Unlocking the potential of AI in manufacturing

Artificial intelligence (AI) has long been heralded as a transformative force in manufacturing, promising unprecedented efficiency and precision. However, the road to fully integrating AI into production technology remains challenging. From inconsistent data structures to the need for standardized measurement systems, the journey toward smart automation requires more than advanced algorithms—it demands a robust digital infrastructure and collaborative international efforts. Experts from RWTH Aachen University shed light on the current status of AI in machine tools and explore the advancements needed to bridge the gap between potential and reality.

How Far Along is AI?

"No automation without data": this axiom underscores the challenges facing AI adoption in machine tools. “The CAD-CAM-NC chain in production technology is often still characterized by a diverse software landscape with an inconsistent data structure,” explains Dr. Marcel Fey, senior engineer in the Department of Machine Data Analysis and NC Technology at the Laboratory for Machine Tools and Production Engineering (WZL) at RWTH Aachen University, under the direction of Prof. Christian Brecher. Prof. Brecher coordinated the nationwide, BMBF-funded ProKI project, which is now a permanent feature in the WGP (Wissenschaftliche Gesellschaft für Produktionstechnik – German Academic Association for Production Technology).

“While the CAM system still contains a lot of information about the tool, blank, material, and process parameters, this context is largely lost on the machine tool when the NC code is created. However, this context is necessary to implement meaningful AI applications in production,” Fey elaborates. Even in digitally advanced companies, collecting raw data with the required contextual information poses greater challenges than the actual AI application itself.

Interestingly, the mere availability of contextualized production information can deliver considerable customer benefits. “In many cases, evaluating this data using engineering expertise can already yield excellent results without relying solely on AI,” adds Fey. While AI offers enormous potential for uncovering complex relationships not fully understood causally, the immediate challenge for German mechanical engineering lies in building a software infrastructure capable of delivering the required data to realize AI's benefits in production technology.

The Machine Tool as a Coordinate Measuring Machine

Smart automation hinges on seamlessly connecting processes. With machine tools increasingly equipped with 3D touch probes as standard, their potential use as coordinate measuring machines is apparent.

“The software and hardware available on the market already support this,” reports Dr. Philipp Dahlem from the WZL at RWTH Aachen University, Chair of Information, Quality, and Sensor Systems in Production, led by Prof. Robert Schmitt. However, challenges persist. “Although technological advancements have made machine tools increasingly precise, measurement uncertainty remains an issue. According to the golden rule of measurement technology, measurement uncertainty should be smaller than the tolerance by a factor of 10,” Dahlem notes.

Machine tools face fluctuating production conditions that make achieving stable measurement results challenging. “The ISO TS 230-13 standardization project is addressing this issue by defining industrially applicable methods for determining measurement uncertainty in machine tools,” explains Dahlem. The project aims to outline potential applications and limitations of this technology, with further developments expected to be showcased at EMO 2025.

Standardization Project Gains Global Interest

The ISO TS 230-13 standardization project marks a significant step toward integrating measurement capabilities into machine tools for industrial applications. By enabling component measurements directly on the machine tool, quality control loops can be shortened, and production resources conserved. Combined with advances in AI and the study of machine tool stability, a future where machine tools monitor their production quality is increasingly within reach.

Dr. Philipp Dahlem, who leads the standardization project, emphasizes its global impact. “The project has attracted significant international interest,” he says. “Initiated in Germany, this effort also holds relevance for the strong machine tool industry in China, which is becoming a major player in influencing standardization efforts.”

Closing the Gap Between Automation and Digitalization

In today’s manufacturing landscape, automation and digitalization are indispensable. Companies that invest in solutions offering immediate support and long-term value will enhance their productivity and competitiveness. At EMO 2025, decision-makers and users will encounter cutting-edge innovations designed to position them for a smart, future-proof manufacturing environment. By embracing these advancements, businesses can stay one step ahead in an increasingly complex and competitive industry.

This article, originally authored by Dag Heidecker, a specialist journalist from Wermelskirchen, has been adapted and edited by International Metalworking News for Asia to align with this section.