie-bar elongation,” Polymers, Vol. 14, no. 4, p. 679 (2022).

[26] Reinhard Schiffers, Stefan Kruppa, and Stefan Moser, “The Right Changeover Point for Each Shot,” Journal Kunststoffe, Vol. 11, pp. 26-29 (2014).

[27] Wei‐Jie Su and Hsin‐Shu Peng, “A real‐time clamping force measurement eigenvalue for prediction, adjustment, and control of injection product quality,” Polymer Engineering & Science, Vol. 61, no. 2, pp. 420-431 (2021).

[28] John P. Beaumont, Runner and gating design handbook: tools for successful injection molding, Carl Hanser Verlag GmbH Co KG, Germany (2019).

[29] Suhas Kulkarni, Robust process development and scientific molding: theory and practice, Carl Hanser Verlag GmbH Co KG, Germany (2017).

[30] Anon, Sasol Limited, 2024. Retrieved from: https://products.sasol.com/pic/products/home/grades/ZA/5hnr100/index.html (accessed on 23 July 2024).

[31] Anon, Dynisco, 2024. Retrieved from: https://www.dynisco.com/userfiles/files/Datasheets/pt4656_5.pdf (accessed on 23 July 2024).

[32] Anon, Gefran, 2024. Retrieved from: https://www.gefran.com/products/strain-and-force-sensors/strain/ge1029-tie-bar-strain-sensor/ (accessed on 23 July 2024).

[33] Anon, Advantech, 2024. Retrieved from: https://advdownload.advantech.com/productfile/PIS/USB-4716/file/USB-4716_DS(121621)20211220135341.pdf (accessed on 23 July 2024).