Reliability of machine tools is critical to high-efficiency manufacturing, and the failure data of feeding systems are requisite to analyze the reliability. This paper presents a multi-axis loading device (MLD) to simulate loading condition of an operating feeding system of a machine tool in a real machining process; this could cause breakdowns of the system and collect failure data. The MLD was developed based on a six-degree-of- freedom (6-DOF) parallel mechanism, and its prototype was presented. Its kinematics was analyzed, and dynamic equations were established based on the principle of virtual work. A fuzzy proportional-integral-derivative controller was designed to build an explicit force control system, whose proportional and integral gain can be adjusted according to the errors between reference and actual forces; and a force and a velocity feedforward were integrated to improve the performance of the control system. Loading experiments were conducted on an experimental platform to validate the loading capacity of the MLD; the spindle of the platform was set to be stationary and driven to perform feeding motions with different dimensions. Results of the experiment with the stationary spindle indicate that the MLD can simultaneously provide a 3-DOF force and a 3-DOF moment with acceptable errors. Experiments with moving spindle illustrate that the MLD accomplished tracking a spindle that is executing a 3-DOF trajectory-unknown feeding motion and adding 3-DOF forces on the feeding system through the spindle. The actual output multi-axis forces have little hysteresis and errors less than 10%. Compared with the long-term machining to load the feeding system in traditional reliability tests of machine tools, the proposed MLD provides an efficient method to simulate the loading condition of the system with reduced testing costs.

A calibration method of a hexaglide parallel manipulator is presented to improve its accuracy. A prototype of the hexaglide parallel manipulator is first proposed and its kinematics is analyzed. Through differentiating kinematic equations, 54 geometric error parameters are generated to present the pose error of the moving platform, on which an iterative algorithm for the calibration is based. The experiment starts with the data acquisition. All of measuring poses are newly selected based on the orthogonal design, and the deviations in each pose are measured by a laser tracker. Subsequently, 54 actual geometric parameters are identified by least squares method and compensated to the nominal kinematic model, which is assessed by 25 configurations to obtain the accuracy of the calibrated hexaglide parallel manipulator. It is discovered that the pose errors of the calibrated hexaglide parallel manipulator are significantly reduced and illustrate the validity of the calibration method to improve its accuracy. Finally, we discussed the feasibility of implementing this method in high-accuracy calibration of variant-scale parallel mechanisms.

To fully bring into play the functions of aircraft engine blades, it is indispensable to perform regular inspections of engine blades, which currently rely on inefficient manual visual assessments. While artificial intelligence technology can be utilized, benchmark datasets are not available yet. To tackle these issues, in thiswork, we first construct two datasets that are collected from real blade defect images at different microscopic magnifications under an electron microscope and a metallographic microscope. Subsequently, we propose an efficient lightweight YOLOv8 framework, incorporating a hierarchical feature fusion module MS-Block for better multi-scale integration, as well as an Efficient Multi-Scale Attention (EMA) and Dilation-wise Residual (DWR) modules to enhance the detection of small targets and replace the loss function with Inner-IoU. The improved YOLOv8 demonstrates a noteworthy increase in mean average precision (mAP), achieving an enhancement of 1.5% on the Electron Microscope Taken (EMT) dataset and 1.8% on the Metallographic Microscope Taken (MMT) dataset compared to the original model. Our approach significantly surpasses the performance of contemporary target detection algorithms, thereby offering a robust solution for microscopic defect detection in aeroengines. This advancement not only streamlines the inspection process but also contributes to the overall safety and reliability of aircraft operations.

This paper presents an adaptive controller equipped with a stiffness estimation method for a novel material-testing machine, in order to alleviate the performance depression caused by the stiffness variance of the tested specimen. The dynamic model of the proposed machine is built using the Kane method, and kinematic model is established with a closed-form solution. The stiffness estimation method is developed based on the recursive least-squares method and the proposed stiffness equivalent matrix. Control performances of the adaptive controller are simulated in detail. The simulation results illustrate that the proposed controller can greatly improve the control performance of the target material-testing machine by online stiffness estimation and adaptive parameter tuning, especially in low-cycle fatigue (LCF) and high-cycle fatigue (HCF) tests.