Research And Implementation Of Intelligent Production Management And Control Technology In MES For Daily Ceramics

With the continuous development of information technology,Manufacturing Execution System(MES)as an important production management tool is gradually gaining wide application.As one of the important industries in the manufacturing sector,daily-use ceramics production management technology,i.e.,how to realize data digitization and production management intelligence during the production process to further improve production efficiency and product quality,has attracted more and more attention and research.To address the problems of unreasonable production planning and difficult control of production firing process in the daily-use ceramics production management,this thesis proposes a daily-use ceramics intelligent production control technology.This technology not only combines knowledge graph and deep learning for reasonable and efficient scheduling and dispatching but also achieves production control of ceramic kilns through adaptive fuzzy control.This thesis mainly conducts the following research:(1)In response to the characteristics of diversified product types,complex process relationships,and miscellaneous in daily-use ceramics production,a knowledge graph-based adaptive scheduling algorithm is proposed.This algorithm first constructs a knowledge graph for scheduling by integrating real-time production data and static data to provide timely information for scheduling strategies,making production plans more in line with actual enterprise situations.Then,the scheduling process is divided into two stages,each setting optimization objectives and using deep reinforcement learning to make scheduling decisions,achieving the purpose of reasonable and efficient scheduling.Finally,through simulation experiments,the effectiveness of the algorithm is verified by comparing it with other algorithms from multiple aspects such as algorithm complexity,convergence,and optimization objectives.The results show that the algorithm significantly shortens the delivery period and improves equipment utilization in the case of dynamic insertion compared with other algorithms.(2)To solve the problems of hysteresis,parameter variability,and multivariable coupling in daily-use ceramic kilns,an adaptive parameter adjustment algorithm for ceramic production based on fuzzy control is proposed.This algorithm uses a dual fuzzy controller,which can be efficiently and reasonably applied to the firing process of ceramic kilns,effectively solving the problem of large numbers of control rules and parameters that are difficult to optimize in traditional fuzzy control algorithms.Moreover,the algorithm’s control accuracy and reliability are further enhanced by the method of adaptive updating control parameters.Finally,the effectiveness of the algorithm is verified through simulation experiments,and the results show that the algorithm has good adaptive ability and robustness in correctly and incorrectly formulating fuzzy control rules and can effectively control the firing process of daily-use ceramic kilns,thereby improving production efficiency and product quality.(3)A daily-use ceramics intelligent production control system is designed and implemented.The system adopts a four-layer architecture of the Internet of Things,with the perception layer realizing the collection of daily-use ceramics production data through sensors,the transmission layer realizing the upload of production data and the issuance of control instructions through gateways,the platform layer using database tools to store and analyze production data,and the application layer realizing various functional modules of the production control system based on the B/S architecture.At the same time,the system implements knowledge graph-based adaptive scheduling technology for ceramic production to reasonably arrange production plans and controls ceramic production processes through adaptive parameter adjustment technology based on fuzzy control.