The machinery manufacturing industry is an important industry of the national economy, and it is of great significance to enhance the modernization level of the machinery manufacturing industry for social development. And intelligent technology with mechanical production and processing, and promotes the upgrading of product quality and the transformation of the industry while improving production efficiency.

Intelligent manufacturing technology in machinery manufacturing industry-specific applications

1. Application in design and development

In the machinery manufacturing industry, design research and development is crucial. The traditional design and development process usually relies on the cycle of “design-sampling-modification” until the product design program meets the standard. This process is time-consuming, slows down the production schedule, and may lead to excessive investment and waste of resources. Intelligent manufacturing technology through virtual simulation and 3D modeling, the realization of the digital design process, and virtualized sampling, which helps to quickly find problems in the design scheme, timely modification, and adjustment.

With the help of smart manufacturing technology, designers can directly synchronize modifications on digital drawings, significantly accelerating the design process. At the same time, visualized simulation tests make it easier to show samples and convey design concepts to customers, and if customers have additional needs, they can also make instant adjustments. By fine-tuning the design in the simulation phase, we can effectively avoid parameter errors or non-conformity problems, thus ensuring the feasibility of the design. Using this as the basis for actual prototyping saves resources and greatly improves the efficiency of design and development.

2. Application in production and processing

In the traditional machinery manufacturing industry, production is usually manually operated machinery to complete the parameter settings and processing processes, resulting in low efficiency and inadequate product quality control. Intelligent manufacturing technology combines automation, computer, and artificial intelligence technology, can realize the automation of production and processing, controllable and remote, without the need for real-time participation and control of manpower, greatly shorten the workflow, reduce the burden of manpower, and the same time, make the product quality more controllable. In the increasingly competitive machinery manufacturing industry, the application of intelligent manufacturing technology not only improves the quality and efficiency but also enhances the market competitiveness of enterprises.

Programmable Logic Controller (PLC) is a commonly used technology in intelligent manufacturing, which can realize the automation and fine control of production equipment.PLC controls the equipment through stored instructions to realize automated production. This technology, on the one hand, reduces the dependence on manpower, safeguards personal health and safety in special production environments, and reduces human errors; on the other hand, it supports 24-hour uninterrupted and efficient production, significantly improves production efficiency and resource utilization, and meets the needs of the machinery manufacturing enterprises to reduce costs and increase efficiency. The basic structure of PLC is shown in Fig. 1.

Figure 1 PLC basic structure

In today’s machinery manufacturing industry, the application of automated production lines and robots has become a development trend. Through sensors automation controllers and other equipment, the production line can realize automation and continuity, and support remote control. Robots, on the other hand, take on repetitive or high-risk tasks, significantly improving the safety and reliability of production and processing.

In the production process, product quality control is a top priority. Artificial intelligence technology enables efficient quality management through intelligent inspection and screening. With visual recognition and deep learning, AI systems can recognize product defects and perform quality inspections. For example, the AI model contains a variety of possible defect samples in advance, such as the appearance of scratches, defects, deformation, etc. The automatic inspection system can quickly compare defects and separate unqualified products from the production line to ensure product quality.

At the same time, artificial intelligence with the help of neural networks can continuously learn and improve from the quality inspection data to further optimize the process parameters, thus improving defect detection efficiency and product qualification rate. This process not only improves the detection efficiency but also provides a continuous improvement direction for the production process, which helps to achieve higher quality production standards.

3. Application in troubleshooting

Failure of production equipment can seriously affect production efficiency and enterprise benefits, so intelligent manufacturing technology should not only improve production efficiency but also strengthen the fault control and handling capabilities, to ensure the stable operation of the production line, and reduce production stoppages due to failures. Traditional fault handling is mainly “after the fact”, that is after the fault occurs and then carries out investigation and maintenance, with low efficiency, affecting the production process. Intelligent manufacturing technology can realize real-time monitoring and early warning of equipment to meet the needs of prior prediction and rapid response.

The current intelligent fault-handling system consists of sensors, communications, computers, and automation technology. Sensors can monitor equipment in real-time collect various types of data (such as environmental data and equipment data), and transmit these data to the central system for analysis. After integrating the data, the system analyzes the abnormal data in depth with the help of algorithms, and as soon as an abnormality is detected, it immediately activates the fault detection mechanism to locate the problem and analyze the cause. Subsequently, the system sends the fault information to technicians promptly for rapid troubleshooting and handling.

In terms of daily maintenance, the intelligent troubleshooting system also assesses the operational status of the equipment, automatically sets the maintenance cycle, and reminds the staff to carry out maintenance when the maintenance is due. This not only greatly improves the reliability of the equipment, but also reduces the incidence of accidental failures, ensuring the continuity of production and the service life of the equipment.

4. Application in management

Management is an important part of the machinery manufacturing industry, and the scientific management mode can significantly improve the production and operational efficiency of enterprises and bring higher economic benefits. In management, the Internet of Things and big data technology in intelligent manufacturing technology play an important role. By mining the value of data, big data technology provides managers with decision-making data on production, sales, markets, and customers, helping to formulate more scientific production plans and supply programs, as well as promoting lean management of the supply chain. In addition, in the production process, big data technology can provide in-depth analysis of costs and resource consumption and propose feasible resource optimization solutions, which helps promote the greening and energy saving of machinery manufacturing.

