Due to their excellent performance, superhydrophobic materials have received a lot of attention and research in friction reduction and wear resistance. However, the effect of different contact angles of superhydrophobicity on friction and wear properties has not been thoroughly studied. In this paper, a nanosecond pulsed laser was used to realize the preparation of a superhydrophobic nickel surface, which indeed reduced the coefficient of friction but also increased the wear volume when compared to the unprocessed surface. As the contact angle of the superhydrophobic nickel surface increased, the coefficient of friction gradually increased, and the wear volume decreased gradually in superhydrophobic nickel surfaces. When the laser energy density was 1 J/cm², the contact angle was 150.3° and the minimum friction coefficient was 0.4. However, when the laser energy density was 50 J/cm², the maximum contact angle was 156.4° and the minimum wear volume was 4.23 × 10⁷ μm³. The friction direction also influenced the tribological properties of the superhydrophobic-textured surface. This method makes it possible to process superhydrophobic surfaces with more suitable friction and wear properties.

Recently, the development of the Industrial Internet of Things (IIoT) has led enterprises to re‐examine the research of the equipment‐state‐prediction models and intelligent manufacturing applications. Take industrial robots as typical example. Under the effect of scale, robot maintenance decision seriously affects the cost of spare parts and labor deployment. In this paper, an evaluation method is proposed to predict the state of robot lubricating oil based on support vector regression (SVR). It would be the proper model to avoid the structural risks and minimize the effect of small sample volume. IIoT technology is used to collect and store the valuable robot running data. The key features of the running state of the robot are extracted, and the machine learning model is applied according to the measured element contents of the lubricating oil. As a result, the cost of spare parts consumption can be saved for more than two million CNY per year.

Based on the spare parts and structure data of industrial robots, the entity list of robot parts is established to form a query dictionary, and entity annotation is performed on the robot corpus by means of dictionary query, which reduces the cost of manual annotation and ensures the quality of annotation data. In the process of entity recognition training, Bert+Bilstm+CRF model structure is used to initially use 70% of the dictionary data for annotation, and the model is trained by iteratively increasing the annotation data in a continuous cycle, so that the model can extract all the entities in the robot corpus as much as possible. In addition, the material number/model information and PM maintenance content/strategy of the entity have been used as attributes of the entity. Meanwhile, the experience summarized by the failure model and effect analysis of industrial robots is fully utilized to connect the phenomena, causes and measures through the entities in order to build the industrial robot knowledge graph relationships. The constructed knowledge graph relationship is stored in a Neo4j graphical database, making it convenient for content retrieval and inquiry of application systems.In the industrial robot knowledge graph application side, the field maintenance personnel requirements are collected through a questionnaire survey and the requirements are classified into intent. A Bert+TextCNN structure model is built to realize the intention recognition of user inquiries. By combining entity recognition models and intent classification models, the system is able to better understand user inquiry needs, leading to the implementation of an intelligent maintenance system for industrial robots.