基于强化学习的瓷砖产线智能生产调控算法研究  

Intelligent Control Algorithm of Ceramic Tile Production Line Based on Reinforcement Learning

作　　者：程荣健 方毅翔 赵怡 张天助 李俊 王俊祥 CHENG Rongjian;FANG Yixiang;ZHAO Yi;ZHANG Tianzhu;LI Jun;WANG Junxiang(School of Mechanical and Electronic Engineering,Jingdezhen Ceramic University,Jingdezhen 333403,Jiangxi,China)

机构地区：[1]景德镇陶瓷大学机械电子工程学院,江西景德镇333403

出　　处：《陶瓷学报》2023年第4期776-785,共10页Journal of Ceramics

基　　金：国家自然科学基金(62062044,62063010);景德镇市级科技计划项目(2020ZDGG004)。

摘　　要：现代瓷砖生产过程可被看作是一个复杂的系统,其中一些调控行为会影响瓷砖最终的产品质量。传统瓷砖生产过程的工艺参数通常是通过多次实验确定的,这些实验一般基于工程师的经验行为。然而,经验行为往往难以准确确定工艺参数,同时最优工艺参数会随着实际工况(例如外部氛围)进行动态变化。在瓷砖产线中多变的工况影响下,很难保证产品质量的稳定性。为了解决经验主导的人工调控问题,并实现实际过程(工况)中的参数动态更新,首次提出了一种基于深度强化学习(DRL)算法的瓷砖产线智能调控框架。所构建的框架包含环境(Environment)模块和智能体(Agent)模块。其中,环境模块基于数据挖掘技术来模拟、更新瓷砖产线中的各种工况,并通过随机森林(RF)预测模型及时预测相应的产品质量。智能体调控模块能够根据预测的产品质量快速自适应调整工艺参数,使瓷砖产品达到预期产品质量。实验结果表明,该方法构建的预测模型准确性相较于其他同类方法的产品质量预测模型性能更好,平均提高率为2%。同时,经过多次迭代后,瓷砖产线智能调控算法可以将生产工艺参数合格提高到95%,具有较好的调控效果。The modern ceramic tile production process can be taken as a complex system,where various regulatory behaviors could affect the quality of the final ceramic tile products.The process parameters of traditional ceramic tile production are usually determined through multiple experiments,which are based on the experience and behavior of engineers.However,it is difficult to accurately determine process parameters based on empirical behavior.Also,optimal process parameters would dynamically change with actual operating conditions(such as external atmosphere).Under the influence of variable working conditions in ceramic tile production lines,it is difficult to ensure the stability of product quality.In order to solve the manual control problem dominated by experience and achieve dynamic parameter updates in actual processes(working conditions),an intelligent control framework is proposed for the first time for ceramic tile production lines,based on deep reinforcement learning(DRL)algorithm.The framework includes an Environment module and an Agent module.The Environment module simulates and updates various working conditions in the ceramic tile production line,based on data mining technology,where the corresponding product quality could be timely predicted through random forest(RF)prediction model.The intelligent Agent control module can quickly adaptively adjust process parameters based on the predicted product quality,so that quality of the ceramic tile product meets the expectation.It is found that the accuracy of the prediction model constructed by using this method is higher than that of similar methods,with an average improvement rate of 2%.At the same time,after multiple iterations,the intelligent control algorithm for the ceramic tile production lines can increase the qualification of production process parameters to 95%,with desirable control effects.

关 键 词：深度强化学习 瓷砖生产 工艺参数 随机森林 

分 类 号：TQ174.76[化学工程—陶瓷工业]