How AI and Digital Twins Are Reshaping Extrusion Design

From Iteration to Prediction

Traditionally, extrusion profile design relied on iteration — prototype, test, adjust, repeat. AI-driven design turns that process on its head. By combining machine learning algorithms with simulation data, engineers can now predict performance outcomes before metal ever meets die.

Digital twins — virtual replicas of physical extrusion lines — create a live, data-driven feedback loop between design and production. By continuously comparing real-time sensor data (temperature, pressure, torque) with the idealised digital model, deviations are detected early. This enables proactive adjustments that minimise downtime, reduce scrap, and maintain process stability.

“AI and digital twins allow manufacturers to close the loop between design intent and production reality — it’s the future of precision manufacturing.”

Manufacturing Technology Centre, UK (2024)

A digital twin acts as a living simulation of the extrusion process, continuously updated through IoT and sensor inputs. This integration of physical and digital systems allows engineers to test what-if scenarios, optimise metal flow, and forecast maintenance needs — all before production is impacted.

Benefits for Bespoke Extrusion

• Reduced Prototyping Cycles: Virtual validation can cut physical trial runs by up to 50%
• Enhanced Tolerance Control: Algorithms model metal flow within the die, optimising geometry for consistent quality
• Predictive Maintenance: Digital twins anticipate wear on tooling and bearings, reducing unplanned stoppages
• Sustainability: Optimised profiles use less material and energy, lowering embodied carbon

By connecting AI, digital twins, and simulation-driven design, extrusion engineers gain a comprehensive view of system performance — improving both product quality and production efficiency.

Implementation at BWC Profiles

BWC Profiles is integrating simulation-driven design across its extrusion process, combining finite element analysis (FEA), computational fluid dynamics (CFD), and empirical production data to refine profile geometries before tooling begins.

This approach shortens development time, improves repeatability, and delivers measurable performance gains — ensuring every custom profile meets both engineering and environmental targets.

By embracing the digital transformation of manufacturing, BWC is not simply adapting to Industry 4.0 — it’s helping to shape it.

References

Manufacturing Technology Centre (MTC), AI in Manufacturing: UK Adoption Report, 2024
Siemens Digital Industries, Digital Twin in Discrete Manufacturing, 2023
National Manufacturing Institute Scotland (NMIS), Predictive Analytics for Extrusion, 2024