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Conference Papers
Conference Papers
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2025
2025
Development and Application of an AI-Assisted System for Enhancing the Efficiency of Plastic Mold Design
Ji-Fang Chen
ANTEC (2025)
With advances in injection molding and diverse manufacturing demands, accelerating mold development and improving quality have become key challenges. This study proposes an AI-assisted mold design system integrating database matching and predictive models...
Application of AI optimization in mold flow analysis
Yi-Ting Chen, Ching-Yu Fan, Shu-Ping Hu
ANTEC (2025)
Traditional optimization of injection-molded product quality relies heavily on trial-and-error or DOE methods, whose efficiency and flexibility become limited when dealing with multi-objective and constrained problems. Such problems often involve conflicting quality factors and require...
Towards High-Fidelity Simulation of Fiber-Reinforced Thermoplastics Using AI-Driven Intelligence and Molding Simulation
Arash Jahangir, Joshua Thean, Jim Hsu
ANTEC (2025)
Fiber-Reinforced Thermoplastics (FRTs), in both short- and long-fiber forms, are increasingly adopted in aerospace, automotive, and electronics industries due to their lightweight and high-performance characteristics. However, their broader use has been hindered by the limitations...
Integrating Weld Line Effects from Molding Processes into Structural FEA
Chih-Yi Chung, Ming-Yu Lin, Li-Hsuan Shen, Chih-Chung Hsu
ANTEC (2025)
Weld lines can significantly reduce the strength of injection-molded parts. This work introduces a workflow to incorporate weld line effects into structural FEA. For fiber-filled polymers, weld line regions are identified through orientation and meeting angle, while for unfilled polymers...
提升塑膠模具設計效率之AI輔助系統研究與實作
Ji-Fang Chen
模具公會 (2025)
隨著塑膠射出成型技術的進步與製造需求的多樣化，如何加速模具開發流程並提升成型品質，已成為當前產業關注的重點。本文提出一套結合資料庫比對與人工智慧模型的智慧化塑膠模具設計輔助系統，旨在協助工程師在模具設計初期能進行快速分析與成型參數預測，從而提升設計效率...
AI最佳化於模流分析中的應用
Yi-Ting Chen, Ching-Yu Fan, Shu-Ping Hu
模具公會 (2025)
隨著人工智慧(AI)技術於各領域迅速發展，工程領域亦迎來前所未有的變革契機。以往射出成型產品的品質優化，常仰賴試誤法（Trial and Error）與實驗設計（Design of Experiments, DOE）等方法進行改善，這些傳統手法雖具系統性，卻在面對複雜條件或多變參數時，可能面臨資源與時間...