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As a high-performance engineering plastic, polyimide (PI) is widely used in the aerospace, electronics and automotive industries. This paper aims to review the latest progress in the tribological properties of PI-based composites, especially the effects of nanofiller selection, composite structure design and material modification on the tribological and mechanical properties of PI-matrix composites.

The preparation technology of PI and its composites is introduced and the effects of carbon nanotubes (CNTs), carbon fibers (CFs), graphene and its derivatives on the mechanical and tribological properties of PI-based composites are discussed. The effects of different nanofillers on tensile strength, tensile modulus, coefficient of friction and wear rate of PI-based composites are compared.

CNTs can serve as the strengthening and lubricating phase of PI, whereas CFs can significantly enhance the mechanical properties of the matrix. Two-dimensional graphene and its derivatives have a high modulus of elasticity and self-lubricating properties, making them ideal nanofillers to improve the lubrication performance of PI. In addition, copolymerization can improve the fracture toughness and impact resistance of PI, thereby enhancing its mechanical properties.

The mechanical and tribological properties of PI matrix composites vary depending on the nanofiller. Compared with nanofibers and nanoparticles, layered reinforcements can better improve the friction properties of PI composites. The synergistic effect of different composite fillers will become an important research system in the field of tribology in the future.

The purpose of this study is to design carbon electrode materials for high performance electric double-layer capacitors (EDLCs) with pores that are large enough and have suitable pore size distribution for the electrolyte to access completely to improve EDLCs’ electrochemical performance.

This study develop an improved traditional KOH activation method, and a series of micro-meso hierarchical porous carbons have been successfully prepared from phenol formaldehyde resin by combining polyethylene glycol (PEG) and conventional KOH activation.

As evidenced by N₂ adsorption/desorption tests, the obtained samples present Types IV and I-IV hybrid shape isotherms compared with KOH-activated resin (typical of Type I). The sample AC2-7-1, which the addition quantity of PEG is 25 per cent PF (weight ration) activated at 700? For 1 h is considered as the optimum preparation condition. It exhibits the highest specific capacitance value of 240 F/g in 30 wt% KOH aqueous electrolytes because of its higher specific surface area (2085 m2/g), greater pore volume (1.08 cm3/g) and the maximum mesoporosity (43 per cent). In addition, the capacity decay of this material is only 3.1 per cent after 1000 cycles.

The materials that are rich in micropores and mesopores show great potential in EDLC capacitors, particularly for applications where high power output and good high-frequency capacitive performances are required.

An effective machine vision-based method for micro-crack detection of solar cell can economically improve the qualified rate of solar cells. However, how to extract features which have strong generalization and data representation ability at the same time is still an open problem for machine vision-based methods.

A micro-crack detection method based on adaptive deep features and visual saliency is proposed in this paper. The proposed method can adaptively extract deep features from the input image without any supervised training. Furthermore, considering the fact that micro-cracks can obviously attract visual attention when people look at the solar cell’s surface, the visual saliency is also introduced for the micro-crack detection.

Comprehensive evaluations are implemented on two existing data sets, where subjective experimental results show that most of the micro-cracks can be detected, and the objective experimental results show that the method proposed in this study has better performance in detecting precision.

First, an adaptive deep features extraction scheme without any supervised training is proposed for micro-crack detection. Second, the visual saliency is introduced for micro-crack detection.

This paper aims to investigate the effects of inhomogeneities on the rolling contact fatigue (RCF) life in elastohydrodynamically lubricated (EHL) point contacts.

A numerical model for predicting the RCF life of inhomogeneous materials in EHL contacts was established by combining the EHL model and the inclusion model through the eigen-displacement and then connecting to the RCF life model through the subsurface stresses. Effects of the type, size, location and orientation of a single inhomogeneity and the distribution of multiple inhomogeneities on the RCF life were investigated.

