In the realm of intelligent CAD technology, conceptual and variant design play crucial roles. However, these concepts face challenges in terms of flexibility, adaptability, and integration with modern design processes. Agricultural machinery, characterized by relatively stable functional modules, low structural complexity, and repetitive design tasks, presents a unique opportunity for applying intelligent CAD solutions. This paper explores how parameter-driven design and assembly modeling can be combined to create an intelligent design system tailored for agricultural equipment, such as seeders. By leveraging case-based reasoning (CBR) and modular design principles, this framework aims to enhance efficiency, reduce redundant work, and improve overall design quality.
As industrial technologies evolve and market competition intensifies, product development cycles are shrinking, and the need for rapid innovation is growing. Traditional CAD systems, which rely heavily on geometric modeling and computational methods, struggle to support symbolic reasoning—essential in areas like conceptual planning, decision-making, and structural design. To address these limitations, intelligent CAD (ICAD) has emerged as a promising solution, integrating artificial intelligence, expert systems, and knowledge-based approaches to enable more creative and adaptive design processes.
ICAD technology focuses on three main areas: knowledge representation, knowledge utilization, and system architecture. Conceptual design and variant design, particularly through assembly modeling, are key components of ICAD. These techniques allow for the creation of flexible, reusable design elements that can be adapted to new requirements without starting from scratch.
The application of ICAD in agricultural machinery design offers significant benefits. It reduces the time and effort required for repeated design tasks, improves the reuse of existing information, and enhances the overall competitiveness of agricultural products. This paper analyzes current CAD theories and practices related to conceptual and variant design, highlighting their strengths and limitations.
One of the key aspects of conceptual design is its focus on functional understanding rather than geometric details. The process involves translating user requirements into functional models, which are then refined through various stages of decomposition, solution generation, and evaluation. In contrast, variant design focuses on modifying existing designs to meet new specifications, often using modular or parameterized approaches.
To support these design processes, advanced modeling techniques are essential. For instance, integrated product information models combine functional, geometric, and intent-based representations to provide a comprehensive view of the design. Assembly modeling, in particular, allows designers to capture not only part geometry but also the relationships between components, making it ideal for variant design.
Modular and feature-based design methods further enhance flexibility. Modular design enables the reuse of standardized components, while feature-based design allows for the customization of shapes and structures based on predefined parameters. Parameter design and variable design techniques offer additional flexibility, enabling automatic updates when design parameters change.
Case-based reasoning (CBR) is another powerful approach in variant design. By reusing past design solutions and adapting them to new scenarios, CBR helps reduce the time and effort needed for new design tasks. However, effective implementation of CBR requires robust data management and a well-structured case base.
In the context of agricultural machinery, where design cycles are long and seasonal constraints are common, the adoption of ICAD can significantly shorten development times and lower costs. An intelligent design framework combining parametric design, assembly modeling, and case-based reasoning provides a practical solution to these challenges.
This paper proposes an intelligent design framework that integrates these technologies, offering a new approach to solving problems in ICAD. The system supports both conceptual and variant design, promotes the reuse of existing resources, and enhances the overall efficiency of the design process.
In conclusion, the integration of intelligent CAD technologies in agricultural machinery design represents a significant step forward. It not only improves design efficiency but also supports sustainable and competitive product development. Future research should focus on enhancing product data management, improving human-computer collaboration, and developing more robust tools for variant design.
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