1. Introduction

Computer-aided design (CAD) systems refer to tools and platforms that use computer technology to help designers with their design work. Since their first appearance in the 1960s, CAD systems have undergone tremendous evolution, from the initial two-dimensional drawing tools to today's complex three-dimensional modeling, simulation and optimization systems. CAD technology has become essential across various fields, including architecture, engineering, mechanical design, automotive design, and industrial design, revolutionizing traditional design methods [1]. As technology advances, CAD systems have evolved significantly, offering more sophisticated tools for modeling, simulation, and analysis. However, many design tasks, such as optimizing structures or creating innovative forms, still rely heavily on the designer's experience, intuition, and manual effort. For example, fine-tuning parameters or predicting design performance often requires expert knowledge and trial-and-error processes.

This dependency on human intervention can lead to a time-consuming design process and an increased likelihood of errors. To address these challenges, artificial intelligence (AI) has begun to transform CAD systems by introducing automation, decision-making support, and advanced analytical capabilities. AI technologies, such as machine learning (ML), neural networks, and genetic algorithms, are now integrated into CAD software, allowing it to handle more complex and data-driven tasks [2]. This article will provide a comprehensive analysis of the development and application of AI technology within CAD systems, examining both the current state of integration and its practical use across various industries. The analysis will delve into how AI technologies, such as machine learning, neural networks, and genetic algorithms, are being applied to enhance CAD systems, enabling automation, predictive modeling, and generative design. Additionally, the article will address the challenges and limitations that remain.

2. Overview of the development of Artificial Intelligence in CAD systems

2.1. Machine learning

Machine learning (ML) is a widely used AI technology in CAD systems, helping optimize designs by analyzing large datasets and identifying patterns. ML algorithms evaluate historical data and regulatory requirements to recommend building layouts that enhance functionality, energy efficiency, and aesthetics. They assess material properties like strength, durability, and cost, aiding in selecting materials that balance performance and sustainability. ML also improves cost estimation by predicting expenses based on material prices and labor, leading to more accurate budgeting. By simulating energy consumption impacts, ML supports sustainable building designs. Generative design powered by ML allows architects to quickly explore numerous design options, boosting efficiency and precision [1]. In mechanical design, ML analyzes thousands of models to identify optimal solutions under different conditions. These algorithms assess material strength, durability, cost efficiency, and performance, providing designers with valuable insights early in the design phase for more informed decision-making.

2.2. Neural networks

Neural networks, another key AI technology in CAD systems, mimic the functioning of the human brain and enhance their ability to handle complex design tasks through extensive data training. Neural networks are particularly good at pattern recognition and nonlinear problem solving, so they have significant advantages in complex design tasks. In automobile design, neural networks are increasingly being utilized to optimize body structure for safety and efficiency. By analyzing vehicle body performance under various collision scenarios, neural networks can predict which designs offer the best protection while maintaining fuel efficiency [2]. This method allows for the rapid evaluation of multiple design configurations, providing insights that help engineers create safer, lighter, and more fuel-efficient vehicles. For instance, neural networks can assess how different materials or structural reinforcements behave during impact, guiding designers to select the most effective design without the need for extensive physical crash testing. This data-driven approach improves safety, reduces development costs, and aids in meeting market-specific regulations for different regions.

2.3. Genetic algorithm

A genetic algorithm (GA) is an optimization algorithm based on biological evolution theory. It simulates the process of natural selection and gene mutation to find the optimal solution in the design space. Genetic algorithms (GAs) are widely used in CAD systems, especially for multi-objective optimization problems. In the field of product design, GAs serve as a powerful tool for optimizing various aspects such as shape, structure, and material selection. These algorithms mimic the evolutionary process by generating a population of design candidates and applying selection, crossover, and mutation operations to evolve improved designs over successive generations. GAs enable designers to explore a broad solution space by generating multiple design candidates that meet predefined objectives, such as minimizing weight, maximizing strength, or enhancing efficiency. In the aerospace industry, for example, GAs are used to optimize aircraft wing designs by refining parameters like wing shape and structure to improve aerodynamic performance and reduce fuel consumption. The algorithm evaluates various configurations and selects the best-performing designs based on criteria like lift-to-drag ratio or structural integrity [3]. This method accelerates the design process by automating the exploration of numerous possibilities and fosters diversification and innovation, allowing designers to discover unconventional solutions that might not emerge through traditional design approaches. By continuously refining and optimizing designs, GAs can significantly enhance both efficiency and creativity in product development.

