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.

Residual stresses (RS) in hot forging severely degrade the machining accuracy and stability of super alloy parts. This is the main reason for deformation during subsequent mechanical machining. RS need recognition, as well as the microstructure and properties achieved by forging. In this study, a simulation and experimental research on the single-pass compression of GH4169 are presented. RS variations with forging temperature, loading speed, and cooling speed are established by finite element (FE) simulation. Based on the FE results, an experiment is conducted at a temperature of 1020$℃$, loading speed of 25 mm/s, and press amount of 16 mm, immediately followed by water cooling. A new layer-stripping method is put forward for the high-efficiency measurement and correction of interior RS. Compared with the traditional strain gauge layer-stripping method, the measurement efficiency of the new layer-stripping method is increased by 10 times. Meanwhile, grain photographs are collected and grain size evolution is summarized; thus, the RS is characterized and evaluated from the angle of grains. It is demonstrated that the RS level rises with the increase in forging temperature, loading speed, and cooling speed, while the cooling method influences both the stress value and distribution. Compressive RS changes to tensile, while the average grain size reduces from the surfaces to the center. In the compressive regions, stress values share the same rules as grain size, while, in the tensile regions, they are contrary. The RS levels are divided according to the grain degree standard. According to the residual stress and grain distribution law of the blank, the optimal position of the part in the blank can be determined. Compared with the center position of the part in the blank, the residual stress of the part is reduced by 70%. The results provide useful strategies for the better design of forging technology, qualification examinations, and subsequent mechanical machining.

Transverse tensile strength of unidirectional (UD) composites plays a key role in overall failure of fiber-reinforced composites. To predict this strength by micromechanics, calculation of actual stress in constituent matrix is essentially required. However, traditional micromechanics models can only give the volume-averaged homogenized stress rather than an actual one for a matrix, which in practice will cause large errors. In this paper, considering the effect of stress concentration on a matrix, a novel micromechanics method was proposed to give an accurate calculation of the actual stress in the matrix for UD composite under transverse tension. A stress concentration factor for a matrix in transverse tensile direction is defined, using line-averaged pointwise stress (obtained from concentric cylinder assemblage model) divided by the homogenized quantity (obtained from a bridging model). The actual stress in matrix is then determined using applied external stress multiplied by the factor. Experimental validation on six UD carbon fiber-reinforced polymer (CFRP) specimens indicates that the predicted transverse tensile strength by the proposed method presents a minor deviation with an averaged relative error of 5.45% and thus is reasonable, contrary to the traditional method with an averaged relative error of 207.27%. Furthermore, the morphology of fracture section of the specimens was studied by scanning electron microscopy (SEM). It was observed that different scaled cracks appeared within the matrix, indicating that failure of a UD composite under transverse tension is mainly governed by matrix failure. Based on the proposed approach, the transverse tensile strength of a UD composite can be accurately predicted.

Difficult laryngoscopy is associated with airway injury, and asphyxia. There are no guidelines or gold standards for detecting difficult laryngoscopy. There are many opinions on which predictors to use to detect difficult laryngoscopy exposure, and no comprehensively unified comparative analysis has been conducted. The efficacy and accuracy of deep learning (DL)-based models and machine learning (ML)-based models for predicting difficult laryngoscopy need to be evaluated and compared, under the circumstance that the flourishing of deep neural networks (DNN) has increasingly left ML less concentrated and uncreative. For the first time, the performance of difficult laryngoscopy prediction for a dataset of 671 patients, under single index and integrated multiple indicators was consistently verified under seven ML-based models and four DL-based approaches. The top dog was a simple traditional machine learning model, Naïve Bayes, outperforming DL-based models, the best test accuracy is 86.6%, the F1 score is 0.908, and the average precision score is 0.837. Three radiological variables of difficult laryngoscopy were all valuable separately and combinedly and the ranking was presented. There is no significant difference in performance among the three radiological indicators individually (83.06% vs. 83.20% vs. 83.33%) and comprehensively (83.74%), suggesting that anesthesiologists can flexibly choose appropriate measurement indicators according to the actual situation to predict difficult laryngoscopy. Adaptive spatial interaction was imposed to the model to boost the performance of difficult laryngoscopy prediction with preoperative cervical spine X-ray.