Smart machine control to meet production needs
The number of machines in the EU is estimated to be over a million. Currently, unpredictable machining variables frequently lead to unstable conditions, overload and often serious damage to the machine tool system. In turn, these can lead to extended loss of productivity, capital and energy resources. To avoid such consequences, highly conservative processes and operations are employed to account for worst-case scenarios leading to inefficiency in utilisation of a machine's production capacity. Scientists initiated the EU-funded project 'Advanced intelligent machine adaptive control system' (AIMACS)(opens in new window) to develop reliable, real-time, self-optimising machine operation. The system utilises continuous monitoring of the most critical machining parameters, including cutting load, vibrations and energy consumption. It then takes into account costs associated with factors such as machining time, maintenance time and energy consumption. All of these are fed into an online multi-optimisation module that includes intelligent adaptation to optimise productivity, quality and cost in the context of whole factory planning. Adaptation, learning and improvement of optimisation occur during each machining process. Following definition of the multi-layer, multi-criteria optimisation system, the team conducted extensive research leading to optimisation algorithms. The hardware and software modules were created and their intercommunication was established. In the end, the modular plug-and-produce prototype system was installed in machine demonstrators that were evaluated in the field. Individual modules and commercial products for optimisation from the machine to the factory level are in the planning by consortium partners. The technology will be equally applicable to new machines as well as for retrofitting of the over one million existing ones. The ambitious AIMACS project developed much needed adaptive control technology to modify machining processes in real time. The system will intelligently adapt to varying machining conditions, production disruptions or changes, and plant performance variations. The technology has the potential to dramatically increase productivity while decreasing costs in a huge sector of the EU economy.