2.4. The rise of big data and advancements in computing power

The popularity of big data and the improvement of computing power are important factors driving the application of AI in CAD systems. As design tasks grow more complex, the volume of data that CAD systems must process has also surged. Traditional manual processing methods can no longer meet the needs of modern design, and AI technology can effectively meet these challenges. Modern CAD systems can process massive amounts of design data and perform real-time analysis and optimization through cloud computing platforms [4]. This approach enables designers to collaborate globally and access and process design data in real-time. The introduction of big data analysis technology allows designers to extract valuable information from massive amounts of data, enabling them to make more informed design decisions. The improvement of computing power not only increases the processing speed of CAD systems but also enables the application of more complex AI algorithms. Deep learning algorithms, which require substantial computational resources for training and prediction, now benefit from modern high-performance computers and cloud computing platforms. This combination has enabled the application scope of AI in CAD systems to continue to expand, bringing unprecedented changes to the design industry.

3. Analysis of the application of Artificial Intelligence in CAD

3.1. Design automation and optimization

The integration of AI into CAD systems, driven by the explosion of big data and advancements in computing power, has revolutionized the design industry. As design tasks become increasingly intricate, the data generated—from performance metrics to material properties—has grown exponentially. Traditional manual methods are no longer feasible for managing this complexity. AI technologies and big data analytics enable modern CAD systems to handle these large datasets and extract meaningful insights that inform better design decisions. Cloud computing platforms enhance this capability by providing real-time processing and storage, allowing designers to collaborate across different locations and time zones, instantly accessing the same updated data. This global collaboration is essential for large-scale projects, where teams must work simultaneously on different aspects of a design [5]. Moreover, big data analytics in CAD helps identify patterns and trends from historical designs, customer preferences, and market behavior, allowing designers to create more user-centered and optimized products. For instance, predictive modeling can forecast how a particular design might perform in various scenarios, reducing the need for physical prototypes and speeding up the product development cycle. Designers can leverage these optimization suggestions to rapidly iterate on design schemes, improving product performance and quality.

3.2. Predictive design and simulation

Predictive design and simulation are other important AI applications in CAD systems. Through AI technology, CAD systems can predict possible problems during the design stage and provide corresponding solutions. This predictive capability significantly reduces uncertainty in the design process, enhancing both reliability and accuracy. In architectural design, AI can predict how buildings will perform under different environmental conditions, such as wind loads and earthquakes. By simulating different environmental conditions, AI can help designers optimize structural designs and reduce potential safety hazards. In automotive design, AI can be used for collision simulation [6]. AI significantly enhances vehicle safety design by analyzing performance data from various collision scenarios. By simulating different collision conditions, AI can predict which vehicle designs offer the best safety improvements. This predictive capability allows designers to optimize the vehicle's body structure, ensuring better protection for occupants and enhancing overall safety performance. AI’s role extends beyond design optimization to include design verification and error detection. By comparing new designs with historical data, AI can identify discrepancies and potential errors early in the design process [7]. This automated verification increases design accuracy and reduces the costs associated with rework and modifications. For example, AI can detect issues such as structural weaknesses or safety compliance deviations that might otherwise be missed, offering actionable suggestions for corrections. This proactive approach to design validation helps streamline the development process, ensuring higher quality and safer vehicle designs while minimizing the need for costly last-minute changes.

3.3. Generative design and personalization

Generative design is a revolutionary technology of AI in CAD systems. Through AI algorithms, CAD systems can generate multiple design solutions that meet the requirements under given design parameters. This generative design method has been widely used in architecture, machinery, and product design. In architectural design, generative design empowers architects to explore a wide range of architectural forms that balance functionality and aesthetics. By inputting fundamental parameters—such as building area, floor height, and the number of windows—into a generative design system, designers can leverage AI to generate numerous innovative design solutions. This method enables rapid exploration of various design possibilities, optimizing both space functionality and the visual appeal of structures. The AI system uses algorithms to evaluate how different configurations meet the specified criteria, such as spatial efficiency, natural light distribution, and structural integrity. This process not only accelerates the design phase but also provides architects with diverse options to choose from, enhancing creativity and ensuring that the final design meets both practical and aesthetic requirements [8]. Designers have the option to select the optimal solution or make further adjustments to the parameters in order to achieve a design that more effectively meets their requirements. AI in CAD systems plays a crucial role in personalization. By analyzing user preferences and project needs, AI can generate tailored design recommendations that reflect individual tastes and functional demands.

