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Author: MULAN -Plastic Molding Manufacturer
Process control strategy for wall thickness of blow molded products In industrial production, the parison wall thickness program controller is used to control the wall thickness of blow molded products, which has the disadvantages of relying on the experience of operators for repeated debugging, large consumption of raw materials and unstable product quality during the debugging process. As the complexity of blow molded products increases, it is difficult to obtain a suitable product wall thickness distribution by this method. To this end, this paper collects the control strategies for the wall thickness of blow molded products. 1. Modeling and strategy for iterative optimization and control of wall thickness of blow molded products The overall plan for the iterative optimization and control of the wall thickness of blow-molded products is proposed. The purpose is to reversely calculate how to set the die-mouth gap curve of the parison head according to the wall thickness requirements of the blow-molded products, so that the extruded parison can be blown. Products with a given wall thickness, while reducing the amount of material as much as possible. The program consists of two parts with 4 phases: (1) Using the finite element FE method, simulate the outer diameter and wall thickness distribution of the extruded parison under a given die gap and the wall thickness distribution of the blow molded product after the parison is inflated, and determine the initial die gap curve; (2) Based on the initial clearance curve, conduct experiments through orthogonal experimental design, conduct sensitivity analysis on the experimental results, invert the optimal clearance curve in theory, and conduct experiments according to this curve; (3) The NN model is established by using the wall thickness distribution of blow molded products obtained from the experiment and the corresponding die gap curve; (4) Use the above NN model to calculate the objective function value, and use the elite retention strategy and the genetic algorithm embedded with the simulated annealing algorithm to perform global optimization, derive a new die gap curve, and adjust the gap curve according to the product wall thickness requirements Iterative optimization, so that the final wall thickness of the blow molded product falls completely into the target range. 2. Intelligent control strategy for wall thickness of blow molded products On the basis of the above research, an intelligent control strategy for the wall thickness of blow molded products is proposed, which integrates numerical simulation, Z optimization technology, online detection and online control. The intelligent control strategy mainly includes the following points: (1) Optimization of parison wall thickness. Carry out finite element FE simulation on the blow molding process, use the simulation results to establish a neural network NN model between the parison wall thickness distribution and the optimization objective function, and use this model to solve the objective function in the optimization iteration process in real time; combined with multi-population parallel genetic algorithm , to obtain the required wall thickness of the blow molded product as the goal, and establish a mathematical model for the optimization of the parison wall thickness; The optimized parison wall thickness distribution curve is obtained by solving the model, and the optimized initial die gap curve is determined according to the relationship between the die gap and the parison wall thickness obtained from the experiment. (2) On-line detection of product wall thickness. Construct a multi-channel ultrasonic detection device, develop detection software with multi-channel data acquisition, signal processing, wall thickness identification and wall thickness output functions, and realize the in-mold online detection of the wall thickness of blow molded products during the blow molding process. (3) Design of self-tuning fuzzy high-order PD iterative learning control algorithm. Combining the fuzzy algorithm and iterative learning control algorithm, a self-tuning fuzzy high-order PD iterative learning control algorithm is proposed, which adds a self-tuning fuzzy high-order PD controller on the basis of iterative learning, giving full play to iterative learning control and fuzzy The advantage of the control is that it improves the robustness and guarantees the accuracy. Proficient in blow molding|custom blow molding products.