Plasticized poly(vinyl chloride) (PVC) gels belong to a class of electroactive polymers (EAPs), which have the ability to realize bending motion, contractile motion, extended motion and crawling motion under electric stimulation.

High-speed on / off valve (HSV) has been widely concerned in the field of hydraulic engineering, mainly focusing on improving its dynamic characteristic. However, relatively little attention is paid to the adjustment of HSV switching characteristic, which limits the applications of HSVs in some specific systems. Therefore, an advanced pulsewidth modulation (PWM) control algorithm is proposed in this article to change the dynamic behaviors of the experimental HSV and makes it adjustable in a wider scope to adapt to more applications. Applying the voltages of opening preduty ratio and holding duty ratio, the opening initial current and closing initial current can be generated, respectively. Combining with the voltages of opening duty ratio and closing duty ratio, the opening and closing dynamic behaviors can be fully controlled and adjusted in a wide range. The effectiveness of the proposed algorithm is proved by numerical analysis and experimental studies. Results show that the opening delay time can be controlled in a range of 0.2–18.3 ms and the closing delay time can be adjusted in a range of 0.1–6.8 ms, while the opening moving time can be adjusted from 1.9 to 14.1 ms and the closing moving time can be adjusted from 1.8 to 6.1 ms. Consequently, the total opening time and total closing time can be controlled in the ranges of 2.1–32.1 ms and 1.9–12.9 ms, respectively. Compared with the conventional single voltage control strategy, the advance PWM control algorithm is verified to be a promising method to improve or even adjust the dynamic behaviors of HSVs and adapt to more complex working conditions.

Defective shafts need to be classified because some defective shafts can be reworked to avoid replacement costs. Therefore, the detection and classification of shaft surface defects has important engineering application value. However, in the factory, shaft surface defect inspection and classification are done manually, with low efficiency and reliability. In this paper, a deep learning method based on convolutional neural network feature extraction is used to realize the object detection and classification of metal shaft surface defects. Through image segmentation, the system methods setting of a Fast-R-CNN object detection framework and parameter optimization settings are implemented to realize the classification of 16,384 × 4096 large image little objects. The experiment proves that the method can be applied in practical production and can also be extended to other fields of large image micro-fine defects with a high light surface. In addition, this paper proposes a method to increase the proportion of positive samples by multiple settings of IOU values and discusses the limitations of the system for defect detection.

Shot peening is a surface-strengthening process that is widely used in various industries, such as aerospace, automotive, and biomedical engineering. The process involves the impact of small, spherical media, called shots, onto the surface of a material, resulting in residual compressive stress and improved surface properties. This review aims to provide an overview of the state of the art and perspectives on surface strengthening by shot peening. The review covers various aspects of shot peening, including process parameters, shot materials, and quality control techniques. The advantages and limitations of shot peening in comparison to other surface-strengthening techniques are also discussed. The findings of this review indicate that shot peening is a versatile and effective surface-strengthening technique with numerous applications, and further research is needed to fully realize its potential. In conclusion, this review provides insights into the current status and future perspectives on surface strengthening by shot peening, and it is expected to be useful for researchers, engineers, and practitioners in the field of material science and engineering.

Robot manipulators perform a point-point task under kinematic and dynamic constraints. Due to multi-degree-of-freedom coupling characteristics, it is difficult to find a better desired trajectory. In this paper, a multi-objective trajectory planning approach based on an improved elitist non-dominated sorting genetic algorithm (INSGA-II) is proposed. Trajectory function is planned with a new composite polynomial that by combining of quintic polynomials with cubic Bezier curves. Then, an INSGA-II, by introducing three genetic operators: ranking group selection (RGS), direction-based crossover (DBX) and adaptive precision-controllable mutation (APCM), is developed to optimize travelling time and torque fluctuation. Inverted generational distance, hypervolume and optimizer overhead are selected to evaluate the convergence, diversity and computational effort of algorithms. The optimal solution is determined via fuzzy comprehensive evaluation to obtain the optimal trajectory. Taking a serial-parallel hybrid manipulator as instance, the velocity and acceleration profiles obtained using this composite polynomial are compared with those obtained using a quintic B-spline method. The effectiveness and practicability of the proposed method are verified by simulation results. This research proposes a trajectory optimization method which can offer a better solution with efficiency and stability for a point-to-point task of robot manipulators.