Abstract: In the textile industry, the flat knitting machine plays a crucial role as a production tool, and the quality of its weaving path is closely related to the overall product quality and production efficiency. Seeking to improve and optimize the knitting path to improve product effectiveness and productivity has become an urgent concern for the textile industry. This article elegantly streamlines and enhances the intricate weaving process of fabrics, harnessing the formidable power of reinforcement learning to achieve unparalleled optimization of weaving paths on a flat knitting machine. By ingeniously integrating reinforcement learning technology into the fabric production realm, we aspire to elevate both the quality and production efficiency of textiles to new heights. The core of our approach lies in meticulously defining a state space, action space, and a tailored reward function, each meticulously crafted to mirror the intricacies of the knitting process. This model serves as the cornerstone upon which we construct an innovative knitting pathway optimization algorithm, deeply rooted in the principles of reinforcement learning. Our algorithm embodies a relentless pursuit of excellence, learning from its interactions with the dynamic environment, embracing a methodical trial-and-error approach, and continuously refining its decision-making strategy. Its ultimate goal: to maximize the long-term cumulative reward, ensuring that every stitch contributes to the overall optimization of the weaving process. In essence, we have forged a groundbreaking collaboration between the traditional art of fabric weaving and the cutting-edge science of reinforcement learning, ushering in a new era of intelligent and efficient textile production. Through this process of iterative optimization, the agent can gradually learn the optimal knitting path. To verify the effectiveness of the algorithm, we performed extensive experimental validation. The experimental results show that reinforcement learning can significantly improve knitting efficiency, improve the appearance and feel of fabrics. Compared with traditional methods, the method proposed in this article has a higher level of automation and better adaptability, achieving more efficient and intelligent knitting production, with a 10% increase in production efficiency.

Tianqi Yang, “Optimization of Knitting Path of Flat Knitting Machine Based on Reinforcement Learning” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0150839

@article{Yang2024,
title = {Optimization of Knitting Path of Flat Knitting Machine Based on Reinforcement Learning},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0150839},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0150839},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Tianqi Yang}
}