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[Industry News]Intelligent Control Upgrade: Digital Twin and AI Empower Full-Process Lean Production

Release time:

Jan 24,2026

[Industry News]Intelligent Control Upgrade: Digital Twin and AI Empower Full-Process Lean Production
[Industry News]Intelligent Control Upgrade: Digital Twin and AI Empower Full-Process Lean Production

Intelligent Control Upgrade: Digital Twin and AI Empower Full-Process Lean Production

Against the backdrop of the intelligent transformation of the manufacturing industry, the pulp molding sector is reconstructing its competitiveness through technological innovation. The in-depth integration of digital twins and AI has become a core engine for breaking efficiency bottlenecks and achieving lean production. In 2026, with the maturity of supporting technologies such as the Internet of Things (IoT) and edge computing, the penetration rate of intelligent technologies like digital twins and AI visual inspection in pulp molding equipment continues to rise, driving the industry to accelerate iteration towards the "dark factory" model and enter a new stage of full-process intelligent control.

Digital twin technology establishes a closed loop of "physical entity-virtual mapping-data interaction-optimization feedback". By reproducing the full-element scenario of the production line in a virtual space, it realizes production visualization and forward-looking optimization. The key parameters of multiple links in pulp molding production are highly interrelated; the system collects data in milliseconds through IoT sensors deployed at key nodes of production equipment, preprocesses it via edge computing, and transmits it to the virtual twin model for dynamic iterative updates. It can predict potential faults such as bearing wear and pipeline blockage, transforming "post-fault maintenance" into "pre-warning maintenance". Data shows that after application, the Overall Equipment Efficiency (OEE) reaches 89.7%, 32 percentage points higher than that of traditional production lines, the Mean Time Between Failures (MTBF) is extended by over 40%, and maintenance costs are reduced by 28%. Meanwhile, virtual simulation optimizes processes, reducing raw material waste and new product development cycles.

The AI visual inspection system addresses the pain points of traditional manual quality inspection. To meet the stringent quality requirements of pulp molding products, it achieves precise, high-speed and unmanned quality inspection relying on pixel-level recognition and deep learning algorithms. The system collects product images through high-definition cameras, optimizes image quality via preprocessing technologies (such as noise reduction, enhancement, and correction), and then uses well-trained deep learning models for pixel-level analysis. It can quickly identify minor surface defects (e.g., scratches, dents, cracks, material shortage) and dimensional deviations with a detection accuracy of 0.01mm, reducing the error rate from 15% to below 0.3%. More importantly, it is deeply linked with automatic sorting equipment and PLC control systems to complete the fully automated "detection-identification-rejection" process. The quality inspection speed of a single production line is increased by more than 5 times, and the product qualification rate is stably maintained above 99.5%.

Based on the aforementioned technologies, the in-depth integration of PLC intelligent control systems and robot clusters breaks through the breakpoints in the full production process, realizing unmanned operations from raw material warehousing, molding, hot pressing, trimming to finished product palletizing. AGV robots and RFID technology manage raw material warehousing and traceability, the PLC system adjusts equipment operation according to optimized parameters, and robots are responsible for precise trimming and palletizing, significantly reducing labor costs and operational errors.

Case studies of smart factories of leading domestic enterprises have confirmed the value of these technologies. After integrating the full-process intelligent system, their per capita output value reaches 350,000 yuan per year, close to the international advanced level and 120% higher than that of traditional factories. Energy consumption and raw material waste are reduced by 22% and 18% respectively, and the comprehensive cost is lowered by 30%. Looking ahead, with continuous technological iteration, the intelligentization of the industry will evolve towards full supply chain collaboration and autonomous decision-making, becoming a core path for enterprises to achieve high-quality development.

As a core link connecting physical production and virtual control, digital twin technology has completely changed the traditional production management logic of the pulp molding industry. Its core value lies in building a closed-loop system of "physical entity-virtual mapping-data interaction-optimization feedback". By reproducing the full-element scenario of the entire production line in a virtual space, it realizes the visualization, predictability and optimizability of the production process. Specifically, the pulp molding production process involves multiple links such as pulping, molding, hot pressing and cooling. The key parameters of each link (e.g., pulping concentration, slurry pH value, vacuum forming pressure, hot pressing temperature and time, cooling air speed) are interrelated and mutually influential; any parameter deviation may lead to product defects or equipment failures. Based on this, the digital twin system collects multi-dimensional operation data in real time at millisecond intervals through IoT sensors deployed at key nodes of production equipment. It simultaneously preprocesses massive raw data using edge computing technology to filter redundant information and extract core features, which are then transmitted to the virtual twin model for dynamic iterative updates.

Relying on high-precision virtual models, enterprises can achieve refined control and forward-looking optimization of the full production process. In terms of equipment operation and maintenance, the digital twin model can simulate the operation status of equipment under different working conditions. Through big data analysis and algorithm deduction, it can identify potential faults such as bearing wear, seal aging and pipeline blockage in advance, and generate predictive maintenance plans, transforming traditional "post-fault maintenance" into "pre-warning + precise maintenance" and significantly reducing unplanned equipment downtime. Data shows that after the application of digital twin technology, the MTBF of pulp molding equipment is extended by more than 40%, maintenance costs are reduced by 28%, and the OEE can be stably increased to 89.7%, 32 percentage points higher than that of traditional manually controlled production lines, completely breaking the bottleneck of difficult improvement in equipment efficiency in traditional production. In terms of process optimization, multi-scenario process parameter simulation tests through virtual models can quickly find the optimal parameter combination for different product specifications, eliminating the need for repeated trial production on physical production lines, which not only reduces raw material waste but also shortens the new product development cycle, helping enterprises quickly respond to changes in market demand.

The application of AI visual inspection systems has brought revolutionary changes to the quality inspection link of the pulp molding industry, completely solving the pain points of low efficiency, high error rate and poor stability of traditional manual quality inspection. Pulp molding products are mostly used in food packaging, buffer protection and other fields, which have high requirements for surface flatness, edge integrity, dimensional accuracy and other indicators. However, traditional manual quality inspection relies on naked eye judgment, which is affected by factors such as fatigue and subjective experience, resulting in an error rate of up to 15%. It is also difficult to detect minor defects, becoming a key shortcoming restricting the improvement of product qualification rate.

The new generation of AI visual inspection systems realizes precise, high-speed and unmanned quality inspection through pixel-level recognition capabilities and deep learning algorithms. The system collects product images from multiple angles through high-definition industrial cameras, optimizes image quality with image preprocessing technologies (such as noise reduction, enhancement and correction), and then conducts pixel-level analysis on the images using well-trained deep learning models. It can quickly identify minor surface defects such as scratches, dents, cracks and material shortage, as well as specification issues such as dimensional deviations and uneven wall thickness, with a detection accuracy of 0.01mm, controlling the quality inspection error rate below 0.3%. More importantly, the system can be deeply linked with automatic sorting equipment and PLC control systems. After detecting unqualified products, it immediately triggers sorting instructions to realize the fully automated "detection-identification-rejection" process without manual intervention. This not only improves quality inspection efficiency (the quality inspection speed of a single production line is increased by more than 5 times) but also ensures the consistency of quality inspection standards, promoting the product qualification rate to be stably above 99.5%.

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