The machinery manufacturing industry is crucial to China’s economy and social development. In Industry 4.0, intelligent manufacturing integrates automation, information, and intelligence to boost quality, transformation, and efficiency.
The application of intelligent manufacturing in machinery manufacturing
1 . Application in design and development
In the machinery manufacturing industry, design research and development are crucial. The traditional design process follows a “design-sampling-modification” cycle until standards are met. This process is time-consuming, often delays the production schedule, and leads to resource waste and overinvestment.
Intelligent manufacturing technology can realize digital design and virtual testing through virtual simulation and 3D modeling technology. This process can efficiently identify problems and contradictions in the design, and make timely modifications and adjustments. Designers can make changes directly on the digital drawings and update the design simultaneously, which greatly accelerates the design progress. At the same time, with the help of visual simulation, designers can show samples and explain design concepts to customers. Designers can promptly adjust to meet additional customer requirements.
Optimizing the simulation phase helps avoid parameter errors and design non-conformities, ensuring solution feasibility. The actual sampling test after this stage can minimize unnecessary waste of resources, improve R&D efficiency, and shorten time-to-market. This innovative process enhances design quality while reducing production costs, crucial for the manufacturing industry’s rapid growth.
2. Application in production and processing
Traditional manufacturing relies on manual parameter settings, leading to inefficiency and unstable product quality. Intelligent manufacturing, integrating automation, computer, and AI technologies, automates production, enables remote control, improves efficiency, and ensures consistent product quality.
Intelligent manufacturing supports the production process by eliminating the need for real-time human involvement. It compresses the workflow significantly, reduces the burden on manpower, and improves the control over the product’s quality standards. This improves quality and enhances market competitiveness for companies that stand out in the increasingly competitive machinery manufacturing industry.
Programmable Logic Controllers (PLC) are commonly used technologies in intelligent manufacturing. PLCs automate production by controlling equipment movements and input/output operations, reducing labor reliance, ensuring safety, and minimizing errors. Additionally, PLCs allow equipment to operate efficiently and continuously, 24 hours a day, significantly boosting production efficiency and resource utilization. This helps machinery manufacturing enterprises reduce costs and improve efficiency. The basic structure of a PLC is shown in Figure 1.
Intelligent manufacturing enhances efficiency, optimizes resources, and boosts opportunities in machinery manufacturing.
Figure 1 PLC basic structure
Automated production lines and robots in the production
Automated production lines and robots have become an important trend in the development of the current machinery manufacturing industry. By integrating sensors, automation controllers, and robots, production lines achieve automated, continuous, and remote-controlled operations, boosting efficiency, safety, and reliability.
Product quality control is a crucial aspect of machinery manufacturing. Artificial intelligence technology effectively ensures product quality through intelligent quality testing and screening. AI leverages visual recognition and deep learning to detect product defects and ensure quality. For example, the AI model contains various types of product defects, such as appearance scratches, defects, deformation, etc. AI can automatically detect defects, compare them, and separate unqualified products for efficient quality control.
AI can use neural networks to learn from past inspections, improve defect detection, optimize production, and increase product quality. This intelligent quality control reduces human error, ensures consistency, and boosts competitiveness.
3. Application in troubleshooting
Production equipment failure significantly impacts production efficiency and enterprise economic benefits. Intelligent manufacturing technology must improve efficiency and enhance equipment failure control to ensure production line stability and reduce interruptions.
The traditional fault-handling method focuses on investigating and repairing faults after they occur. This approach is less efficient and has a greater impact on the production process. Intelligent manufacturing technology enables real-time monitoring and early warning of equipment, improving fault-handling efficiency through prediction and rapid response.
The current intelligent fault handling system usually includes sensors, communication technology, computer technology,gy, and automation technology. Sensors can monitor the operation of equipment in real-time, collect environmental data, equipment data, and other key parameters, and transmit these data to the computer system for analysis. By integrating and analyzing data in-depth, the system uses algorithms to identify abnormal data. As soon as it detects signs of potential faults, it immediately activates the fault detection mechanism to locate the source of the fault and analyze its cause.
The system will send fault information to technicians for troubleshooting and processing the issue. It can also assess equipment status, predict maintenance needs, determine maintenance cycles, and remind staff when maintenance is due to maximize reliability and reduce malfunctions.
Intelligent manufacturing technology improves troubleshooting speed and accuracy, reduces production line downtime through predictive maintenance, ensures equipment efficiency, and saves costs and resources.
4. Application in management
Management work in the machinery manufacturing industry occupies a crucial position. The scientific management mode can not only enhance the production efficiency of enterprises but also significantly improve operational efficiency. In this process, the Internet of Things (IoT) and big data technology in intelligent manufacturing technology play a key role.
Big Data Technology in Management
Big data technology provides business managers with the information they need to make decisions by deeply mining and analyzing production data, sales data, market data, and customer data. These data help managers develop more scientific production plans and supply chain programs, thus realizing lean supply chain management. Big data technology can also provide in-depth analysis of cost inputs and resource consumption, and analyze and propose optimization solutions to enhance the greening and energy-saving level of machinery manufacturing.
For example, through data analysis, enterprises can optimize production processes, reduce raw material waste, improve energy efficiency, and contribute to sustainable development.
