When AI large models encounter various application scenarios in the label printing industry, how much surprise can they bring?Currently, artificial intelligence (AI) technology has become an important driving force for a new round of technological revolution and industrial transformation globally. Its application scope has gradually expanded from consumer intelligence to the field of enterprise intelligence, driving and creating stronger productivity, and having a profound impact on many industries. In the label printing industry, the future application of AI can help label printing enterprises achieve product innovation, intelligent production, quality control, smart logistics, and more, further improving production efficiency and product quality, while reducing operating costs and resource consumption.
Scene 1: Intelligent Production SchedulingBy optimizing production scheduling through data analysis, the operational efficiency of production lines can be improved. Label printing enterprises often face complex production processes and diverse product demands. By introducing an AI scheduling system, it is possible to monitor the production status in real-time, adjust production plans promptly, thereby reducing downtime and increasing capacity.Scene 2: Equipment Health ManagementBased on real-time monitoring of equipment operation data, using feature analysis and machine learning techniques, on one hand, it is possible to predict equipment failures before incidents occur, reducing unplanned downtime. On the other hand, in the event of sudden equipment failures, it can quickly diagnose faults, locate the causes, and provide relevant solutions.Scene 3: Vision-based Surface Defect DetectionMachine vision-based surface defect detection can quickly identify smaller and more complex defects on labels in a matter of milliseconds, even under frequently changing environmental conditions, and classify them, such as detecting whether the label surface has white spots or contaminants. Currently, there are industrial intelligent companies combining deep learning with 3D microscopes, improving detection accuracy to nanometer levels. For detected defective products, the system can automatically determine repairability, plan repair paths and methods, which are then executed by the equipment to perform the repair actions.Scene 4: Intelligent SortingThe label printing industry has many sorting tasks in inventory management. If manual work is used, it is slow and costly, and it also requires a suitable working temperature environment. If industrial robots are used for intelligent sorting, costs can be significantly reduced, and speed can be increased.
Scene Five: Digital TwinA digital twin is a mirror of an objective entity in a virtual world. The process of creating a digital twin integrates artificial intelligence, machine learning, and sensor data to establish a highly immersive 'real' model that can be updated in real-time to support decision-making activities throughout the lifecycle of physical products. In terms of reduced-order modeling of the digital twin object, complexity and nonlinear models can be placed into neural networks, using deep learning to establish a finite target, and based on this finite target, perform reduced-order modeling.Scene Six: Generative DesignGenerative design is a human-machine interaction and self-innovative process. When engineers are designing labels, they only need to set expected parameters and performance constraints such as materials, colors, shapes, and application scenarios under the system's guidance, combined with artificial intelligence algorithms, which can then automatically generate hundreds or thousands of feasible solutions based on the designer's intent. They can then independently conduct comprehensive comparisons to select the optimal design scheme to present to the designer for final decision-making.Scene Seven: Demand Forecasting and Supply Chain OptimizationBased on artificial intelligence technology, by analyzing market demand and supply conditions, precise demand forecasting models are established to help enterprises better manage inventory and resource allocation, making demand-oriented decisions. In the face of a rapidly changing market environment, the application of AI enables companies to respond more flexibly to market fluctuations, enhancing the overall efficiency of the supply chain.In summary, while AI holds significant application potential in the label printing industry, the vast majority of label printing companies are not well-prepared for AI applications and show a general lack of understanding of AI. Additionally, the data collection, utilization, and development within label printing enterprises still face certain difficulties, and since the company's database is primarily private and data scale is limited, there is a lack of high-quality machine learning samples for AI applications, which to some extent hinders the pace of AI adoption in enterprises.Undoubtedly, in the process of transformation and upgrading in the label printing industry, the application of artificial intelligence will undoubtedly play an important role in promoting this change. Label printing companies should also promptly shift their thinking, actively seek application scenarios for artificial intelligence within their enterprises, and promote the intelligence of internal management.