February 2026 Volume 8

EQUIPMENT & TECHNOLOGY

THE IMPACT OF AI IN TRAINING AND DEVELOPMENT ON FORGE SHOPS OF THE FUTURE By THORS eLearning Solutions

A s one of the oldest and most reliable manufacturing little over time. Yet the industry itself is anything but static. Competitive pressures, workforce demographics, and expectations around productivity and consistency continue to rise. Forging leaders routinely evaluate investments in equipment, tooling, and process improvements to meet these demands, but what is less visible, but equally critical, is employee development – evolving how people learn and how knowledge flows through the organization. Structured training programs are designed to develop people’s skills, which in turn directly influence quality, safety, production, and risk. As modern-day AI (artificial intelligence) tools like ChatGPT, Gemini, Claude, CoPilot and others become a normal part of how people learn and solve problems, the cost of relying solely on traditional or conventional training approaches is becoming harder to ignore. With new classes of AI tools emerging and advanced AI capabilities now embedded in many commercial LMS platforms, the question for forging organizations is no longer whether AI-powered learning solutions are too futuristic for this industry. It is whether organizations can afford not to adapt as employee learning behavior itself changes. There are many benefits to be gained from adding AI-based learning tools to the forging work environment, which we’ll explore in this article. Learning Behavior Has Changed, While Training Systems Have Not Outside the workplace, many employees already rely on AI tools to answer questions, summarize information, and explore cause-and effect relationships. For new workers entering forging roles, this behavior is not new or experimental; it has become normal and natural. When new employees encounter shop-floor training systems built around printed materials, physical manuals, or scattered digital files, friction appears. Initially, this shows up as lost time spent searching for answers or seeking help from a knowledgeable co worker. Over time, it becomes a deeper issue. Employees either accept “time-outs” for knowledge retrieval as standard operating procedure or they develop informal workarounds to get the information they need. These all-too-common “blind spots” reflect a growing gap between how people expect to learn and solve problems – and how organizations provide information. When internal systems are difficult to navigate, employees do not stop learning – they simply look elsewhere. Let’s look at ‘Closed-Die Forging’ as an example because it is widely used on shop floors. processes, the fundamentals of forging – such as controlled deformation, heat, force, and material flow – have changed

Typical shop-floor challenges New operators often learn the sequence of steps quickly: billet heating, placement, press operation, and part removal. What takes much longer to develop is the understanding behind those steps. How billet temperature affects die fill. How forging force influences flash formation. How lubrication impacts die life. This difference – between knowing what to do and understanding why – matters. Operators who lack cause-and-effect understanding may struggle when conditions change, defects appear, or equipment behaves unexpectedly. Traditionally, this knowledge develops slowly through experience, trial and error, and mentoring. While effective, this approach consumes time, scrap, and supervisory resources, putting a strain on limited resources. In contrast, AI-powered learning tools offer a way to accelerate this understanding without increasing production risk. Why Traditional Training Struggles to Scale Conventional forging training relies heavily on experience. Shadowing and mentoring new team members remains essential, but it can be difficult to scale this model consistently across shifts, facilities, and workforce turnover. Documentation helps standardize expectations, but static instructions are unable to convey dynamic processes such as metal flow or defect formation. Manuals explain procedures, but they rarely build intuition. As a result, learning often remains reactive – focused on trial and error to gain experience, and correcting problems after they occur rather than preventing them. With AI-powered learning solutions, hands-on training is paired with a learning layer that allows operators to explore processes, visualize outcomes, and build their decision-making skills before mistakes occur on the shop floor. Practical Applications of AI in Closed-Die Forging Training The following examples illustrate how AI-powered learning tools can support closed-die forging training in practical, shop-floor relevant ways. 1. Parameter–Effect Exploration AI-based learning tools allow learners to explore parameter effects virtually by enabling them to adjust variables such as billet temperature, forging force, lubrication level, and die alignment and immediately see the impact. Outcomes such as underfill, excessive flash, or die damage can be explored virtually using AI. This approach builds cause-and-effect understanding rather than rote memorization. Operators develop intuition that helps them respond more effectively to real-world variability – without scrap or downtime.

FIA MAGAZINE | FEBRUARY 2026 14