IoT in Warehousing and Logistics Management
IoT technology plays an important role in warehousing management. With the help of RFID radio frequency identification technology and sensors, IoT can monitor the status of raw materials, products, equipment, and other management elements in real-time. Through the construction of identification networks and information databases, IoT enables automatic recording and management of inventory.
The system automatically senses chip changes and updates inventory, notifying purchasing staff to replenish when the stock reaches the threshold, preventing shortages in production.
In logistics management, IoT uses RFID tags, GPS, and environmental sensors for real-time tracking of materials, helping businesses prepare for material reception, reduce delays, improve production efficiency, and ensure smooth operations. The application path of IoT in machinery manufacturing enterprises is shown in Figure 2.
Figure 2 The application path of the Internet of Things in machinery manufacturing enterprises
The optimization direction of intelligent manufacturing technology in the machinery manufacturing industry
1. Control strategy and algorithm optimization
With the continuous development of the machinery manufacturing industry, the customer’s requirements for product quality are increasing, the product structure is more and more complex, and the requirements for production accuracy are also rising. Intelligent manufacturing technology started late in China, and developed countries have a clear advantage. There is still significant room for development, especially in optimizing control strategies and algorithms.
For intelligent manufacturing technology, the scientific nature of the control strategy and the intelligence of the algorithm technology directly determine the level of mechanical manufacturing. The control strategy mainly focuses on finely controlling production equipment. The higher the control precision, the better the stability and fineness of the production process. Precise control strategy can effectively improve the consistency and quality of products.
Algorithmic technology is embodied in the optimization and adjustment of the production process, which simplifies and optimizes it while ensuring good product quality. Through reasonable algorithms, the production process can realize self-optimization, reduce energy consumption and resource waste, and improve overall production efficiency.
As intelligent manufacturing technology develops, integrating more cutting-edge technologies can further optimize control strategies and algorithms. For example, introducing visual recognition technology into the control strategy can equip production equipment with “eyes,” enhancing the stability and reliability of the production process. Through real-time monitoring and feedback, visual recognition technology can identify abnormalities in the production process, and adjust production parameters promptly to ensure product quality.
Advanced technologies such as genetic algorithms, fuzzy algorithms, and ant colony algorithms further strengthen their application to optimize the trajectory of production equipment and simplify the production process. By simulating the optimization process in nature, these algorithms can help the production equipment find the optimal solution in complex production tasks, improve production efficiency, reduce production costs, and ensure the stability of product quality.
2 . Human-machine cooperative optimization
Intelligent manufacturing technology has optimized human resource use, but it doesn’t eliminate the need for human involvement. Human-machine cooperation is key to improving production efficiency and effectiveness.
To optimize human-machine collaboration, machines must learn from people and adapt for better cooperation. This requires intelligent manufacturing systems to use data for training neural networks and deep learning models, enabling machines to understand human habits and behavior.
By learning from human behavior, machines can improve their perception, analysis, and decision-making, enhancing collaboration with operators. The machine can adjust its operation in real-time based on feedback, optimizing workflow, reducing errors, and improving safety and efficiency.
Through this collaborative optimization of humans and machines, production processes can become more flexible and efficient. The machine is not only a tool for executing tasks, but also an intelligent partner for collaborating with human beings, capable of self-adjustment and optimization according to different production demands, which significantly improves the intelligence level and production efficiency of the production line.
3. Decentralized multi-dynamic optimization
The development of intelligent manufacturing technology is closely dependent on the power source output characteristics of production equipment. With the evolution of machinery manufacturing in the direction of high efficiency, flexibility, and intelligence, the complexity and diversity of production equipment are increasing. Therefore, intelligent manufacturing technology must be able to accurately monitor and control the motion characteristics of the equipment to ensure the efficient and stable operation of the equipment.
In the production line of intelligent manufacturing, a large number of production equipment often participate. The power sources of these equipment have their characteristics, and there are differences in power output, control mode, and motion. Realizing cooperative work and precise control of the equipment has become a key issue that intelligent manufacturing technology must solve.
To this challenge, it is crucial to adopt a decentralized multi-power optimization strategy. This strategy realizes accurate matching and cooperative control of different power sources by constructing control models and databases for different equipment characteristics. By dynamically monitoring the power sources, control modes, and motion characteristics of the equipment, we can optimize the coordination between the equipment and improve the efficiency of the entire production line.
Decentralized multi-power optimization mainly includes the following aspects:
Power source matching and coordination:
Real-time monitoring of equipment operation status and power demand, dynamically adjusting the power output of each device, avoiding power waste or mismatch, and improving energy utilization efficiency.
Decentralized control system:
Optimize the feedback mechanism between each equipment’s control unit and its power source to adjust according to the working state and environmental conditions, reducing manual intervention and improving precision and response speed.
Collaborative Optimization:
Using big data analysis and artificial intelligence algorithms, it realizes collaborative work between equipment, optimizes production, and improves resource utilization and efficiency.
Decentralized multi-dynamic optimization enables smarter manufacturing by improving equipment control, coordination, and efficiency, and reducing failures and energy consumption.
Conclusion
At present, intelligent manufacturing is the key direction for the development of China’s machinery manufacturing industry. Relevant enterprises and personnel should enhance the integration of intelligent manufacturing in design, production, and management to improve efficiency, sustainability, and innovation. Accelerating the independent development of smart manufacturing technology is key to transforming the industry and promoting sustainable development.
