1. Introduction

The rapid development of Artificial Intelligence (AI) technologies over the past few decades has led to a significant transformation in multiple industries, particularly in electronic engineering. Electronic systems, ranging from industrial machines to consumer devices, are becoming increasingly intelligent and autonomous due to the development of AI. AI is leveraged to automate control systems, improve performance, reduce errors, and optimize operations, fundamentally altering how engineers design, manage, and troubleshoot electronic systems.

Numerous studies have explored AI’s integration into electronic systems. For instance, Shen examined the application of AI in electronic engineering automation control, demonstrating its potential in optimizing system performance and reducing human intervention [1]. Similarly, Zhang investigated the relationship between AI and mechatronic systems, highlighting the key benefits AI brings to automation processes [2]. Jiang et al. emphasized that AI is increasingly applied in power system planning, control, and fault diagnosis due to its ability to analyze large-scale heterogeneous data [3]. Zhang and Xiong further discussed how AI mimics human cognition to enhance automation control, replacing traditional manual labor with intelligent systems [4]. These studies provide a foundational understanding of AI’s role in enhancing electronic engineering practices. This paper builds on these findings by addressing two key questions: first, what are the current areas of application for AI in electronic engineering? Second, what are the future development trends and challenges for AI integration?

This study carries out an extensive literature review of AI applications in electronic engineering, with a focus on automation and control systems. Through analyzing existing research and case studies, this paper aims to present a comprehensive overview of the present and future roles of AI in this field.

2. Current status of AI applications in electronic engineering

AI has become a critical component in the design, manufacturing, and operation of electronic systems. Its application is widespread, ranging from intelligent prediction to precision control, and even autonomous systems such as smart vehicles. The following sections outline some of the key areas in which AI is currently making an impact.

2.1. Intelligent prediction

One of the most significant applications of AI in electronic engineering is its use in intelligent prediction. AI algorithms, particularly Machine Learning (ML) models, can process large volumes of data to predict equipment behavior, identify patterns, and forecast potential failures. By using data-driven models, AI systems can predict the health of mechanical and electronic components, which is crucial for predictive maintenance.

For example, Huang demonstrated how AI could predict the wear and tear of electrical components by analyzing historical data on temperature, load, and usage patterns [5]. Such predictive capabilities enable engineers to schedule maintenance before a failure occurs, minimizing downtime and maximizing operational efficiency. AI-driven predictive maintenance is not limited to machinery; it also applies to power systems, transport infrastructure, and even smart home devices, providing early detection of potential issues and facilitating better resource management.

2.2. Precision control and smart grids in energy management

Precision control refers to the ability to finely tune system parameters to achieve the desired output with minimal errors. In electronic engineering, AI plays a pivotal role in enabling real-time, autonomous adjustments to optimize system performance. Reinforcement Learning (RL), a subset of machine learning, has demonstrated significant potential in this area. RL algorithms allow AI systems to learn optimal control strategies through trial and error. In industrial settings, AI algorithms can adjust parameters such as motor speed, temperature, and pressure in real time to optimize production processes. A study by Pan and Zhang demonstrated that an AI-based RL system could reduce energy consumption and improve product quality by continuously adjusting parameters in a factory’s production line [6].

AI’s role in smart grids and energy management is another area where its impact is significant. AI is increasingly utilized to manage power systems more efficiently, particularly with the growing complexity of distributed renewable energy sources. Jiang et al. highlighted how AI-driven energy management enables real-time load balancing, demand forecasting, and integration of renewable energy, making power grids more resilient [3]. AI-driven algorithms analyze large-scale heterogeneous power data to optimize energy distribution, reducing energy consumption and operational costs. Moreover, AI improves the integration of renewable energy sources like solar and wind power by predicting energy availability and adjusting consumption patterns accordingly.

2.3. Automation in manufacturing and factory systems

In the manufacturing sector, AI has revolutionized factory automation. AI-driven systems are currently capable of performing tasks that were once the exclusive domain of human operators. These systems utilize machine learning and computer vision to monitor production lines, adjust settings in real time, and detect defects during the manufacturing process.

