A leading global automotive manufacturer, specializing in tractors, sought to streamline and optimize the 3D pipe system design used in vehicle chassis. Their traditional workflow was:

• Labor-intensive: Heavily reliant on the manual efforts of CAD engineers.
• Error-prone: Required iterative manual validation by domain experts, leading to rework.
• Time-consuming: Each lengthy design cycle delayed overall product timelines.

This created significant bottlenecks in scalability, accuracy, and cost-efficiency for their engineering process.

We developed a multi-stage, AI-driven solution to automate the entire design pipeline:

• Rule-Based Design Engine: We first encoded the manufacturer’s complex engineering constraints (such as bend radii, clearances, and connection types) into a machine-readable format for automated validation.
• Generative Model Deployment: AI models, including Variational Autoencoders (VAE) and Diffusion-based models, were trained to generate multiple valid pipe configurations that adhered to all structural and spatial rules.
• CAD Automation Integration: The solution was integrated with CAD APIs to automatically convert the AI-generated designs into 3D models, enabling seamless export into existing engineering workflows.

The automation of the design process led to substantial improvements in speed, cost, and accuracy.

• ⚡ 60% reduction in design cycle time through AI-driven automation.
• 📐 Higher design accuracy, minimizing the need for rework and validation by domain experts.
• 💰 20% cost savings in engineering workflows due to a significant reduction in manual effort.
• 🚀 Improved scalability, giving the team the ability to rapidly generate and evaluate multiple valid pipe layouts.

• Open3D: For 3D modeling automation and geometric processing.
• PyTorch: For the deployment and training of the AI/ML models.
• NumPy: For handling scientific computations and complex geometry.
• Python: For overall scripting and pipeline orchestration.