There are lots of general parts with frequent maintenance and high interchangeability for continuous casting machine during its operation and maintenance. To acquire the service information of each general part under the environment of high temperature and serious oil pollution for predicting remaining life and preventative maintenance, a digital twin-driven monitoring and traceability system for general parts in continuous casting machine is proposed. First, the systematic architecture of proposed approach is given in detail, where a typical five-layer model for digital twin-driven monitoring and traceability is established. Second, the Web-based 3D visualization monitoring for continuous casting machine is achieved by using lightweight 3D twin model. After that, an assembly model based on polychromatic sets is built for expressing and describing the assembly relationships and assembly process of general parts. Meanwhile, an encoding rule for part and position is put forward by considering assembly position information. On the basis of that, the general parts with total process information are traced in the service life cycle. Finally, digital twin-based 3D visualization monitoring and traceability prototype system is developed and implemented, where the experiment cases verify the effectiveness and feasibility of the proposed approach.

Purpose: The purpose of this paper is to find a method for key assembly structure identification in complex mechanical assembly. Three-dimensional model reuse plays an increasingly important role in complex product design and innovative design. Assembly model has become important resource of models reuse in enterprises, which contains certain function assembly structures. These assembly structures implicating plenty of design intent and design experience knowledge can be used to support function-structure design, modular design reuse and semantics analysis for complex product. Design/methodology/approach: A method for identifying key assembly structures in assembly model is presented from the viewpoint of assembly topology and multi-source attributes. First, assembly model is represented based on complex network. Then, a two-level evaluation model is put forward to evaluate importance of parts assembled, and the key function parts in assembly can be obtained. After that, on the basis of the function parts, a heuristic algorithm upon breadth first searching is given to identify key assembly structures. Findings: The method could be used to evaluate key function parts and identify key assembly structures in complex mechanical assembly according to the specific circumstances. Practical implications: The method can not only help designers find the key assembly structure in complex mechanical assembly model, facilitate the function-structure designing and semantics analyzing, and thereby improve the efficiency of product knowledge reuse, but also assist in analyzing influence scope of key function part changing and optimization of the assembly process for complex mechanical assembly. Originality/value: The paper is the first to propose a method for key assembly structure identification in complex mechanical assembly, where the key function parts can be evaluated through a two-level evaluation model, and the key assembly structures are identified automatically based on complex network.

The vigorous development of big data technology has changed the traditional user requirement acquisition mode of the manufacturing industry. Based on data mining, manufacturing enterprises have the innovation ability to respond quickly to market changes and user requirements. However, in the stage of complex product innovation design, a large amount of design data has not been effectively used, and there are some problems of low efficiency and lack of objectivity of user survey. Therefore, this paper proposes an acquisition method of user requirements based on patent data mining. By constructing a patent data knowledge base, this method combines the Latent Dirichlet Allocation topic model and a K-means algorithm to cluster patent text data to realize the mining of key functional requirements of products. Then, the importance of demand is determined by rough set theory, and the rationality of demand is verified by user importance performance analysis. In this paper, the proposed method is explained and verified by mining the machine tool patent data in CNKI. The results show that this method can effectively improve the efficiency and accuracy of user requirements acquisition, expand the innovative design approach of existing machine tool products, and be applied to other complex product fields with strong versatility.

Conceptual design is a key link in the process of complex product design, and it is very important to select the appropriate design scheme; however, there are many types and inaccuracies of the evaluation data, and there is a problem of mutual influence between the evaluation criteria, which leads to unreliable decision making of the optimal solution. In order to solve this problem, a decision-making method based on intuitionistic fuzzy sets (IFS) and prospect theory is proposed. This method can be used for symmetric and asymmetric evaluation data. The evaluation data are classified according to different expression types and unified using intuitionistic fuzzy numbers. The intuitionistic fuzzy prospect value of decision information is calculated using prospect theory, and the prospect transformation of decision information is completed. At the same time, the Gray Relational Analysis (GRA) method and the Criteria Importance Though Intercriteria Correlation (CRITIC) method are used to calculate the subjective and objective weights of the technical and economic evaluation indexes of the product, and the combination weights are given; then, based on the evidence theory, the basic probability distribution of the evidence chain of all conceptual design schemes is synthesized, and the comprehensive prospect evaluation results of the schemes are obtained to complete the optimization of the conceptual design schemes. Finally, the effectiveness of the proposed method is verified by the conceptual design of the chip removal system of the deep hole machining machine tool. This work provides a promising method for decision makers to optimize the design scheme and provides insights into multi-objective decision-making problems.

Aggressive driving behavior is one of the main reasons for traffic crashes in China. However, how cognitive interventions affect impulsive driving behavior is still unknown. In this study, a simulated drive was constructed to evaluate the influence of different cognitive interventions. In addition to speeding behavior in limit zones, speed when passing pedestrians, at intersections, and on the whole drive was adopted as a measure to evaluate aggressive driving behaviors. Forty-eight young drivers were recruited with monetary rewards. Compared to the control group, the penalty feedback intervention significantly reduced the mean speed in the 40 km/h zone, the 80 km/h zone, and when passing pedestrians. The combined feedback intervention significantly reduced the distance ratio of speeding in the 40 km/h and 80 km/h zones, as well as the mean speed at the intersection and on the whole drive. However, the interaction effects between the driving task and intervention method were not remarkably observed during aggressive driving behaviors. The findings from this study provide evidence that different cognitive interventions contribute distinctively to improving aggressive driving behaviors. These conclusive results have possible implications for the design of vehicle warning systems and traffic safety interventions.