Top Technologies | Engineering
LinkedIn Issue 01 - February 2026
Top technologies in engineering: simulation, FEM analysis, holistic lightweight construction and technical weight management.
Introduction
Dear Readers,
In recent months, TGM has been repeatedly confronted with a very similar question from various industries: What role can artificial intelligence realistically play today in engineering and in holistic lightweight construction - beyond buzzwords and visions?
These discussions took place in parallel in projects with OEMs from various industrial sectors as well as with system and Tier 1 suppliers. It was noticeable that the questions revolved less around individual tools and more around fundamental topics: early decision-making, system understanding, robustness, simulation methodology and dealing with uncertainty in complex development projects.
This prompted us to create a technically sound newsletter series on current key technologies - not as an overview of trends, but as an engineering classification based on real projects, methods and research results.
TGM has been supporting development programs in railroad, automotive and aerospace projects for over 20 years and regularly operates at the interface of system architecture, lightweight construction, FEM simulation and technical weight management. This perspective also characterizes the following explanations: AI is not seen here as a substitute for engineering work, but as a tool whose benefits and limitations must be clearly understood.
AI in engineering - basic understanding
Artificial intelligence is often discussed very abstractly in engineering. In industrial product development, however, it is becoming increasingly clear that the benefits of AI do not come from autonomous algorithms, but from the targeted coupling of AI with physical understanding, simulation methods and systemic lightweight construction.
Particularly in safety-critical industries with long life cycles, AI is not a substitute for engineering methods, but a tool for structuring, evaluating and validating decisions.
1. AI in early design and development phases: Dealing with uncertainties
In pre-development, data is incomplete, variants are numerous and boundary conditions are unstable. Research-oriented AI approaches are used here for:
- Systematic exploration of variants (e.g. architecture, materials, structures)
- Estimation of physical influencing variables such as loads, mechanical similarities, mass effects, stiffness and functional integration at a low degree of maturity
- Identification of primary and secondary linear and non-linear physical effects (interfaces, functional superpositions, weight spirals)
The added value lies not in „optimal solutions“, but in more robust decision-making spaces.
2. holistic lightweight construction as a prerequisite for meaningful AI application
AI is particularly effective in lightweight system construction, rather than in isolated, verifiable structural component optimizations.
Relevant fields of research are:
- Function integration and system decoupling
- Analysis of load paths, effective areas and neutral axes
- Interactions between structural, material and system decisions
Only when these relationships have been modeled can AI:
- Prioritize relevant degrees of freedom
- Making non-intuitive lightweight construction potential visible
- Transparently present conflicting objectives (mass vs. rigidity vs. service life, acoustics, fatigue strength)
3 AI & FEM: support instead of replacement
In simulation, AI does not replace FEM analysis. Rather, it provides support for:
- Preprocessing standard processes
- Preselection of critical load cases
- Plausibility check of results
- Derivation of sensible optimization directions (e.g. topology, wall thicknesses, material substitution)
- faster parameter convergence
- Reduction of unnecessary iterations
The coupling of data-driven methods with physically validated models is particularly relevant to research.
4. mass-properties-engineering, weight control & weight management
Weight is not a result, but a dynamic development parameter. AI is used here for:
- Assistant-supported forecasts for low maturity levels
- Risk and tolerance assessment
- Evaluation of variants, platforms and modular architectures
- Consistency checks between top-down objectives and bottom-up maturity
This significantly increases decision-making reliability, especially in long-term projects.
5. industry-specific research perspectives
Relevant industries:
- Rail, Aerospace, Defense
- Bus industry and commercial vehicles
- Mechanical engineering and medical technology
Typical framework conditions:
- Long service life and high safety requirements
- High regulatory requirements and verification obligations
- Statically overdetermined systems with complex linear and non-linear influencing variables
- High costs for later changes
- Ambitious conflicts of objectives
AI serves here as a tool for early risk identification and deepening system understanding.
Additionally in the premium automotive and sports car sector:
- Short development cycles with high safety requirements
- High innovation density and dynamics
AI is used here to accelerate concept evaluations and technology decisions.
6. limits and open research questions
Current challenges are:
- Data quality and comparability
- Explainability of AI results
- Integration into existing engineering processes
- Safeguarding against standards and approval
The consensus in research is clear: AI is only resilient if it is based on sound engineering methods.
Conclusion
AI in engineering is not an end in itself. Its added value arises where it complements - not replaces - systemic thinking, lightweight construction knowledge, FEM methodology, mass-properties engineering and weight management.
The combination of experience, physics and AI is significantly more efficient than each approach on its own, especially in holistic lightweight construction.
Sources (selection)
- Sacks et al: BIM Handbook, Wiley, 2018
- Agrawal et al: Prediction Machines, Harvard Business Review Press, 2018
- Karniadakis et al: Physics-informed machine learning, Nature Reviews Physics, 2021
- Bendsøe & Sigmund: Topology Optimization, Springer, 2004
- VDI guideline 2221, 2019 edition
Hans-Peter Dahm
TGM Lightweight Solutions GmbH
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