Mechanical design, manufacture and automation: Research progress and fusion ant colony algorithm-based optimization

Abstract

Mechanical automation plays a pivotal role in modern engineering and manufacturing, offering a wide array of benefits. By applying algorithms to automate mechanical systems, industries can capitalize on advantages such as error reduction, increased efficiency, and enhanced production quality. Among the various algorithms used in mechanical automation, the fusion ant colony algorithm stands out as a particularly effective approach that has demonstrated promising results. The fusion ant colony algorithm draws inspiration from the collective behavior of ants and focuses on identifying the shortest path between two points. This algorithm, when applied to mechanical systems, empowers engineers to optimize numerous processes and elevate the overall performance of the system. Furthermore, the fusion ant colony algorithm possesses the potential to enhance the intelligence of mechanical systems. By incorporating artificial intelligence and machine learning techniques, this algorithm enables machines to learn and adapt to dynamic environments, thereby improving their efficiency and productivity. In summary, the fusion ant colony algorithm stands as a powerful tool for automating mechanical systems. Through the utilization of collective behavior principles and optimization, this algorithm significantly enhances the performance and intelligence of machines. The outcomes include improved efficiency, productivity, and quality within the realms of mechanical engineering and manufacturing. By embracing the fusion ant colony algorithm, industries can unlock the full potential of mechanical automation and achieve remarkable progress in their operations.