In vibration-assisted milling (VAM), an additional high-frequency oscillation is superimposed on the kinematics of a conventional machining process. This generates oscillations of the cutting edge in the range of a few micrometers, thereby causing a high-frequency change in the cutting speed and/or the feed. Consequently, a reduction of cutting forces, an increase of the tool life, and an improvement of the workpiece quality can be achieved. This paper shows and compares the effects of longitudinal and longitudinal-torsional (L-T) vibrations on the cutting force, the tool life, and the surface quality when milling Ti-6Al-4V. In comparison with the conventional milling process, the cutting forces are significantly reduced and the surface finish of the workpiece can be improved by introducing ultrasonic vibrations to the milling process. Longitudinal-torsional vibration assistance showed better overall process performance than the pure longitudinal vibration assistance.

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Ball screws are key components in machine tool linear feed drives since they translate the motors' rotary motion into linear motion. With usage over time, however, tribological degradation of ball screws and the successive loss in preload can cause imprecise position accuracy and loss in manufacturing precision. Therefore condition monitoring (CM) of ball screws is important since it enables just in time replacement as well as the prevention of production stoppages and wasted material. This paper proposes an idea based on a probabilistic classification approach to monitor a ball screw's preload condition with the help of modal parameters identified from vibration signals. It will be shown that by applying probabilistic classification models, uncertainties with respect to degradation can be quantified in an intuitive way and therefore can enhance the basis of decision making. Furthermore, it will be shown how a probabilistic classification approach allows the estimation of remaining useful life (RUL) for ball screws when the user only has access to discrete preload observations.

Ball screws and linear guides are among the key components of machine tools. Abrasive wear causes a loss in stiffness of these components over time affecting the attainable manufacturing precision and, eventually, leads to failures and costly down-time. In order to control these effects, the condition of the crucial feed drive components needs to be monitored. This paper shows, how the feed drive condition can be monitored by looking at the modal parameters of the system. It will be shown, that preload loss cannot only be detected globally, but can be traced back to the worn component. A distinct test cycle was developed for this purpose.

The material removal rates of machine tools are often limited by chatter, which is caused by the machine’s most flexible structural modes. Active vibration control systems mitigate chatter vibrations and increase the chatter free axial depth of cut. However, model-based control strategies reach their limit if the machine tool exhibits highly position-dependent dynamics. In this paper, an adaptive control strategy is presented. This strategy uses online system identification to adapt the controller. The adaption algorithm is mainly automated. However, a few parameters still need to be selected. Therefore, a methodology for the determination of the optimal parameters is proposed. The adaptive controller was implemented on a B&R PLC and its suitability was verified experimentally by the observation of notable increases in the chatter-free material removal rates.