Introduction

In CAE-driven safety development, bolted joints play a critical role in crash and structural performance, yet they are often oversimplified due to time and modelling constraints. Traditional solid bolt modelling is manual, repetitive, and difficult to scale, especially in large vehicle assemblies.

As vehicle architectures evolve with lightweight materials, electrification, and complex joining strategies, maintaining accuracy and continuity across the CAE digital workflow has become increasingly challenging.

Xipa -powered by ML solves automation of solid modelling by intelligently preserving design intent, ensuring consistent joint representation, and keeping the digital thread intact without manual rework.

Challenges in Conventional Solid Bolt Modelling

Manual Solid Bolt Modelling in CAE typically involves:

1. Creating detailed bolt geometry (shank, head, nut, washer)
2. Ensuring proper mesh quality and connectivity
3. Manually defining contacts and joint behaviour

These steps require substantial engineering effort and are highly dependent on individual expertise. In large-scale models, this leads to:

1. Increased modelling time
2. Inconsistent modelling standards across programs
3. Higher risk of missing or incorrect connections
4. Delays in simulation turnaround time

Solution using Xipa

What is Xipa?

Xipa is an ML-trained advanced CAE automation tool designed to streamline and standardize CAE pre-processing tasks. One of its key capabilities is Solid Bolt Modelling Automation, enabling CAE engineers to generate high-quality, solver-ready bolt models with minimal manual intervention. By embedding engineering knowledge into the CAE digital thread workflow, Xipa helps maintain consistency and continuity across the digital thread.

How Xipa Automates Solid Bolt Modelling

1. Intelligent Bolt Detection

Xipa automatically identifies bolt locations from CAD or FE models by recognizing geometric features such as:

1. Holes and cylindrical features
2. Mating and clamped surfaces
3. Repetitive fastener patterns

This eliminates manual bolt identification and ensures that all critical joints are captured systematically, even as designs evolve.

2. Parametric Solid Bolt Creation

Using predefined standards and ML-trained parameters, Xipa generates Solid Bolt mesh, including:

1. Bolt shank
2. Head and nut
3. Washers (if required)

The mesh is created consistently across the model, ensuring compliance with CAE best practices and uniform behaviour across vehicle platforms.

3. Mesh-Ready Output

The generated Solid Bolts are optimized for meshing, maintaining element quality and solver compatibility. This significantly reduces rework during meshing and solving stages.

Xipa supports two bolt modelling approaches:

1. Simplified Bolt Models
   • Idealized geometry for faster pre-processing
   • Improved solver performance for large crash models
2. Detailed / Profile Bolt Models
   • High geometric fidelity
   • Suitable for detailed stress and failure analysis

Automated Contact Definition and Connectivity

4. Automated Contact Creation

Correct contact definition is critical for realistic load transfer during crash events. Xipa automatically generates contacts between:

1. Bolt shank and hole surfaces
2. Bolt head and clamped parts
3. Nut/washer and mating components

Based on predefined rules and ML-trained patterns, Xipa assigns appropriate contact types such as tied, frictional, or sliding contacts.

Consistent and Error-Free Connectivity

Automated contact and connectivity ensure:

1. No missing or incorrect contacts
2. Consistent modelling standards across programs
3. Reduced solver errors and convergence issues

Once identified, connections are instantiated with standardized modelling strategies for stiffness, contact behaviour, preload, and failure logic. This ensures that the same assumptions are applied consistently across models and platforms.

By doing this, Xipa ML preserves the digital thread, eliminates variability between simulations, reduces rework, and ensures that crash results reliably reflect the latest design without slowing down the CAE process.

Conclusion

Xipa, powered by ML, automates Solid Bolt Modelling by preserving design intent, ensuring consistent joint representation, and maintaining the digital thread without manual rework. For complex CAE and crash simulations, Xipa eliminates variability by automating bolt detection, Solid mesh creation, contact definition, and solver-ready connectivity. This results in up to 90% reduction in manual modelling effort, improved quality, facilitates digital thread and faster turnaround.

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