As a key component of motion support, the rolling bearing is currently a popular research topic for accurate diagnosis of bearing faults and prediction of remaining bearing life. However, most existing methods still have difficulties in learning representative features from the raw data. In this paper, the Xi’an Jiaotong University (XJTU-SY) rolling bearing dataset is taken as the research object, and a deep learning technique is applied to carry out the bearing fault diagnosis research. The root mean square (RMS), kurtosis, and sum of frequency energy per unit acquisition period of the short-time Fourier transform are used as health factor indicators to divide the whole life cycle of bearings into two phases: the health phase and the fault phase. This division not only expands the bearing dataset but also improves the fault diagnosis efficiency. The Deep Convolutional Neural Networks with Wide First-layer Kernels (WDCNN) network model is improved by introducing multi-scale large convolutional kernels and Gate Recurrent Unit (GRU) networks. The bearing signals with classified health states are trained and tested, and the training and testing process is visualized, then finally the experimental validation is performed for four failure locations in the dataset. The experimental results show that the proposed network model has excellent fault diagnosis and noise immunity, and can achieve the diagnosis of bearing faults under complex working conditions, with greater diagnostic accuracy and efficiency.

To solve the problem of poor performance of the target detection algorithm and false detection in the detection of paint surface defects of office chairs five-star feet, we propose a defect detection method based on the improved YOLOv3 algorithm. Firstly, a new feature fusion structure is designed to reduce the missed detection rate of small targets. Then we used the CIOU loss function to improve the positioning accuracy. At the same time, a parallel version of the k-means++ initialization algorithm (K-means||) is used to optimize and determine the parameters of the a priori anchor so as to improve the matching degree between the a priori anchor and the feature layer. We constructed a dataset of paint surface defects on the five-star feet of office chairs and performed optimization training, and used multiple algorithms and different datasets to conduct comparative experiments to validate the algorithm. The experimental results show that the improved YOLOv3 algorithm is effective in that the average precision on the self-made dataset reaches 88.3%, which is 5.8% higher than the original algorithm. At the same time, it has also been verified based on the Aliyun Tianchi competition aluminum dataset, and the average precision has reached 89.2%. This method realizes the real-time detection of the paint surface defects of the five-star feet of the office chair very well.

Piezoelectric positioning stage is the core component of precision positioning system and advanced instrument. Its hysteresis characteristics, especially rate-dependent characteristics, are the main factors affecting the positioning or control accuracy. The multi-slope Prandtl–Ishlinskii (MPI) based hysteresis modeling and compensation experiments of two-dimensional piezoelectric positioning stage are discussed. The impact of the driving voltage amplitude and frequency on the hysteresis characteristics of uniaxial piezoelectric actuator in the piezoelectric positioning stage are studied, especially the influence of variable-frequency voltages on the output displacement of a piezoelectric actuator. The MPI compensation control of two-dimensional piezoelectric positioning stage is carried out, and the fitting coefficient R² is proposed to evaluate the hysteresis compensation accuracy of MPI model. Under the full range driving voltage of 20~120 V, the fitting coefficient reaches more than 99.6%. The experiments of feedforward compensation and feedback compensation are implemented. Having applied the composite triangular-wave signal, the average absolute displacement error of the piezoelectric actuator is 0.1192 μm, as well as the mean square error 0.2949 μm. It demonstrates that the MPI model is effective against hysteresis for two-dimensional piezoelectric positioning stage.

In view of the phenomenon that camera imaging will appear large up close and small from afar in the eye-to-hand hand-calibration system, one hand–eye calibration is carried out. The manipulator is only suitable for grasping objects of the same height, and the calibration results cannot be applied to grasping products with variable height. Based on the study of the pinhole camera model and the rigid transformation model between coordinate systems, the introduction of the calibration height parameters, the relationship between parameters of the rigid transformation matrix between image the coordinate system and the robot coordinate system, and sampling height are established. In the experiment, firstly, through the calibration of camera parameters, the influence of camera distortion on imaging quality is eliminated, and the influence of calibration height is ignored. Then, the machine coordinate system and image coordinate system of the calibration plate at different heights are calibrated using the four-point calibration method. The parameters of the rigid transformation matrix at different heights (H) are calculated. Finally, through experimental analysis, the high linear relationship between the parameters of the rigid transformation matrix from the image coordinate system to the robot coordinate system and the calibration height is fitted. By analyzing the random error of the experiment, the linear relationship between calibration height and pixel density is further established, and the systematic error of the experimental process is deeply analyzed. The experimental results show that the hand–eye calibration system based on this linear relationship is precise and suitable for grabbing products of any height, and the positioning error is less than 0.08%.

Intelligent manufacturing has a strong role in promoting the transformation and upgrading of traditional industries such as the chair industry. This study aimed to accurately evaluate the production status and technical level of chair industry enterprises, and then better guide chair industry enterprises to gradually implement intelligent manufacturing. Based on the analytic network process (ANP), we propose four capability domains, nine capability sub-domains, and 21 evaluation elements, thereby constructing an evaluation model for the capability maturity of chair industry enterprises’ intelligent manufacturing. First, the weight relationship of each index of the model was determined by means of an expert questionnaire. Then, super decision software was used to complete the modeling of the evaluation index of the network analytic hierarchy process. Finally, the evaluation model of the intelligent manufacturing maturity of chair industry enterprises was applied to 50 chair industry enterprises for evaluation and verification, and the evaluation results of the model proposed in this paper were compared with the evaluation results of the intelligent manufacturing maturity model released by China’s national standards. The results show that the evaluation model constructed in this study can better reflect the development status and overall technical level of intelligent manufacturing in the chair industry. Furthermore, the evaluation results can provide decision-making suggestions for chair industry enterprises to identify important areas for improvement and implementation of intelligent manufacturing upgrade plans.