The development of intelligence and automation in the metal processing industry faces numerous complex and crucial challenges, which cover several important aspects including technology, talent, cost, management, and the market.
Technological Aspect
Difficulties in System Integration: The metal processing process is complex, involving a variety of processes such as turning, milling, welding, and heat treatment, and there are numerous devices in each link. To achieve intelligence and automation, it is necessary to organically integrate various subsystems produced by different manufacturers, such as CNC machining centers, industrial robots, automated material handling devices, and intelligent warehousing systems. However, the interface standards, communication protocols, and data formats of different devices vary widely. For example, some domestic CNC systems use self-defined communication protocols that are not compatible with international general protocols; there are differences in the data interfaces between old devices and new intelligent devices, resulting in 不畅 in data transmission. This often leads to compatibility issues such as inability to communicate between devices and incorrect data interaction during system integration, which greatly affects the overall operation efficiency and may even cause the entire intelligence and automation project to come to a standstill.
Dependence on Foreign Core Technologies: High-precision sensors, high-performance drivers, advanced CNC systems, etc., are the core technologies and key components for the intelligence and automation of metal processing. Domestic enterprises have weak independent research and development capabilities in these aspects and rely heavily on imports. In the high-end CNC system market, for example, foreign brands such as Fanuc and Siemens dominate, and the market share of domestic enterprises' independent products is relatively low. Relying on imports not only makes the procurement cost of equipment extremely high but also restricts enterprises in terms of technology upgrading, equipment maintenance, and fault diagnosis. Once there are changes in the international situation or problems with suppliers, enterprises' production will face serious risks. For instance, if key components cannot be obtained in a timely manner, it will lead to equipment downtime and production interruption.
Cybersecurity Threats: Intelligence and automation have deeply connected metal processing equipment to the network, enabling real-time data interaction and remote monitoring. However, this also brings severe cybersecurity risks. Hackers may intrude through the network and tamper with production instructions, causing processing equipment to operate incorrectly and producing a large number of unqualified products. They may also steal the enterprise's core technical data, such as processing parameters and product design drawings, damaging the enterprise's intellectual property rights. In extreme cases, they may launch malicious attacks, paralyzing the entire production network, resulting in huge economic losses and production accidents. For example, in 2017, a metal processing enterprise was attacked by ransomware, and its production system completely collapsed. The enterprise had to spend a huge amount of money to recover the data and system and suffered heavy losses due to production suspension. In addition, the security vulnerabilities of the equipment itself may also be exploited. For example, some intelligent device operating systems have security risks, and if the patches are not updated in a timely manner, they are likely to become the entry point for hacker attacks.
Talent Aspect
Scarcity of Interdisciplinary Talents: The intelligence and automation of metal processing require professionals who are proficient in both the principles of metal processing technology, familiar with the characteristics of various metal materials and traditional processing techniques, and master intelligent technologies such as automation control, robot programming, artificial intelligence algorithms, and data analysis. However, currently, there is an extreme shortage of such interdisciplinary talents. In the university education system, traditional majors such as mechanical manufacturing are relatively independent from emerging majors such as automation and computer science, and the curriculum lacks cross-integration, resulting in graduates being unable to meet the needs of enterprises. In enterprises, traditional technical personnel lack knowledge of intelligent technologies, while newly recruited professionals in automation and computer science have insufficient understanding of metal processing technology and cannot effectively apply intelligent technologies to actual production, which restricts the advancement of enterprises' intelligence and automation projects.
Lagging Talent Training System: Universities and vocational colleges seriously lag behind the development speed of the industry in terms of relevant major settings and curriculum system updates. On the one hand, there are few courses offered in emerging fields such as intelligent manufacturing and the industrial Internet, and the curriculum content is highly theoretical with a lack of practical links, which is out of touch with the actual production of enterprises. On the other hand, the internal training mechanism of enterprises is not perfect, with insufficient investment in the training of existing employees and a single training method, making it difficult to improve employees' professional skills. For example, some enterprises only conduct training for employees through short-term lectures, which cannot enable employees to deeply master the operation and maintenance skills of intelligent equipment. In addition, the industry is developing rapidly, with new technologies and new equipment emerging continuously, but the talent training system has not been able to keep up in a timely manner, resulting in an increasing gap between talent training and industry needs.
