Yu Xiang, suspension engineer at ALTEN China, has extensive experience in the development and performance integration of automotive suspension systems. In the midst of the AI wave sweeping through the manufacturing industry, he does not get lost in the mist named Replacing Humans but instead returns to the essence of engineering, calmly clarifying AI’s true role and boundaries in suspension development: AI is not a decision maker, but a tool to help engineers think faster and more clearly. Its real value lies in reducing uncertainty, not in replacing human judgment.

Background: Complexity and Methodological Challenges in Suspension Development

The suspension system is one of the most complex engineering objects in an automobile chassis. Its performance depends on multiple factors: geometric structure, elasticity and damping characteristics, bushing stiffness, tire properties, and control strategies. These factors are highly coupled and nonlinear. There are long-standing conflicts between ride comfort, handling stability, and durability.

For many years, suspension development has relied on physical modeling, engineering experience, and extensive testing. This approach has supported steady improvements in vehicle performance. However, as vehicle models become more standardized, project development cycles shorten, and user scenarios diversify, the conventional model—dependent on accumulated experience and localized optimization—has started to reveal limitations in efficiency, consistency, and knowledge reuse.

In this context, AI methods are increasingly integrated into suspension development processes. The core value of AI lies not in replacing engineers, but in enhancing the efficiency and certainty of engineering cognition during the development of complex systems.

The Basic Positioning of AI in Suspension Engineering

From an engineering perspective, AI cannot resolve the fundamental contradictions inherent in suspension development. Conflicts among different performance objectives persist, and the engineering trade-offs associated with product positioning cannot be automatically addressed by algorithms. Therefore, AI’s fundamental role in suspension engineering should be:

To assist in improving engineering cognition efficiency and certainty, rather than substituting engineers in decision-making.

The results produced by AI are essentially predictions or trend judgments about system behavior under established assumptions and data conditions. Their role should be limited to providing informational support before final decision-making, rather than directly forming engineering conclusions.

Core Engineering Value from the Perspective of Suspension Development Processes

Looking at the full development process, AI provides practical value mainly in three areas.

1. Clarify The Data, Narrow Down the Design Space

A suspension system involves many structural and performance parameters, and their relationships are highly nonlinear. Data-driven methods such as surrogate models, sensitivity analysis, or dimensionality reduction can systematically analyze multi-dimensional parameter combinations and their performance responses while satisfying basic physical constraints.

The engineering value is not to find the best parameters. Instead, AI helps engineers efficiently identify key parameters and their effective ranges. This narrows the design search space and reduces wasted iterations. The primary outcome of this phase should be a clear understanding of the design space structure.

2. Reusable digital models

In suspension development, discrepancies between CAE models and real vehicle tests are inevitable. In traditional processes, model corrections are often completed on a project-by-project basis, making it difficult to form long-term accumulations.

AI can assist in analyzing sources of model errors and their distribution patterns under different operating conditions. This supports continuous model parameter updates across multiple projects and phases. The goal is not to improve the simulation accuracy of a single project but to promote the formation of a reusable and evolvable digital suspension model system.

3. Joint Analysis Assistant

In active and semi-active suspension systems, structural parameters and control strategies are highly coupled. AI can serve as a joint analysis tool to evaluate system trends and sensitivities under different structural and control combinations.

However, this depends on clear problem definitions, reasonable modeling assumptions, and thorough engineering validation. AI supports system-level engineering judgment, but it does not automatically find the optimal system.

Application Boundaries of AI in Suspension Development

In engineering practice, the risks associated with AI often stem from indistinct usage boundaries rather than insufficient capabilities. Therefore, establishing clear engineering constraints is an indispensable part of the AI methodology.

1. AI Cannot Replace Structural Feasibility Judgment

The suspension system is still a mechanical structure. Its feasibility is limited by geometry, materials, load paths, and manufacturing conditions. AI can only analyze performance variations under stipulated structural premises and cannot determine whether a structural scheme is reasonable or manufacturable.

Particularly in extreme conditions, failure modes, and durability risks, AI conclusions must comply with physical analysis and experimental verification, clearly excluding them from the scope of automated decision-making.

2. The Trustworthy Range Depends on Data Distribution

Suspension engineering data is often uneven. There is plenty of data for normal conditions, but very little for extreme or failure cases. AI models can provide trend references within the data coverage area, but their conclusions in extrapolated areas should only serve as risk alerts.

Therefore, engineering processes should clearly mark where a model is applicable. This prevents mistaking a trend estimate for a firm conclusion.

3. Modeling Assumptions Are Engineering Assumptions

Every AI model is an abstract representation of a system and includes assumptions. These assumptions constitute the engineering boundaries of the model. Once operating conditions or system conditions exceed the assumed range, the model’s reliability drops significantly.

Engineering teams must possess the ability to identify the risks of model failure. They should include a check on whether the model is still within its assumed range as a separate item in reviews.

4. AI Can Not Directly Produce Engineering Conclusions

Engineering decisions involve goal weighting, risk preferences, and product positioning, which fundamentally exceed the capabilities of models. AI outputs should be strictly treated as decision support information, not as engineering conclusions.

Building Capabilities from an Engineering System Perspective

In the long run, AI capabilities will become an essential component of the chassis engineering system. However, what truly creates engineering differentiation is not specific algorithms or tools. It is:

• A continuously accumulating engineering data system
• A model system with an evolutionary path
• Engineering capabilities that embed AI into the development process

In this process, the core value of automotive chassis engineers has not been diminished. Understanding physical laws, judging model boundaries, and making system-level trade-offs are even more critical in complex engineering.

Conclusion

Overall, AI is not the ultimate goal of suspension engineering but rather an engineering methodology tool. In the foreseeable future, suspension development will still be based on physical modeling and experimental validation. What AI can provide is a means to reduce uncertainty and improve engineering efficiency in complex systems. The true determinants of suspension system quality remain the maturity of the engineering system, the long-term accumulation of data and models, and the sustained investment in the essence of engineering.