AI is also instrumental in robotics, controlling robotic arms to execute complex tasks such as component assembly, welding, and packaging. As mechatronic systems advance, AI is pivotal in the development of autonomous robots that can collaborate with human workers, thereby optimizing production processes. Zhang and Xiong highlight that AI in automation control not only enhances precision and efficiency but also enables machines to supplant traditional manual labor, thereby boosting productivity [4].

2.4. Autonomous vehicles and smart systems

AI is transforming the transportation sector, particularly autonomous vehicles. Self-driving cars, trucks, and drones rely on AI navigation, decision-making, and system optimization. By processing data from cameras, sensors, and GPS, AI systems can recognize objects, predict traffic patterns, and plan routes autonomously.

AI optimizes power systems in Electric Vehicles (EVs) by balancing energy consumption with driving demands. Zhang’s research demonstrates how AI enhances EV battery usage and extends driving range by predicting optimal charging cycles based on user behavior and traffic conditions [2]. Beyond autonomous driving, AI also refines intelligent traffic management systems, utilizing real-time data to adjust traffic signals, alleviate congestion, and diminish fuel consumption, thereby reducing travel time and lessening the environmental impact of urban transportation networks.

2.5. AI in product design and quality control

In product development, AI is used in Computer-Aided Design (CAD) tools to assist engineers in creating optimized designs. AI can identify potential design flaws, suggest improvements, and even automate some aspects of the design process. This reduces the time required to develop products, resulting in more efficient production cycles.

AI is also integrated into quality control systems, where it helps to detect defects in products and suggest corrective actions. By leveraging computer vision, AI systems can identify visual defects in components or products with higher accuracy than human inspectors, ensuring that only high-quality products reach the market.

3. Future trends and developments

Looking ahead, the integration of AI in electronic engineering is expected to evolve further, enabling new capabilities and opening up exciting possibilities in various fields.

3.1. Advanced manufacturing and smart factories

One of the most promising future trends is the rise of smart factories powered by AI. These factories will leverage AI to enable real-time optimization of production lines, improving flexibility and adaptability. AI will enable these factories to adjust to demand changes, allocate resources and effectively schedule production, drastically improving their efficiency and reducing costs.

AI will also enable collaborative robots (cobots) to work alongside human workers, performing repetitive or dangerous tasks while allowing human operators to focus on more complex activities. This human-robot collaboration is expected to revolutionize industries such as electronics, automotive, and consumer goods manufacturing.

3.2. Quantum electronics

In the coming years, AI will play an important role in the development of quantum electronics. Quantum computing holds the potential to revolutionize fields such as cryptography, optimization, and machine learning. As quantum computers become more advanced, AI algorithms will be used to harness their power and develop more efficient computational models. The combination of AI and quantum technologies could lead to breakthroughs in many fields, including materials science and artificial intelligence itself.

3.3. Cross-domain collaboration

To fully realize the potential of AI in electronic engineering, cross-domain collaboration will be essential. The future of AI in electronic engineering will require collaboration between AI experts, engineers, and industry practitioners. This collaboration will foster innovation, ensuring that AI technologies are seamlessly integrated into existing systems and creating new applications that were previously unimaginable.

For example, the development of smart, flexible production systems that combine AI, robotics, and mechatronics will require collaboration between experts from various fields to create systems that can adapt to rapidly changing production requirements.

3.4. Adaptive technologies in manufacturing

As AI continues to advance, the development of self-adaptive technologies will be a key trend. AI will enable manufacturing systems to autonomously adjust production parameters based on real-timedata, improving both efficiency and quality. These systems will be capable of real-time self-optimization, learning from previous production runs and adjusting parameters such as machine speed, material usage, and energy consumption to reduce waste and improve outcomes.

4. Conclusion

AI has made significant strides in the field of electronic engineering, particularly in automation control. Its applications are transforming the industry, driving improvements in system efficiency, precision, and adaptability. From predictive maintenance to factory automation and autonomous vehicles, AI is reshaping electronic systems in profound ways. As AI algorithms continue to advance and hardware evolves, the future holds even greater promise for the integration of AI in electronic engineering.

The development of smart factories, quantum electronics, and self-adaptive manufacturing technologies will continue to drive innovation. Cross-disciplinary collaboration will be essential to fully harness AI’s potential, enabling the creation of smarter, more efficient, and sustainable engineering practices.