Think ahead.
Networking.
Driving forward.

Trends:

• Embedded & case-specific AI: AI is increasingly being integrated into production processes, not just as a "nice-to-have" - e.g. for quality control, predictive maintenance or real-time optimization.
• Generative AI / automated design: use of AI models to generate and optimize product designs faster, for example in terms of material usage or lightweight construction.
• Digital twins & simulation: Virtual replicas of production facilities or processes are becoming more important for scenario planning, optimization, simulation of changes without real interruption.
• Edge AI + IoT integration: Data is increasingly being processed close to the source (machines, sensors) in order to reduce latency, enable real-time control and save bandwidth/data transport.
• Operational AI systems: Systems that not only make predictions, but also intervene in production lines or automatically adjust parameters (adaptive control).

Challenges:

• Data quality, data availability and silos.
• Skills/job profiles are changing - production teams need more knowledge in data analysis, AI tooling.
• Security and robustness: How to protect AI systems against attacks, errors and unintended consequences?
• Scaling and integration into existing systems.

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Trends:

• Hyperautomation: Combination of robot-supported automation solutions, RPA, AI and analytics to automate as many processes (productive, administrative) as possible.
• MES/SCADA / real-time monitoring: Modern process automation increasingly relies on systems that control and monitor production processes in real time.
• Shorter throughput times & responsiveness: Just-in-time / just-in-sequence requirements, greater diversity of variants, demand for shorter delivery times require more flexible automation processes.
• Sustainability & resource efficiency: Automation to lower material and energy consumption, reduce waste and make processes more efficient.

Challenges:

• Increasing complexity - automated processes have to deal with a variety of products, components and supply chains.
• High initial costs; ROI must be clear, especially for SMEs.
• Integration of old and new systems, standardization.

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Trends:

• Electrification and increased efficiency: demand for more efficient drives (fewer losses, better energy management, energy recovery).
• Integration of sensor technology & intelligence: drives with integrated sensors, monitoring (temperature, vibration), self-diagnosis.
• Lightweight & compact designs: Reduced mass, more compact housings, optimized cooling, etc., particularly relevant for mobile systems or in vehicles.
• Modularity & flexible drives: Universal drive modules or modular systems that can be used in different configurations.
• Regenerative drives & hybrid systems: Combinations with electric, hydraulic or pneumatic components, energy recovery if necessary.

Challenges:

• Thermal issues & cooling at high power.
• Materials and manufacturing techniques (e.g. coatings, precision) must also cope with increasing complexity and higher requirements.
• Reliability, service life, maintainability.

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Trends:

• Mobility + cobots: Mobile cobots (mobile bases + collaborative gripper arms) are gaining in importance. They extend the field of application beyond stationary workstations.
• Simple programming & adaptivity: Cobots are becoming more user-friendly, with more intuitive interfaces, AI-supported planning and adaptivity (e.g. learning ability, simple conversion).
• Safety & human-robot collaboration: New safety concepts, attention to ergonomic aspects, safer sensor technology (e.g. distance detection, collision avoidance).
• Use in areas with a shortage of skilled workers: Cobots help to alleviate labor shortages by taking on tasks that are repetitive or physically demanding.
• Digital twins & simulation also in robotics: Before planning and operation, for validation, to optimize cycle times, movements and layouts.

Challenges:

• Safety (physical and IT) when working directly with humans.
• Flexibility vs. complexity: Quick to set up, but many applications require adjustments.
• Cost and maintenance, especially for moving units.

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Trends:

• IT-OT convergence & visibility: Many companies are increasingly connecting their production (OT) with IT networks. This increases the attack surface. Visibility across both areas (monitoring, logging) is becoming more important.
• Zero trust architectures in production networks and OT. Every access is checked, no longer "everything within the network is trusted".
• Regulation & compliance: New laws and regulations (e.g. EU-wide Cyber Resilience Act, NIS2, IEC 62443) enforce stricter security requirements.
• Security hardware & firmware measures: Stronger authentication, encryption, secure boot processes, Hardware Security Modules (HSM), etc. Also the protection of firmware and components against manipulation.
• Ransomware and supply chain attacks: Production facilities are more frequently the target of ransomware, attacks via suppliers or networked third-party providers are on the rise.

Challenges:

• Old systems and devices with outdated protocols that were not originally designed for networked environments.
• Shortage of security specialists, especially OT security.
• Balance between security and availability/performance - production systems should run as smoothly as possible, but security must not lead to major additional costs.

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Trends

• Automated AM cells & robotics
• Integration of robots for handling, assembly and post-processing.
• AM becomes part of networked production lines.
• In-situ monitoring & digital twins
• Sensors + AI enable process monitoring in real time.
• Goal: consistent quality and self-regulation.
• AI & Design for AM (DfAM)
• AI-supported topology optimization and parameter control.
• Function integration instead of classic assemblies.
• Multi-material & embedded functionality
• Combination of structural and functional materials (sensors, conductor paths).
• Creation of genuine mechatronic components "from the printer".
• Scaling & sustainability
• Focus on series production capability, material efficiency and recycling.

The challenges

• Process stability & reproducibility
• Fluctuations between systems and batches make series production difficult.
• Post-processing & throughput
• Post-processing remains a bottleneck - automation is crucial.
• Material & qualification costs
• Limited variety of materials, high testing costs, standards still lacking in some cases.
• System integration
• Interfaces to robotics, MES/ERP and safety standards often inconsistent.
• Competencies & economic efficiency
• Need for interdisciplinary expertise (AM + automation + data analysis).
• High investments, ROI only with well thought-out use case.

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