Optimization for Intelligent Manufacturing Quality Inspectio

In the context of Industry 4.0 and intelligent manufacturing, enterprises encounter challenges in balancing quality assurance and inspection costs during multi - process production. This research utilizes binomial distribution theory to establish a sampling framework and a dynamic programming optimization model. Through Matlab simulation, it determines the optimal sample sizes for different confidence levels. For complex scenarios, a simulated annealing algorithm optimizes inspection decisions. For instance, in Scenario 1, a specific inspection - omission strategy maximizes the average profit per unit. The research findings can be applied to discrete manufacturing, with potential in green manufacturing, personalized customization, and supply chain management, promoting the high - quality development of the manufacturing industry.