No abstract

A tool magazine is one of the key functional components of machining centers with frequent faults. The reliability level of a tool magazine directly affects the reliability level of the machining center. After establishing a reliability test bench and a prognostic and health management system for a tool magazine, a novel fault-forecasting method for machining center tool magazines based on health assessment is proposed. First, the health status of each tool magazine subcomponent is determined using the grey clustering method. Second, the weight of each tool magazine subcomponent is determined using an entropy weight method. Third, the health status of the tool magazine is evaluated via fuzzy comprehensive evaluation. If the tool magazine exhibits an unhealthy status, then the subcomponent with the worst health status is selected for fault forecasting. In addition, standardized treatment, stationarity test, and differential processing are conducted separately on the raw performance indicator data of the worst subcomponent. Finally, the performance indicators of the worst subcomponent are forecasted with the constructed autoregressive moving average model. Using tool-falling failure as an example, the forecasted and experimental tool-pulling forces are compared and analyzed, and the prediction accuracy of the proposed method is verified.

Automatic tool changer system (ATCS) and drawbar mechanism (DM) are two of key basic parts in machining centers for realizing automatic tool-changing cycle. In the condition monitoring, fault diagnosis and failure warning of the ATCS and DM, the dynamic force is an important characteristic signal. However, there is little research about the specific dynamic force measurement system in this regard. Thus, a novel dynamic force measurement system (DFMS) is developed and implemented. Based on the BT40 toolholder, a resistance strain gauge-based force senor is used to convert the dynamic force signal into electrical signal. The real-time dynamic force acquiring system is controlled via an 8-bit RISC microcontroller. Digital measurements are obtained from the 24-bit sigma-delta analog-to-digital converter with a programmable gain array, which then are transmitted to the upper computer software via a wireless transceiver for display and storage. Finally, a Teager energy operator based dual-threshold two sentences endpoint detection method is proposed to extract the maximum dynamic force and the duration time. Experimental results show that the DFMS is reliable and can be easily used to detect the dynamic force for the ATCS and DM.

This paper presents a novel control strategy for transferring large inertia loads using flexible space manipulators in orbit. The proposed strategy employs a Luenberger state observer and damping-stiffness controller to address issues of large tracking error and vibration. A comprehensive joint dynamics model is developed to identify the main sources of disturbance, and a Luenberger state observer is designed to estimate unmeasurable transmission deformation. Transmission stiffness and load inertia perturbations are identified based on the estimated results. By adjusting velocity damping and the gain of the forward channel, perturbations are suppressed to maintain optimal system damping and stiffness. Simulation and physical experiments demonstrate the effectiveness of the algorithm, with simulation experiments showing smoother joint output characteristics and minimal vibration under large load inertia changes, and a 97% reduction in internal deformation. Physical experiments demonstrate improved joint dynamic command tracking performance, with an 88% reduction in position tracking error. The algorithm provides a practical and efficient approach for transferring large inertia scientific payloads in space.

An automatic tool-changing system (ATCS) is one of the key sub-systems for realizing automatic tool changing in machining centers. Each step in a tool-changing cycle tends to result in impacts, and thus generates transients in the vibration signal. The impact features often reflect important operational information related to the ATCS dynamics, and a crucial problem for impact-feature extraction is how to effectively represent the transients. A novel method for extracting impact features from an ATCS is proposed, based on sparse representation theory. A parametric multiple-impulse dictionary is constructed by the unit impulse-response function of a damped multiple-degree-of-freedom system, whose modal order, amplitudes, natural frequencies, relative damping ratios and initial phases are directly identified from the vibration signal by an improved state-space method. This leads to high similarity between atoms and impact-induced transients. To improve the calculation speed, a split augmented Lagrangian shrinkage method is used to obtain optimal sparse coefficients. With the proposed method, both the moments of impact occurrence and the time intervals between transients can be effectively identified, and thus the impact features can be extracted. The effectiveness of the proposed method is validated by simulated signals as well as practical ATCS vibration signals. A comparison study shows that the proposed method is superior to empirical-mode decomposition, ensemble-empirical-mode decomposition and variational-mode decomposition when used for impact-feature extraction.