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Accelerating Structural Reinforcement Development Through Digital Engineering, Automation and AI

How L&L Products Is Reducing the Time Required to Reach a Confident Engineering Decision

As vehicle programs become increasingly complex, CAE teams face a growing challenge: evaluating larger design spaces while meeting increasingly aggressive targets for crashworthiness, stiffness, durability, mass reduction, manufacturability, and cost.

While simulation has dramatically accelerated vehicle development, engineers are often constrained not by their ability to run simulations, but by the number of potential design iterations that can realistically be assessed within a program timeline.

In this technical paper, Núria Ignés, Structural Product Development Manager at L&L Products Europe, examines how digital engineering methodologies—including simulation, optimization, automation, design exploration, and PhysicsAI®—can be combined to accelerate structural reinforcement development and enable faster engineering decision-making.

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The Challenge Facing Modern CAE Teams

Today's engineering organizations are expected to:

• Evaluate more concepts in less time
• Reduce vehicle mass while maintaining performance
• Improve crashworthiness and durability
• Balance performance, manufacturing, and cost targets
• Support increasingly compressed development schedules

As simulation capabilities continue to expand, the number of possible design alternatives often exceeds what engineering teams can practically investigate.

This raises an important question:

Can historical simulation knowledge be leveraged to accelerate
future engineering decisions?

This paper explores L&L Products' journey toward answering that question.

Technical Topics Covered

• Building a Digital Engineering Ecosystem
• From Simulation Data to Engineering Knowledge
• PhysicsAI® and Predictive Engineering
• The Real Challenge: Data Quality
• Structural Reinforcement Development as a Digital Engineering Challenge

Who Should Read This Paper?

This paper is designed for:

• CAE Engineers
• Simulation Engineers
• Crash Engineers
• Durability Engineers
• BIW Engineers
• Structural Engineers
• Vehicle Architecture Teams
• Optimization Specialists
• Digital Engineering Managers
• Engineering Data Scientists
• OEM and Tier-1 Development Teams

Key Insights

• Predictive engineering starts with structured engineering data
• Automation enables knowledge generation at scale
• AI complements engineering expertise rather than replacing it
• Historical simulation knowledge can accelerate early-stage concept evaluation
• Model quality depends as much on data quality as algorithm selection
• Predictive methods can help engineers focus more time on decision-making and less time on repetitive iteration management