Scholarlink

<p>This study establishes an adaptive model predictive control (AMPC) framework for energy optimization in plastic injection molding machines. Traditional fixed-gain PID controllers struggle to cope with material variability and machine aging, leading to pressure fluctuations and low energy efficiency. By integrating a Lyapunov-based adaptive gain mechanism, injection and holding pressures can be dynamically adjusted, thereby minimizing energy consumption while ensuring process stability. This study developed a comprehensive mathematical model that considers factors such as pressure compensation, material viscosity, and equipment aging. Experimental results on the FCS AF-50 platform show that the proposed AMPC method reduces total energy consumption per cycle by 8.7% compared to PID control. Specifically, power consumption during the holding and cooling phases is reduced by 25% and 40%, respectively. Furthermore, the system suppresses 32.5% of pressure fluctuations, resulting in a 74% improvement in thermal uniformity. In-mold monitoring confirms that the maximum temperature difference can be reduced from 17.2&#xa0;°C to 4.5&#xa0;°C, validating the robustness of this strategy in achieving high-precision manufacturing. This adaptive control method is applicable to various materials and aging scenarios and improves the gain regulation efficiency of intelligent injection molding.</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

Adaptive model predictive control with Lyapunov-based compensation for energy optimization in injection molding machines

  • 期刊论文

  • 发表日期：

    2026年3月2日

查看全文
  • Yu-Fan Wu,
  • Wu-Sung Yao

摘要

This study establishes an adaptive model predictive control (AMPC) framework for energy optimization in plastic injection molding machines. Traditional fixed-gain PID controllers struggle to cope with material variability and machine aging, leading to pressure fluctuations and low energy efficiency. By integrating a Lyapunov-based adaptive gain mechanism, injection and holding pressures can be dynamically adjusted, thereby minimizing energy consumption while ensuring process stability. This study developed a comprehensive mathematical model that considers factors such as pressure compensation, material viscosity, and equipment aging. Experimental results on the FCS AF-50 platform show that the proposed AMPC method reduces total energy consumption per cycle by 8.7% compared to PID control. Specifically, power consumption during the holding and cooling phases is reduced by 25% and 40%, respectively. Furthermore, the system suppresses 32.5% of pressure fluctuations, resulting in a 74% improvement in thermal uniformity. In-mold monitoring confirms that the maximum temperature difference can be reduced from 17.2 °C to 4.5 °C, validating the robustness of this strategy in achieving high-precision manufacturing. This adaptive control method is applicable to various materials and aging scenarios and improves the gain regulation efficiency of intelligent injection molding.