Research on Intelligent Algorithms for Estimating Multistep Process Capability in the Industrial Field

With the deep integration of artificial intelligence into industries such as automobiles, aerospace, transportation, chemicals, energy, and electricity, the research and development of intelligent manufacturing is on the rise. Artificial intelligence algorithms have achieved great success in single processes. However, due to the complexity of industrial processes and the dynamic changes in the environment, the research on reliability analysis and capability estimation of multi-step processes in the industrial field is relatively lacking. This article proposes an intelligent algorithm model DB_GJ based on parameter estimation and optimization theory for multi-step process capability estimation in the industrial field. The algorithm model calculates the optimal parameters for each step of the multi-step process from the perspective of global optimization, ensuring that the overall capability estimation is close to the true data distribution, thus achieving multi-step problem capability prediction. The algorithm model has good interpretability and has been extensively validated and analyzed on the dataset, proving the effectiveness and capability of the algorithm.