New-Tech Europe | Q3 2026 | Digital Edition

product lifecycles continue to shorten, this combination of precision and adaptability is expected to become one of the defining characteristics of next generation factories. Physics-Informed AI: Combining Data with Engineering Knowledge Artificial intelligence learns from data. Manufacturing, however, is governed by far more than historical observations. Every production process is constrained by the laws of physics, material behaviour, thermodynamics, fluid dynamics and mechanical engineering principles. These relationships remain valid regardless of how much operational data has been collected. Physics-Informed AI brings these two worlds together. Rather than relying exclusively on statistical learning, these models incorporate engineering equations and physical constraints directly into the learning process. The result is AI that not only recognises patterns but also understands the engineering principles that govern industrial processes. This approach is particularly valuable where operational data is limited or expensive to obtain. New production technologies, advanced materials and highly specialised manufacturing processes often lack the large datasets required by conventional AI models. Integrating engineering knowledge allows reliable predictions even under conditions where historical data alone would be insufficient. Applications are expanding rapidly across advanced manufacturing, including process optimisation, additive manufacturing, thermal management, materials engineering and industrial simulation. For manufacturers, the significance is clear. The future of Industrial AI will depend not only on larger datasets, but also on combining artificial intelligence with decades of accumulated engineering expertise. The Challenges Ahead Despite rapid technological progress, Industrial AI remains at an early stage of adoption. The technologies described in this article already exist, but integrating them into real manufacturing environments presents significant technical and organisational challenges. Data quality remains one of the most important obstacles. Many factories continue to operate equipment from multiple

AI systems require context to generate reliable recommendations. Digital Twins provide that context by describing not only the current state of manufacturing systems but also their relationships, constraints and historical behaviour. Increasingly, they are becoming the operational memory of the intelligent factory. Physical AI: Bringing Intelligence to the Factory Floor For decades, industrial automation has relied on deterministic control. Robots execute predefined movements, automated systems follow carefully programmed sequences, and production lines operate according to engineering logic developed long before manufacturing begins. This approach has transformed modern industry, but it also has limitations. Conventional automation performs exceptionally well in predictable environments. It becomes less effective when conditions change unexpectedly or when machines must adapt to situations that were never anticipated during system design. Physical AI represents the next stage in industrial automation. By combining artificial intelligence with robotics, machine vision, sensor fusion and advanced motion control, Physical AI enables machines to interpret their surroundings and respond dynamically to changing conditions. Instead of simply repeating programmed actions, intelligent systems begin to understand what is happening around them and adjust their behaviour accordingly. The impact is already becoming visible across manufacturing. Intelligent robots can compensate for small variations in component positioning. Autonomous mobile robots continuously optimise their routes as factory conditions change. AI-powered vision systems distinguish between acceptable process variation and genuine quality defects with increasing accuracy. Importantly, Physical AI does not replace conventional automation. Deterministic control remains essential for safety-critical processes requiring absolute precision and repeatability. Artificial intelligence complements these systems by introducing adaptability where traditional programming reaches its limits. As manufacturing environments become more flexible and

For manufacturers, the significance is clear. The future of Industrial AI will depend not only on larger datasets, but also on combining artificial intelligence with decades of accumulated engineering expertise. " " 22 l New-Tech Magazine Europe