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Microstructural Optimization and Corrosion Resistance Enhancement of Austenitic 317L Stainless Steel through Tailored Heat Treatment

Austenitic 317L stainless steel was favored in many industrial applications due to its excellent corrosion resistance and mechanical properties. The study involved heat treating Austenitic 317L stainless steel samples at temperatures of 500℃, 950℃, and 1100℃ to explore the effects of heat treatment temperature on microstructure and corrosion resistance. Electrochemical analysis showed that the 317L sample treated at 1100℃ exhibited the lowest passive current density, indicating the best improvement in corrosion resistance at this temperature. Results from corrosion weight loss experiments confirmed that the least weight loss occurred under the heat treatment conditions of 950℃ and 1100℃, suggesting enhanced corrosion resistance of the material. Microstructural characterization revealed that after heat treatment at 950℃, the metallographic structure transformed from a complex, irregular size and chaotic growth pattern to uniformly grown and comparatively equal-sized metallographic structures. Furthermore, heat treatment at 1100℃ resulted in larger metallographic structures with reduced boundary width and distribution density. Consequently, enhanced corrosion resistance was observed at both temperatures. Based on these findings, a heat treatment range of 950℃ to 1150℃ appeared to be a suitable post-processing method for optimizing the microstructure of 317L while concurrently improving its corrosion resistance.

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Intelligent optimization of inspection route based on multi-population integer coding particle swarm optimization algorithm

Aiming at the problems of premature convergence and slow convergence speed in the optimization process of single population particle swarm optimization algorithm, in order to improve the global convergence performance of particle swarm optimization algorithm, a hierarchical three-population integer coded particle swarm optimization algorithm based on grade evaluation with parallel structure is proposed and used to solve the traveling salesman problem (TSP). The algorithm imitates the form of biological aggregation in nature. In the initialization stage, three independent populations are generated according to different particle fitness, including an elite population composed of small-scale individuals with high fitness and two large-scale civilian populations composed of remaining individuals. The three populations apply the immigration strategy based on grade evaluation to exchange particles after a certain number of evolutionary generations. By applying the grade evaluation strategy, the natural law of the biological cluster is integrated into the particle swarm optimization algorithm, which effectively improves the optimization efficiency of the particle swarm optimization algorithm. The above algorithm is applied to the inspection route optimization of one of the traveling salesman problems. The results show that the improved algorithm is superior to the single population particle swarm optimization algorithm in terms of convergence speed, global optimization ability and stability.

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