They are advancing industrial artificial intelligence through a data alliance.
Several leading machine tool manufacturers—including Trumpf, Chiron, Renishaw, Heller, Grob, the Voith Group, and the Machine Tool Laboratory of RWTH Aachen University—are joining forces with Siemens to increase the sector's efficiency through industrial artificial intelligence developments.
The partnership is based on the exchange of anonymized machine data while complying with data protection and security standards. This data will be used to develop and train AI solutions tailored to the requirements of industrial manufacturing. Among other things, the data generated by the alliance will be used for the automatic creation of so-called NC programs, which are the "work instructions" of specialized manufacturing machines. Additional target areas include predictive maintenance with accurate, machine-specific forecasts; adaptive manufacturing processes that adjust to changing conditions in real time; and the optimization of energy efficiency through the intelligent control of machine parameters.
The alliance's long-term strategy also includes involving additional companies—even from outside the machine tool industry—to enable industrial artificial intelligence to be applied more broadly across different industries. The partnership represents an important step toward the realization of an industry-specific AI model. Siemens presented this vision, the Siemens Industrial Foundation Model, at Hannover Messe 2025.
"Together with our customers and partners, we are taking a significant step toward the scalability of industrial AI," said Roland Busch. The CEO of Siemens AG stated that he sees great potential in this for the European economy and its strong industrial base—from the automotive industry, the chemical and pharmaceutical industries, to mechanical engineering, energy, healthcare, infrastructure, and transportation. By making their data assets available for the development of generative AI models, companies can ultimately achieve higher levels of productivity.