# Implementing CAD API Automated Processes in Engineering Design: A Case Study Approach
## 2 Related Work and Context
### Automation Systems and CAD API Integration  
Automating a task or system, as shown in Figure 1, involves the depicted steps. By following these steps, a structured approach to integrating automation into engineering processes becomes possible, ensuring both an effective and efficient transition from manual to automated workflows. Knowledge-based engineering (KBE) refers to the integration of knowledge management into the design and manufacturing process. This enables engineering expertise and data to be reused to automate more complex tasks. Thus, KBE represents a powerful design approach that uses explicitly represented knowledge to automatically generate product variants [23; 24]. In [25], the authors consider that KBE is capable of drastically decreasing the time devoted to repetitive, labor-intensive tasks by automating the processes that traditionally involve manual intervention. According to [10], KBE not only automates recurring tasks but also improves decision-making by drawing on past experiences and design rules. This helps to reduce iteration cycles and accelerate adaptation to new requirements. The authors of [9] support this view by stating that shortening is of paramount importance in modern engineering, as it is imperative to be able to respond quickly to dynamic market demands. Moreover, as the authors of [10] point out, the automated retrieval and application of previous design data enables KBE to minimize redundant tasks in alignment with [PERSON]'s assertion, as published in [25]. The overall result is a leaner, more efficient workflow, where time and resources are optimized, thus meeting strict project deadlines  
Figure 1: Stages of the automation process. and improving the overall quality of final products, allowing engineers to become more creative and innovative [26]. Product design and engineering design are closely linked disciplines [27], where the former defines user needs and vision, while the latter connects creativity with technical execution [28]. Advances in manufacturing increasingly rely on the effective reuse of engineering knowledge [29], data science integration, and flexible, modular automation systems--such as microservice-based industrial control architectures--to reduce information loss and enhance design efficiency [30]. In the current study, an API (Application Programming Interface), which is the basis of a modern CAD system, will be utilized [31]. Application Programming Interfaces (APIs) serve as intermediaries between software systems, facilitating seamless communication and interoperability among them [32], automating repetitive tasks in CAD systems, and reducing the need for manual intervention [33]. This makes APIs an essential tool for automating a wide range of engineering tasks, from routine operations to more complex processes such as parametric modeling and design validation. In [34], the authors point out that these platforms guarantee greater interoperability. APIs can thus be exploited to extend the capabilities of CAD systems for better integration with other tools and data sources. The transition to cloud-based solutions facilitates enhanced computational performance, improved collaborative capabilities, and increased flexibility in design environments, thereby better addressing the evolving demands of diverse industrial sectors [35]. Despite the considerable automation potential offered by Application Programming Interfaces (APIs) in CAD systems, their effective implementation often entails certain challenges, such as the requirement for programming proficiency and ongoing system maintenance. Nevertheless, those limitations are outweighed by the substantial advantages of API-driven automation, particularly in reducing repetitive manual input and enabling engineers to focus on higher-level design tasks. Various commercial CAD platforms provide API support to facilitate such automation.