Q&A Digital challenges at the EMO Hannover
Despite fantastic digital opportunities, Prof. Dr.-Ing. Frank Barthelmä is certain of one thing: without more openness in the machine tool industry, the digital Industry 4.0 concept is not going to pay off for small and mid-tier enterprises.
Reason enough for the Thuringian-born tool expert and Executive Director and Institute Manager of the German Society for Production Technology and Development (GFE – Gesellschaft für Fertigungstechnik und Entwicklung Schmalkalden e.V.) to use the EMO Hannover 2017 not only for acquiring information, but also for recruiting co-campaigners for digital networking.
Professor Barthelmä, how is the trend towards Industry 4.0 influencing metal-cutting applications?
Industry 4.0 is influencing the process chain in two ways: firstly in terms of technology and engineering, and secondly in terms of the data flowing along it. When both of these interact to optimum effect, we obtain a textbook example for Industry 4.0. This applies in regard to quality and disturbance variables, but also and increasingly to predictable parameters such as tool paths and tool lifetimes, achievable surface qualities, and for maintenance intervals for machines and lines. Ideally, users receive all important information on the entire spectrum of process-related factors. What’s more, all data available can now be recorded and evaluated in real-time, and control loops created, so as to upgrade the efficiency of the processes and the quality of the products involved thanks to more transparent metal-cutting.
How far are intelligent tools already the state of the art?
If you remember: a bit more than ten years ago, there was an ongoing debate on whether or when a tool can be classed as intelligent. Nowadays, within the context of Industry 4.0 we are talking about intelligent holistic solutions, in which, of course, tool sensors and actuators play an important role. So when you say “state of the art”, you’re absolutely correct. Not only in terms of using increasingly miniaturised and more energy-efficient sensors or actuators in the tool itself, but also with a view to their utilisation in the overall system comprising the tool, the machine and its control system, and the application concerned.
But how can the multiplicity of data now being acquired be evaluated to optimum effect?
The answer to this is still in its infancy at many of our typical customers, the small and mid-tier companies. Many potential users of intelligent solutions of this kind, especially in these SMEs, are sometimes unable to assess what data they actually need in order to render their technology/IT fit for purpose, and to generate from these new production lines when needed. This extends to new business models that may prove necessary. Universities and large corporations are already well advanced in this respect, whereas many of the small and mid-tier companies are still in the exploratory phase. For meaningful analysis, moreover, a comprehensive data history is required, in order to correlate it with new key statistics: but what do we actually know about the technical wisdoms of our predecessors? So what we need here is even more collaboration between the academic and business communities when it comes to generating new ideas, models and above all new solutions. What I would like to see here, for instance, is more joint projects involving partners from the academic community and the industrial sector representing a highly disparate range of scientific disciplines, which elucidate these questions not least with the aid of live demonstrators, for example.