Extrusion International 5-2026

34 Extrusion International 5/2026 The difference may seem minor, but over a year of continuous production, avoiding just a handful of qual- ity incidents can prevent signi cant material losses, cus - tomer complaints and unplanned downtime. One of the most common misconceptions surrounding industrial AI is that value comes only from major break- throughs. In reality, competitive advantage often comes from preventing the small process disruptions that occur every day but rarely attract attention. The cumulative ef- fect of these improvements can be substantial. Digital Twins and Virtual Process Development Many advanced AI projects are built around digital twin technology. A digital twin is essentially a dynamic virtual representation of the extrusion process. Using live production data, the model continuously predicts how the line is expected to behave under different op- erating conditions. This allows engineers to evaluate changes in: • Throughput • Energy consumption • Product dimensions • Melt temperature • Pressure pro les • Material performance before applying those changes to the actual produc- tion line. For processors producing high-value products, the ability to test process modi cations in a virtual environ - ment can signi cantly reduce development costs and technical risk. What We Can Learn from Industry Leaders Some of the most advanced examples of AI-assisted manufacturing have emerged outside the extrusion sec- tor. Organisations such as Siemens have demonstrated digital twin technologies capable of modelling entire production systems, while major materials producers including BASF, Dow and Borealis continue to invest heavily in advanced process analytics and data-driven manufacturing strategies. Although these operations function at scales far larg- er than most extrusion facilities, the underlying prin- ciple remains remarkably similar: collect reliable data, build accurate process models, and use that information to support better decisions. The extrusion industry does not necessarily need to re- invent the wheel. Many of the technologies and imple- mentation strategies being adopted today have already been tested in sectors ranging from chemicals and auto- motive manufacturing to aerospace and semiconductor production. The challenge is adapting those lessons to the practical realities of extrusion processing. The Reality of Implementation Costs – An Unpopular Opinion: AI Is Not the Biggest Challenge A common assumption is that adopting AI requires sophisticated algorithms or expensive specialist soft- ware. In practice, many extrusion facilities face a more fundamental challenge. The greatest barrier is often data quality. Companies exploring AI projects frequently dis- cover that critical process data is incomplete, incon- sistent, or stored across multiple systems that do not communicate effectively. It is difficult to train ad- vanced machine-learning models when sensors have not been calibrated regularly, process records contain gaps, or production reporting still relies heavily on manual spreadsheets. In many facilities, improving instrumentation, stan- dardising data collection and establishing stronger process discipline will deliver more immediate ben- efits than deploying AI itself. The organisations achieving the strongest results with industrial AI are often those that first invested in operational fundamentals. The software itself is rarely the largest expense. In practice, the biggest investment is usually cre- ating the infrastructure needed to generate reliable data. Many extrusion facilities still operate equip- ment that was never designed for advanced analytics. Additional sensors, communication networks, data historians and integration work are often required before AI projects can begin delivering value. For a single extrusion line, an initial analytics proj- ect may cost between £20,000 and £100,000, while fully integrated closed-loop optimisation systems can require investments ranging from several hundred thousand pounds to well over £1 million, depending on complexity. The economics therefore depend heavily on pro- duction volume, product value, scrap levels and exist- ing digital maturity. Facilities with high material costs, tight dimensional tolerances or frequent downtime events generally achieve the fastest return on invest- ment. A More Realistic View of the Future Despite the enthusiasm surrounding AI, fully auton- omous extrusion plants remain some distance away. Many extrusion operations still struggle with inconsis- tent data quality, incomplete sensor coverage, legacy equipment and limited process standardisation. These challenges must be addressed before advanced AI sys- tems can function effectively. The most successful projects today are not replacing process engineers. Instead, they are helping engineers make faster and better-informed decisions. AI can iden- tify patterns hidden within millions of process data points, but it still lacks the practical understanding that experienced extrusion personnel develop over years of troubleshooting, product development and process op- timisation. The most effective manufacturing environments will therefore combine human expertise with machine intel- ligence rather than viewing one as a replacement for the other. EXTRUSION TECHNOLGIES, AI