High efficiency far-infrared barrel heating control with excess heat prediction based on generalized predictive control in injection molding

Injection molding accounts for a huge share of the energy consumption of material processing. It has received considerable attention for its potential to enhance production efficiency, which relies on both product consistency and energy efficiency. Far-infrared heating, which has the advantages of high energy efficiency and low dissipation, has emerged as a promising heating technology in injection molding. However, the issue of low heat dissipation can lead to excess heat accumulation and cause substantial temperature errors. In this paper, a new barrel heating control method based on generalized predictive control (GPC) with real-time excess heat pre-diction model is proposed, in order to improve control accuracy and heating efficiency of far-infrared heating. A constrained multistep optimization combined with excess heat compensation is stated and an experimental platform is designed to validate the superiority of the proposed method under different operation scenarios. Experimental results show that the proposed method outperforms traditional proportional-integral-differential (PID) and GPC methods. Specifically, the proposed method can improve temperature control accuracy by 85.3 and 78.9 %, compared to PID and GPC, respectively. Such high temperature accuracy goes a long way in improving product consistency. Moreover, energy efficiency is improved, e.g., heating time is reduced by 42.7 and 16.5 %, energy consumption is reduced by 27.9 and 25.4 %, compared to PID and GPC, respectively. Control robustness is also verified under different operating conditions. In second-heating experiments, the temperature accuracy is improved by 84.4 % compared to PID. Therefore, the proposed method has the potential to enhance the production efficiency of injection molding.