Research on manufacturing quality improvement based on product gene evaluation method and a meta-heuristic algorithm with hybrid encoding scheme

Product manufacturing quality is influenced by various factors of the production process, so the key to improve product manufacturing quality is improving the combination of the relevant parameters and processing methods in the manufacturing process. Aiming at this issue, a manufacturing quality improvement method using gene recombination and editing mechanism is proposed. In this method, an optimization model is established and described by formulas, in which three optimization objectives including production quality, costs, and time are involved. In the model, the quality indicator is measured by a comprehensive evaluation approach of product gene. To address the model, an improved genetic algorithm (GA) and artificial bee colony algorithm (ABC) with hybrid encoding scheme (H-IGA-IABC) is designed by considering the different types of gene elements. Fifteen comparison experiments with different scales are performed to test the model and H-IGA-IABC. According to the data obtained by different components and algorithms, the search ability, speed of convergence of H-IGA-IABC are better than that of other components and algorithms, especially in solving large-scale problems. Compared with the solution before optimization, the quality evaluation results and other indicators of the solutions after optimization are significantly better. Therefore, the proposed method is effective and performs well.