IoT technology is also widely used in warehousing and logistics management. Through sensors and RFID radio frequency identification technology, the Internet of Things can establish identification networks and management mechanisms for management elements such as raw materials, products, and equipment, and construct databases and information ledgers. The process of materials in and out of the warehouse, without manual registration, can be directly through the chip induction to achieve automatic records. When the inventory of raw materials or equipment is insufficient (triggering the preset threshold), the system will automatically remind the purchasing staff to replenish the goods.

In logistics management, real-time management of the transportation process is realized through RFID tags, including location monitoring (GPS technology) and logistics environment monitoring (sensors). Logistics management helps enterprises to prepare to receive materials in advance, shorten the time difference between material warehousing and production application, and thus improve production efficiency. These intelligent management tools enable machinery manufacturing enterprises to operate more flexibly and efficiently, enhancing market competitiveness. 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

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 products are increasing, the product structure is becoming more and more complex, and the production accuracy requirements are increasing. However, intelligent manufacturing technology started late in China, and there is still a certain gap with the developed countries, especially in the application of control strategies and algorithms, there is still a lot of room for development. The optimization of control strategies and algorithms directly determines the level of mechanical manufacturing.

Control strategy is the key to intelligent manufacturing, which involves the fine control of production equipment. The higher the control precision, the higher the stability and fineness of the production process, which in turn improves the quality and production efficiency of the product. In the intelligent manufacturing process, the use of scientific control strategies can effectively reduce the instability caused by errors or equipment fluctuations in the production process, thus ensuring the consistency of product quality.

At the same time, algorithm technology plays a crucial role in the optimization and adjustment of the production process. Excellent algorithms can streamline the production process and reduce redundant operations while maintaining or improving product quality. For example, optimizing the trajectory and flow of production equipment can significantly improve productivity and reduce costs. With the continuous development of intelligent technology, more cutting-edge technologies should be integrated into intelligent manufacturing technology to further optimize control strategies and algorithms.

For example, the introduction of visual recognition technology in the control strategy, production equipment equipped with “eyes”, real-time monitoring and adjustment of unstable factors in the production process, thereby further enhancing the stability and reliability of the production process. In terms of algorithmic technology, the application of genetic algorithm, fuzzy algorithm, and ant colony algorithm can be strengthened, which can be adaptively optimized according to the actual production situation, improving the trajectory accuracy of the production equipment and simplification the process, to improve the production efficiency, and reduce the manual intervention and production errors.

2. Human-machine cooperative optimization

Although intelligent manufacturing technology has greatly optimized the use of human resources, it does not mean that the entire process of mechanical manufacturing can be completely independent of human participation. Human-machine cooperative optimization has become an important direction in the development of intelligent manufacturing technology. Through the cooperation and coordination between machines and equipment and human beings, the overall efficiency of manufacturing can be effectively improved. Therefore, to achieve the optimization of human-machine collaboration, the key lies in the ability of machines to learn and adapt to human operations, forming a more efficient and intelligent collaboration mode.

The core of realizing this goal lies in strengthening data collection and analysis. By collecting and analyzing massive amounts of data, it can provide a solid foundation for the machine’s neural network and deep learning. By learning from human behavior, machines gradually form a more intelligent mode of thinking. With the help of these learnings, machines can improve their perceptual capabilities, perform smarter analyses, and have the ability to make autonomous decisions. For example, machines can make adaptive adjustments according to the production environment or human instructions to optimize the operation process in real-time. This not only improves productivity but also enhances flexibility and safety in the production process.

Human-machine co-optimization ultimately maximizes the potential of smart manufacturing by enabling deep collaboration between equipment and operators. Under this collaborative model, the combination of human creativity and judgment with the efficient execution capabilities of the machine can bring about higher production efficiency, reduce human error, optimize the production process, and make the manufacturing process more precise and efficient.

3. Decentralized multi-power optimization

The progress of intelligent manufacturing technology is closely related to the power source output characteristics of production equipment. With the gradual development of machinery manufacturing in the direction of high efficiency, flexibility, and intelligence, the intelligent manufacturing system needs to be able to meet these complex operational requirements, and accurately monitor and control the movement characteristics within the production equipment. In modern production lines, a large number of equipment is usually included, and the power sources of different equipment are often different, which leads to the diversity of each equipment component in terms of movement and control mode. How to realize the coordinated cooperation and precise control between various equipment has become a challenge for intelligent manufacturing technology.

To solve this problem, decentralized multi-dynamic optimization has emerged. Through decentralized multi-power optimization, fine management and control of different equipment in the production line can be achieved. Specifically, this method involves establishing models for different equipment and making uniform correspondence and matching of information in the database. Through precise data analysis and algorithmic optimization, more efficient and accurate motion control of equipment components can be achieved. For example, in the production process, the optimized multi-dynamic control system can automatically adjust the power output according to the characteristics of different equipment to achieve smoother cooperative work.

The core of decentralized multi-power optimization lies in the advanced control technology and algorithms that enable the power source of each equipment to be optimally matched in different working modes, thus achieving coherence between equipment. This not only improves production efficiency but also enhances the flexibility and adaptability of the entire production line, providing strong support for intelligent manufacturing and further promoting the modernization of the manufacturing industry.

Conclusion

At present, intelligent manufacturing is the key direction of the development of the machinery manufacturing industry, the relevant enterprises and personnel to strengthen the integration of intelligent manufacturing technology in the design and development of production and processing and other areas, to achieve more efficient, intelligent and environmentally friendly production and management, and at the same time, to further accelerate the development of intelligent manufacturing technology and innovation, to promote the industry’s qualitative and efficient and transformational development.