Cost Aspect
Huge Initial Investment Costs: To promote intelligence and automation, metal processing enterprises need to purchase advanced automated equipment, such as high-precision CNC machining centers and industrial robots. These devices are extremely expensive, and the price of a single CNC machining center can reach several million or even tens of millions of yuan. At the same time, it is also necessary to build information infrastructure, such as high-speed networks and data centers, and develop customized software to achieve the interconnection of devices and the monitoring and management of the production process. For small and medium-sized enterprises with weak financial strength, such a huge initial investment is often unaffordable, making them hesitate in the process of intelligence and automation transformation, which severely restricts the overall development of intelligence and automation in the industry.
High Maintenance Costs in the Later Stage: Intelligent and automated equipment has a complex structure and high technical content, and its maintenance requires professional technicians as well as special maintenance tools and spare parts. The salary level of professional maintenance technicians is relatively high, and the diagnosis and repair of equipment faults are difficult and time-consuming. For example, when high-end CNC equipment fails, it may require the technical support of the manufacturer, either remotely or on-site, and the maintenance period may last for several days or even weeks. During this period, the equipment is out of operation, and the enterprise faces the loss of production suspension. In addition, regular maintenance of the equipment and software upgrades also require continuous capital investment. As the service life of the equipment increases, the maintenance cost will continue to rise, imposing a heavy burden on the enterprise.
Management Aspect
Difficulties in Transforming the Production Management Mode: The production management mode of traditional metal processing enterprises focuses on manual experience and extensive management, which is difficult to meet the requirements of intelligent and automated production. Intelligent production requires enterprises to adjust their organizational structure, break down departmental barriers, and establish a cross-departmental collaborative working mechanism; optimize the process flow to achieve seamless connection and efficient operation of production links; adopt a digital production planning and scheduling system to monitor the production progress in real-time and arrange production tasks accurately. However, in the actual transformation process, there are many resistances within the enterprise. On the one hand, employees are not adapted to the new management mode and are worried about job adjustments or increased skill requirements, so they have a resistant attitude. On the other hand, the enterprise management has a deep understanding of the digital management concept and lacks the experience and determination for transformation, resulting in a slow progress in the transformation of the production management mode, and the advantages of intelligence and automation are difficult to be fully realized.
Lack of Data Management and Application Capabilities: In the intelligent production process of metal processing, a large amount of data will be generated in all aspects such as equipment operation, production technology, and product quality. However, most enterprises have insufficient capabilities in data management and application. In terms of data collection, the configuration of equipment sensors is not perfect, and some key data cannot be effectively collected. In terms of data storage, there is a lack of a unified data storage architecture, and data is scattered, making it difficult to integrate and utilize. In the data analysis link, enterprises lack professional data analysis talents and tools and cannot mine valuable information from the vast amount of data, such as optimizing the production process, predicting equipment failures, and improving product quality through data analysis. For example, some enterprises have collected a large amount of equipment operation data, but due to the inability to analyze it, they cannot detect potential fault hazards of the equipment in advance, resulting in sudden equipment failures and affecting production. The lack of data management and application capabilities makes it impossible for enterprises to fully utilize the data advantages brought by intelligence and automation, restricting the intelligent development of enterprises.
Market Aspect
Challenges Posed by Diverse Customer Demands: The market demand for personalized and customized metal processing products is increasing day by day, requiring the intelligent and automated production systems of enterprises to have a high degree of flexibility and adaptability. Customers may put forward unique requirements for product dimensions, shapes, materials, surface treatments, etc., and enterprises need to quickly adjust production processes, equipment parameters, and production flows. For example, in the aerospace field, the requirements for the precision and performance of metal parts are extremely high, and the degree of product customization is high; in the consumer electronics product field, products are updated rapidly, and the requirements for the processing styles and techniques of metal casings are constantly changing. This poses a severe test to the flexible production capacity and rapid response ability of enterprises' intelligent and automated systems. Enterprises need to continuously invest in research and development and technological upgrades to meet the diverse needs of customers, otherwise, they will be at a disadvantage in the market competition.
Increased Pressure from Intensified Market Competition: With the development of the trend of intelligence and automation in the metal processing industry, market competition has become increasingly fierce. Domestic enterprises not only have to face competition from their peers in traditional aspects such as price, quality, and service but also have to deal with the competitive pressure brought by international advanced enterprises with advanced intelligent and automated technologies. International advanced enterprises achieve efficient, high-precision, and low-cost production through intelligent production, and the quality and production efficiency of their products far exceed those of some domestic enterprises. If domestic enterprises cannot accelerate the pace of intelligent and automated transformation, improve production efficiency, product quality, and reduce production costs, it will be difficult for them to gain a foothold in the international and domestic market competition. At the same time, emerging enterprises continuously enter the market with new technologies and new models, which also intensifies the complexity and intensity of market competition.