The RCF life of a half-space containing manganese sulfide (MnS) inhomogeneity or the mixed inhomogeneity of aluminium oxide (Al₂O₃) and calcium oxide (CaO) was longer than that for the case of Al₂O₃ inhomogeneity. For a single ellipsoidal MnS inhomogeneity, increases of its semi-axis length and decreases of its horizontal distance between the inhomogeneity and the contact center shortened the RCF life. Furthermore, the relationship between the depth of a single MnS inhomogeneity and the RCF life was found. For the half-space containing multiple inhomogeneitites, the RCF life decreased remarkably compared with the homogeneous half-space and showed discreteness.

This paper implements the prediction of the RCF life of inhomogeneous materials under EHL condition.

This paper aims to improve the generalization capability of feature extraction scheme by introducing a micro-cracks detection method based on self-learning features. Micro-cracks detection of multicrystalline solar cell surface based on machine vision is fast, economical, intelligent and easier for on-line detection. However, the generalization capability of feature extraction scheme adopted by existed methods is limited, which has become an obstacle for further improving the detection accuracy.

A novel micro-cracks detection method based on self-learning features and low-rank matrix recovery is proposed in this paper. First, the input image is preprocessed to suppress the noises and remove the busbars and fingers. Second, a self-learning feature extraction scheme in which the feature extraction templates are changed along with the input image is introduced. Third, the low-rank matrix recovery is applied to the decomposition of self-learning feature matrix for obtaining the preliminary detection result. Fourth, the preliminary detection result is optimized by incorporating the superpixel segmentation. Finally, the optimized result is further fine-tuned by morphological postprocessing.

Comprehensive evaluations are implemented on a data set which includes 120 testing images and corresponding human-annotated ground truth. Specifically, subjective evaluations show that the shape of detected micro-cracks is similar to the ground truth, and objective evaluations demonstrate that the proposed method has a high detection accuracy.

First, a self-learning feature extraction method which has good generalization capability is proposed. Second, the low-rank matrix recovery is combined with superpixel segmentation for locating the defective regions.

The purpose of this study is to investigate the influence mechanism of different interface component surface textures on the ultrasonic motor (USM) output performance.

The energy transmission mechanism of the traveling-wave ultrasonic motor 60 (TRUM-60) was numerically and experimentally investigated by fabricating dimple textures with different feature types on the friction material and the stator.

Textured friction material can increase the contact range effectively, and thus, can improve the friction characteristics of the interface and the output performance of the TRUM-60. The experimental results verified the expected influence mechanism and demonstrated that the use of either a textured friction material or stator has a very different effect on USM output performance. A textured PI-based friction material improved the TRUM-60 output performance, resulting in a maximum energy conversion efficiency of 57.11%. However, a textured stator degraded the TRUM-60 output performance, resulting in a minimum energy conversion efficiency of only 44.92%.

The results of this study provide a theoretical foundation for improved USM designs with textured interfaces.

This paper aims to predict and evaluate the wear rate of TiAl-2 Wt.% MoO₃ tabular crystals (TMCs) using the Newton interpolation methods.

The friction and wear behaviors of TMC were examined using pin-on-disc apparatus at different times, namely, 1,200, 2,400, 3,600, 4,800 and 6,000 s. The wear rates of five different times as interpolation nodes were measured and calculated by electron probe microanalysis (EMPA) and field emission electron microscope (FESEM). Then, the prediction formula of wear rate was constructed using the Newton interpolation method. The accuracy of the prediction formula and the relationship with friction layer and worn surface are verified for evaluating the reliability of the prediction formula.

The prediction formula shows a similar variation trend of TMC as the experimental results, indicating that the prediction formula can forecast the wear rate and working condition of TMC. Moreover, the microstructures of friction layer and worn surface also have a strong impact on the prediction formulas.

The prediction formulas of the Newton interpolation polynomial can be adopted to predict working longevity in the mechanical components, which can guide the practical engineering application in industrial fields.

This paper aims to propose a real-time augmented reality (AR)-based assembly assistance system using a coarse-to-fine marker-less tracking strategy. The system automatically adapts to tracking requirement when the topological structure of the assembly changes after each assembly step.

