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.

Hybrid manufacturing combines manufacturing processes (typically additive manufacturing and machining) exploiting the benefits of each and enabling repair scenarios. Such an approach can be integrated with a robot, and if made portable, can form a flexible machine tool that can be easily transported anywhere to enable repairs in the field. However, the design of the load-bearing structure determines its transportability, and its stiffness plays a crucial functional role under dynamic loads and affects the product quality. Finding the right balance between weight and stiffness requires accurate boundary conditions and an effective design. In this work, a method is proposed towards process-driven optimization of a portable manufacturing cell structure. The dynamic cutting forces are determined through a machining process model and, via a simplified model of the robot arm, the forces at the base of the robot are estimated. Since the frame consists of beams, the layout optimization method is applied, using the estimated process forces as boundary conditions to optimize the arrangement of beams. The proposed method achieved 0.05 mm displacement in the load-bearing structure under milling and an acceptable accuracy of the position of a hole’s center during drilling, while the overall weight reduced by 17.6%.