A hybrid optimization approach for intelligent manufacturing in plastic injection molding by using artificial neural network and genetic algorithm

Abstract

This study presents a novel hybrid optimization approach for intelligent manufacturing in plastic injection molding (PIM). It focuses on globally optimizing process parameters to ensure high-quality products while reducing cycle time, material waste, and energy consumption. The method combines a backpropagation neural network (BPNN) with a genetic algorithm (GA) and employs a multi-objective optimization model based on design of experiments (DoE). A BP artificial neural network captures the relationship between optimization goals and process parameters. Leveraging the genetic algorithm, it effectively optimizes process parameters for achieving global optimization goals. The case study involves a polypropylene product, considering dimensional deviation, weight, cycle time, and energy consumption during the PIM cycle. Design variables include melt temperature, injection velocity, injection pressure, commutation position, holding pressure, holding time, and cooling time. The results demonstrate that this approach efficiently adjusts process parameters to meet quality standards, significantly reducing raw material consumption (2%), cycle time (12%), and energy consumption (16%). This offers substantial benefits for companies in highly competitive markets demanding swift adoption of smart production methods.

Prediction of Ultimate Bearing Capacity of Soil–Cement Mixed Pile Composite Foundation Using SA-IRMO-BPNN Model

“…This mapped the original data to the interval [0, 1], thereby mitigating the influence of variable dimensions on convergence speed. The data are de-normalized to revert them to the original order of magnitude after the network activity is completed, with the restoration formula given by (17). Let x ′ denote the normalized value obtained from the original data represented by x.…”

“…They employed the regularization backpropagation technique (BRB) for network training, and the resulting output values closely matched the measured values, showcasing the robustness and reliability of the FFNN model. EL et al [17] developed a novel multi-hybrid optimization model based on Design of Experiments (DoE), which combined reverse-propagated neural networks and genetic algorithms (GAs) to optimize the parameters of a BP neural network and process parameters. Ren et al [18] developed a new technique for calculating the UBC of pile foundations by optimizing BPNN utilizing the adaptive genetic algorithm and the adaptive particle swarm optimization algorithm.…”