Metal additive manufacturing in aerospace: A review

Recent progress of chatter prediction, detection and suppression in milling

Currently, Edge computing (EC) paradigm is adopted to provision the low-latency resources for the massive real-time services in Internet of vehicles (IoV). To alleviate the QoE (Quality of Experience) degradation of the vehicular users due to the uncertainties (e.g., resource conflicts and communicating interruption), software-defined network (SDN) is involved in the EC-enabled IoV to manage the cooperative operation of distributed edge nodes (ENs). However, the increasing privacy leakage for the IoV service offloading causes the disclosure of the sensitive information, including driving location, personal information of the driver, etc. Moreover, the regulation of SDN is practically insufficient, as the general control is incompetent to maintain balanced operation with the premise of efficient service utility. In view of these challenges, a secure s ervice o ffloading me thod, named SOME, is designed to promote IoV service utility and edge utility, meanwhile ensuring privacy security, in SDN-enabled EC. Specifically, an SDN-based framework for IoV service management is developed to address the inherent uncertainty of edge network by SDN controllers. Besides, the locality-sensitive-hash (LSH) is leveraged to realize utility- and privacy-aware service selection. Eventually, comparative experiments are implemented to verify the effectiveness of SOME.

Secure Service Offloading for Internet of Vehicles in SDN-Enabled Mobile Edge Computing

Self-optimizing machining systems

Automatic tuning of active vibration control systems using inertial actuators

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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Experimental investigations on longitudinal-torsional vibration-assisted milling of Ti-6Al-4V

Comparison of Different Control Strategies for Active Damping of Heavy Duty Milling Operations

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.

Estimating remaining useful life of machine tool ball screws via probabilistic classification

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.

/pdf/adaptive-active-vibration-control-for-machine-tools-with-4cexf4e3hi.pdf

Robin Kleinwort

Papers

Automatic tuning of active vibration control systems using inertial actuators

Experimental investigations on longitudinal-torsional vibration-assisted milling of Ti-6Al-4V

Comparison of Different Control Strategies for Active Damping of Heavy Duty Milling Operations

Estimating remaining useful life of machine tool ball screws via probabilistic classification

Adaptive Active Vibration Control for Machine Tools with Highly Position-Dependent Dynamics