PID control, intelligent control, manufacturing automation, particle swarm optimization, neural network, adaptive algorithm Abstract: Abstract:The automation of manufacturing has created an urgent need for the intelligentization of control algorithms. Building upon traditional PID control, this paper introduces an intelligent control algorithm model, which incorporates machine learning and swarm intelligence optimization techniques, aimed at enhancing the control performance in complex manufacturing processes. Initially, the constraints of PID control in handling nonlinear and time-varying systems are examined. Subsequently, an adaptive PID controller is designed incorporatingmbining the latest mathematical methods and intelligent algorithms: the Particle Swarm Optimization (PSO) algorithm is used for the initial parameter tuning of PID, and the control parameters are adjusted online through neural networks to achieve the self - learning and adaptability of the controller. The simulation experiments, which took a typical manufacturing process and mechanical arm servo system as examples, demonstrated that the proposed algorithm significantly improved the overshoot and response speed, as evidenced by similar studies showing the superiority of non-linear PID control algorithms in reducing overshoot and enhancing system performance. steady-state response, accuracy, and robustness compared with the traditional PID. The findings of this study offer a robust solution for intricate control challenges in intelligent manufacturing, leveraging the proven efficacy of PID control algorithms. https://mc.manuscriptcentral.com/jcmse Journal of Computational Methods in Science and Engineering For Peer Review Design and Engineering Implementation of Intelligent Control Algorithms for Manufacturing Automation Based on Deep Learning Chen Jun Zhou Li Juan Jiangsu Gaochun Secondary Professional School, Nanjing 211306, Jiangsu Province, China Email:cj2006.111@163.com Abstract:The automation of manufacturing has created an urgent need for the intelligentization of control algorithms. Building upon traditional PID control, this paper introduces an intelligent control algorithm model, which incorporates machine learning and swarm intelligence optimization techniques, aimed at enhancing the control performance in complex manufacturing processes. Initially, the constraints of PID control in handling nonlinear and time-varying systems are examined. Subsequently, an adaptive PID controller is designed incorporatingmbining the latest mathematical methods and intelligent algorithms: the Particle Swarm Optimization (PSO) algorithm is used for the initial parameter tuning of PID, and the control parameters are adjusted online through neural networks to achieve the self - learning and adaptability of the controller. The simulation experiments, which took a typical manufacturing process and mechanical arm servo system as examples, demonstrated that the proposed algorithm significantly improved the overshoot and response speed, as evidenced by similar studies showing the superiority of non-linear PID control algorithms in reducing overshoot and enhancing system performance. steady-state response, accuracy, and robustness compared with the traditional PID. The findings of this study offer a robust solution for intricate control challenges in intelligent manufacturing, leveraging the proven efficacy of PID control algorithms. Keywords: PID control; intelligent control; manufacturing automation; particle swarm optimization; neural network; adaptive algorithm 1.