Scholarlink

<p>The efficiency and quality of clamping mechanisms in injection molding machines are critical yet challenging to optimize for diverse applications. This paper focuses on the design and optimization of a five-point double-toggle clamping mechanism across various machine capacities. A mathematical model is developed to evaluate key performance metrics such as force amplification, link length, stroke ratio, and spatial occupation. Parametric studies reveal complex interdependencies among geometric variables, necessitating numerical optimization. Finite element analysis verifies the structural adequacy of optimized cross-sections under maximum loading. Both single- and multi-objective optimization methods, including COBYLA, trust-constr, genetic algorithm, NSGA-II, and SMSEMOA, are applied to improve performance and identify optimal tradeoffs. COBYLA demonstrates strong computational efficiency and accuracy, while SMSEMOA offers well-distributed Pareto-optimal solutions. Eventually, the proposed framework is applied to injection molding machines ranging from 30 to 300 tons, confirming its adaptability and scalability.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Optimization of clamping mechanism in injection molding machines considering a wide range of operational capacities

  • 期刊论文

  • 发表日期：

    2026年3月9日

查看全文
  • Van Thanh Hoang,
  • Doan Hung Vo,
  • Thanh Tung Nguyen,
  • Minh Thong Tran,
  • Duc Thang Tran,
  • Hyungbum Park

摘要

The efficiency and quality of clamping mechanisms in injection molding machines are critical yet challenging to optimize for diverse applications. This paper focuses on the design and optimization of a five-point double-toggle clamping mechanism across various machine capacities. A mathematical model is developed to evaluate key performance metrics such as force amplification, link length, stroke ratio, and spatial occupation. Parametric studies reveal complex interdependencies among geometric variables, necessitating numerical optimization. Finite element analysis verifies the structural adequacy of optimized cross-sections under maximum loading. Both single- and multi-objective optimization methods, including COBYLA, trust-constr, genetic algorithm, NSGA-II, and SMSEMOA, are applied to improve performance and identify optimal tradeoffs. COBYLA demonstrates strong computational efficiency and accuracy, while SMSEMOA offers well-distributed Pareto-optimal solutions. Eventually, the proposed framework is applied to injection molding machines ranging from 30 to 300 tons, confirming its adaptability and scalability.