The prototype system’s process can be divided into two stages: the offline preparation stage and online execution stage. In the offline preparation stage, planning results (assembly sequence, parts position, rotation, etc.) and image features [gradient and oriented FAST and rotated BRIEF (ORB)features] are extracted automatically from the assembly planning process. In the online execution stage, too, image features are extracted and matched with those generated offline to compute the camera pose, and planning results stored in XML files are parsed to generate the assembly instructions for manipulators. In the prototype system, the working range of template matching algorithm, LINE-MOD, is first extended by using depth information; then, a fast and robust marker-less tracker that combines the modified LINE-MOD algorithm and ORB tracker is designed to update the camera pose continuously. Furthermore, to track the camera pose stably, a tracking strategy according to the characteristic of assembly is presented herein.

The tracking accuracy and time of the proposed marker-less tracking approach were evaluated, and the results showed that the tracking method could run at 30 fps and the position and pose tracking accuracy was slightly superior to ARToolKit.

The main contributions of this work are as follows: First, the authors present a coarse-to-fine marker-less tracking method that uses modified state-of-the-art template matching algorithm, LINE-MOD, to find the coarse camera pose. Then, a feature point tracker ORB is activated to calculate the accurate camera pose. The whole tracking pipeline needs, on average, 24.35 ms for each frame, which can satisfy the real-time requirement for AR assembly. On basis of this algorithm, the authors present a generic tracking strategy according to the characteristics of the assembly and develop a generic AR-based assembly assistance platform. Second, the authors present a feature point mismatch-eliminating rule based on the orientation vector. By obtaining stable matching feature points, the proposed system can achieve accurate tracking results. The evaluation of the camera position and pose tracking accuracy result show that the study’s method is slightly superior to ARToolKit markers.

The purpose of this paper is to provide a knowledge-enabled digital twin for smart design (KDT-SD) of aircraft assembly line (AAL) to enhance the AAL efficiency, performance and visibility. Modern AALs usually need to have capabilities such as digital-physical interaction and self-evaluation that brings significant challenges to traditional design method for AAL. The digital twin (DT) combining with reusable knowledge, as the key technologies in this framework, is introduced to promote the design process by configuring, understanding and evaluating design scheme.

The proposed KDT-SD framework is designed with the introduction of DT and knowledge. First, dynamic design knowledge library (DDK-Lib) is established which could support the various activities of DT in the entire design process. Then, the knowledge-driven digital AAL modeling method is proposed. At last, knowledge-based smart evaluation is used to understand and identify the design flaws, which could further improvement of the design scheme.

By means of the KDT-SD framework proposed, it is possible to apply DT to reduce the complexity and discover design flaws in AAL design. Moreover, the knowledge equips DT with the capacities of rapid modeling and smart evaluation that improve design efficiency and quality.

The proposed KDT-SD framework can provide efficient design of AAL and evaluate the design performance in advance so that the feasibility of design scheme can be improved as much as possible.

The purpose of this paper is to investigate the effect of laser surface texturing on the tribological properties of polyimide composites and the output performance of traveling wave rotary ultrasonic motor.

The surface texturing on polyimide composites specimens were fabricated by laser ablation process of different dimple densities, and then the tribological properties were tested by a flat-on-flat tribometer under dry conditions. Finally, the output performance of the traveling wave rotary ultrasonic motor was tested to verify the effectiveness of dimples surface texturing.

The results show that surface texturing can greatly enhance the friction coefficient of contact interface, especially the specimen with a dimple density of 7.06 per cent exhibited the highest friction coefficient among the specimens. When the input voltage is 500 V, the output power, locked-rotor torque and output torque of ultrasonic motor with textured PI of 7.06 per cent dimple density as friction material at the speed of 100 r/min increased by 13.8, 19 and 12.8 per cent compared to that of the untextured PTFE, respectively. When the ultrasonic motor reverses, the output performance is increased by 20.9, 40.3 and 17.7 per cent, respectively.

Surface texturing is an effective way to improve the friction behavior of polyimide composites and then correspondingly enhance the energy conversion efficiency and output performance of the traveling wave rotary ultrasonic motor.