4. Challenges and limitations

4.1. Technical challenges

AI in CAD systems faces technical challenges such as data quality, computing power, and algorithm complexity. The effectiveness of AI depends on high-quality data input, but design data is often incomplete or inaccurate, negatively impacting AI performance. For instance, inaccurate input in architectural design can lead to flawed solutions that may pose safety risks. Therefore, data cleaning and preprocessing are crucial before AI application. Additionally, computing power remains a bottleneck, as AI tasks like deep learning require the processing of large datasets, which demands significant computational resources. Insufficient computing power can lead to inefficiencies or task failures, necessitating advances like quantum or distributed computing. Algorithm complexity is another challenge, with current AI algorithms struggling to handle diverse design scenarios and balance multi-objective optimization problems [9]. More research in algorithm design is required to enhance AI's adaptability and robustness in CAD systems.

4.2. User adoption and skill gap

The promotion of AI technology also faces the challenges of user adoption and skill gaps. For many traditional designers, AI remains a relatively unfamiliar field, and they may lack the experience and skills needed to effectively use AI tools. The skill gap of designers is an important obstacle in the promotion of AI technology. Designers who are accustomed to traditional tools and methods may feel discomfort or resistance when confronted with AI-driven design processes. Therefore, in order to effectively promote the use of AI technology, it is essential for designers to undergo systematic training to enhance their understanding and proficiency in using AI technology. User acceptance of AI technology is also an issue that needs attention. Although AI performs well in improving design efficiency and optimizing design, some users may be skeptical about the reliability of its results. They may have concerns that the design solutions generated by AI lack humanity and may not effectively address real-world needs. When promoting AI technology, it is necessary to prove the value of AI through more application examples and success cases, and gradually change users' perceptions. Education and training are key to bridging the gap [10]. Designers must acquire the knowledge of utilizing AI tools and comprehend their operational mechanisms in order to fully harness their potential. At the same time, design education also needs to keep up with the times, include more AI-related courses, and cultivate a new generation of designers with AI skills.

4.3. Ethical and legal considerations

The use of AI in design brings forth several ethical and legal concerns. One major ethical issue is AI bias and algorithm transparency. AI algorithms often operate as a "black box," making it challenging for users to understand the rationale behind their decisions. This lack of transparency can introduce biases into the design process, especially if the training data itself is biased, potentially compromising fairness in the final designs. Another significant concern is intellectual property rights. The question of whether AI-generated designs can be copyrighted and who owns these rights remains unresolved. As AI technology advances, this issue could become increasingly complex, particularly when AI-generated designs are used commercially. Disputes may arise over intellectual property ownership between designers and companies, highlighting the need for updated legal frameworks to protect designers' rights.

5. Conclusion

In summary, the integration of AI with CAD systems represents a transformative advancement for the design industry. This integration provides sophisticated tools and capabilities that significantly enhance the design process. The shift from basic 2D drafting tools to advanced 3D modeling and simulation systems has revolutionized the creation, optimization, and validation of designs. Machine learning facilitates the analysis of large data sets to refine design patterns, while neural networks offer robust tools for pattern recognition and complex problem-solving. Genetic algorithms introduce innovative solutions to multi-objective design challenges by mimicking evolutionary processes. The advent of big data and increased computing power enables real-time analysis and global collaboration, altering how design data is processed and leveraged. However, there are still several limitations and challenges that persist. Technical issues such as data quality, computing power constraints, and the complexity of algorithms continue to present obstacles. User adoption and skill gaps pose significant barriers, as traditional designers may struggle to adapt to AI-driven tools, necessitating comprehensive training and education programs to bridge this divide. Additionally, ethical and legal concerns, including AI bias, algorithm transparency, and intellectual property rights, require careful consideration to ensure fairness and protect designers' rights. AI’s potential impact on employment in the design industry also calls for proactive measures to secure jobs and offer retraining opportunities.

Besides, the rapid pace of technological advancement in AI and CAD systems may render current findings outdated as new developments emerge. Future research should address this limitation by incorporating longitudinal studies to track technological evolution and its effects over time.