Machining Scheme Selection Technique for Feature Group Based on Re-Optimized Bacterial Foraging Algorithm

Machining scheme selection is presently time-consuming and inefficient due to singularity and uncertainty in decision-making. Typically, the selection is based on a singular feature, with eventual outcomes remaining uncertain. In response, this paper proposes a feature-group-oriented re-optimized bacterial foraging algorithm to solve this problem by selecting the optimal machining schemes for multiple similar features in one part at once. Our focus is on designing the re-optimized bacterial foraging algorithm to optimize machining schemes, which considers the processes of chemotaxis, fine-tuning, replication, and adaptive migration with re-optimization. Then, comparative studies with varying weights and algorithms are conducted as an illustration of machining scheme selection for the hole feature-group. The results indicate that the re-optimized bacterial foraging algorithm accurately produces three distinct machining schemes based on varying weights. Additionally, the optimal average value outperforms other algorithms in the comparative study, demonstrating the algorithm's validity and superiority.