Industrial robots have been getting a more important role in manufacturing processes during the last decades, due to the flexibility they can provide in terms of reachability, size of working envelope and workfloor footprint. An especially interesting application are material removal processes and specifically machining. Use of robots in machining has opened new pathways for the development of flexible, portable robotic cells for several use cases. However, the peculiarity of such cells compared to traditional machine tools calls for novel approaches in their design and dynamic analysis. To this end, this work proposes an approach that integrates the digital twin of the machining process to set the boundary conditions for the design and dynamic analysis of the robotic cell. Physics-based modelling of milling is coupled with a Multi-Body Simulation of the robotic arm to define the inputs for the design of the cell. The design and dynamic analysis of the robotic cell is performed in a commercial FEA package, taking into account the requirements of the machining process.

The future of the milling process is the fully autonomous operation of the machine tools. Developments in terms of automation and machine tool design are now enabling fully autonomous operation. However, the optimization and stability of the process itself still remains a challenge. Chatter is the most significant bottleneck, and as such, it should be constantly monitored to ensure a stable process. This work proposes a sensor-integrated milling vice using an MEMS accelerometer as a non-invasive monitoring solution for chatter detection. The system is comprised by low-cost, industrial-grade components suitable for implementation in real production scenarios. The dynamic analysis of the sensor-integrated vice enables the definition of the sensor-integration point to ensure measurement quality. The use of advanced signal process algorithms for the demodulation of the vibration signal, along with the use of artificial intelligence for chatter detection, led to a high-performance system at a low cost. A wide set of milling experiments that has been conducted showcased that the proposed solution enables continuous, real-time process optimization in milling through in-process chatter detection.

The future of manufacturing processes is the fully autonomous operation of machine tools. The reliable autonomous operation of machine tools calls for the integration of inline quality control systems that will be able to assess in real time the process status and ensure that the machine tool, process and workpiece are complying with the manufacturing tolerances and requirements. Sensor integrated tooling for machining processes can significantly contribute towards this goal as they can facilitate monitoring close to the actual process. However, most of the solutions proposed so far are highly expensive or very complex to integrate and operate in an industrial environment. To this end, this paper proposes an approach for a sensor integrated vise using low-cost industrial sensors that can easily be integrated in existing machine tools in a non-invasive fashion. The development and dynamic analysis of the system is presented, along with an experimental verification against a lab-scale, high accuracy sensing setup