Embracing Emerging Technologies

Existing AAC production lines have not fundamentally solved the issue of data interaction between equipment or achieved deep integration across various aspects such as production, warehousing, and sales. Currently, most Chinese AAC factories have adopted PLC distributed control, with a few achieving preliminary automation of production lines. However, even the most automated AAC production lines operate with relatively independent equipment segments, with minimal data exchange and safety decision-making interaction between equipment. Compared to traditional manual control, the current control methods merely utilize simple sensors to connect various judgment points and achieve standalone or online automatic operation of equipment, with few comprehensive safety solutions. Therefore, even at the highest level of automation, manual supervision and intervention are still required to prevent production line downtime or unforeseen failures or accidents due to equipment failures, material shortages, sensor failures, etc.

For AAC factories, addressing the construction of smart ecosystem requires the application of emerging technologies and tailor-made solutions to build reliable smart production lines to achieve comprehensive connection of production, warehousing, and sales data.

Solutions for Building Smart AAC Factory

Achieving intelligent Human-Machine interaction

AAC factories need an intelligent human-machine interaction system to real-time access production data of various equipment on the production line and synchronize the operating status of relevant personnel on the production line. This system should be equipped with relevant detection devices to proactively prompt various equipment needs and monitor the health status of important equipment.

Dosmart Tech Co., Ltd (Dosmart) adopts front-end development technology to create web pages and apps and other front-end interfaces to help users achieve production line visualization. Through mainstream computer programming languages and derived information technologies, frameworks, and solutions, it builds a human-machine interaction system for AAC factories.

Its main functions include:

1.     Install comprehensive data visualization screens on the production line, which can provide real-time feedback on production information, including raw material usage, raw material inventory, current production data, equipment health, energy consumption, carbon emissions, safety monitoring, finished product inventory, logistics, and sales, allowing key information to transition from passive manual retrieval to proactive system communication.

2.     Enterprise operation managers and other departments can achieve collaborative office work through web platforms or app terminals to Classify and visualize data such as production, sales, and inventory; Preview or download various reports online; Prompt equipment health status and maintenance information; Spare parts management; Integration of sales data and classification of customer resources, etc. Users can grasp the operational status of the enterprise at any time without geographical or time constraints.

Using Industrial Internet technology and Edge Computing to online monitor production line

The current control method of AAC production lines in China has evolved from traditional relay control devices to PLC control methods. It employs point-to-point detection using switch-type sensors, and the signals are transmitted to PLC controllers for control via a bus or other means. Sensor applications are limited to determining equipment position status, without progressing to the use of intelligent technologies with self-calibration, self-diagnosis, self-learning, self-decision-making, self-adaptation, and self-organizing capabilities.

Dosmart independently developed embedded analysis modules (referred to as "analysis modules"). These modules collect data through different sensors (such as vibration, torque, temperature, pressure, etc.), transmit the collected data to the analysis module through protocols, calibrate and learn the sensor data according to the characteristics of different equipment and components, and establish a dedicated database, thereby achieving equipment self-diagnosis, self-decision-making, and realizing equipment early warning and dynamic health monitoring.

((Fig. 2)) Dosmart MES-IoT dominates site’s production.

Application of Big Data and Artificial Intelligence

In the production process of AAC, each section and equipment generate a large amount of process data and result data, but almost no enterprise at the current stage can reasonably collect, classify, analyze, and utilize these data. The process formula of raw materials is the core element of the entire production process, but there is a lack of unified theoretical standards because the design experience of these formulas often comes from the long-term practice exploration of practitioners, and each person's ability and experience vary, leading to uneven product quality.

The smart factory system developed by Dosmart collects and classifies process data through establishing a source database, combines theoretical algorithms and related result data to create AI analysis models, and obtains more scientific process formulas. For example, through the built-in algorithm of the AI analysis model, relying on different combinations of various data to continuously provide feedback and correct the formula, helping enterprises obtain the most economical and reliable formula under the premise of determining the functional properties of raw materials and meeting product quality standards.

The Industrial Internet platform constructs big data analysis capabilities, accumulates data, and builds models for various aspects of enterprise production and operation, including: optimization models for single equipment operation, collaboration models for production lines, optimal models for cost elements, etc.

Conclusion

Chinese AAC factories are at a new stage of transitioning from automated factory to smart factory. However, achieving this goal still faces many challenges. Automation does not equate to smart, and there is a lack of effective data interaction and connection between the production and management levels of enterprises, resulting in increasingly apparent shortcomings in enterprise operations, severely affecting the achievement of enterprise goals. It is necessary to recognize that emerging technologies such as the thriving Industrial Internet, artificial intelligence, and edge computing can be applied to the AAC industry. The application of these emerging technologies will drive changes in the mindset and behavior of the AAC industry. Only by empowering AAC factories with new technologies can the industry transition from automation to intelligence.