Search

Digital Twin (DT) has been considered one of the most promising technologies promoting the development of the industry and has successfully achieved significant applications in many fields including national defense, intelligent manufacturing, smart cities, and so on. In addition, DT is gradually used in the research of automobiles which are necessary means of transportation in people's life. A systematic review of the definitions of DT and its applications in automotive-related fields reported in the literature is presented in this paper, with the overall goal to summarize almost all the related research achievements and stimulate innovative thinking. The digital twin, its structure, and its evolution are systematically reviewed. And all reviewed automotive applications of DT are divided into three categories: automotive industry, transportation, and batteries which is the research hotspot in the automotive field. Besides, these methods and ideas will be emphasized in this paper. Particularly, future development directions of digital twins in the automotive field are highlighted.

ObjectivesThis paper aims to explore new methods for enhancing the abnormal data processing of real ships in inland rivers, improving data comprehension and assisting in ship behavior recognition research.MethodsBy constructing a navigation logic level, the time series data is divided to obtain the semantic label of the ship behavior. A navigation logic visualization analysis system is designed on the basis of semantic labels, and the navigation status of the ship is combined with data visualization to assist in analyzing data problems and studying ship characteristics. Relying on a digital waterway, the data of working ships with complex behavior in an inland waterway is selected for example-based testing, and the system is used to analyze abnormal data and conduct research on ship behavior.ResultsThrough the interactive visualization of navigation logic, the causes and characteristics of abnormal data with position jumping can be effectively determined, thereby enhancing abnormal data processing. In addition, the qualitative analysis of features and quantitative analysis of thresholds effectively divides the berthing and direct sailing status data, further enriching the semantic labels of ship behavior. Conclusions The visual analysis system designed with semantic labels of ship behavior proposed herein improves data comprehensibility through free human-computer interaction. It can enhance abnormal data analysis and processing, assist in ship behavior recognition research and provide new research tools for data analysts.

The rapid development of the industrial internet had caused widespread concern about the network security, and the end-user authentication technology was considered a research hotspot.According to the characteristics of human-computer interaction in industrial internet, an experimental website was designed.24 users' mouse behavior data in an uncontrolled environment were collected within 2.5 years to conduct case studies.Hilbert-Huang transform (HHT) was used to extract frequency domain features of mouse behavior signals, combined with time domain features to form a time-frequency joint domain feature matrix of 163-dimensional to characterize user mouse behavior patterns.Bagged tree, support vector machine (SVM), Boost tree and K-nearest neighbor (KNN) were used to build a user authentication model， and the comparison result showed that the Bagged tree had the best internal detection effect in this case, with an average false acceptance rate (FAR) of 0.12% and an average false rejection rate (FRR) of 0.28%.In external detection, the FAR was 1.47%.Compared with the traditional mouse dynamics method, the frequency domain information of mouse behavior extracted by HHT can better realize the user authentication, and provide technical support the security of the industrial internet.

Micro-tools comprising difficult-to-machine materials have seen widespread application in micro-manufacturing to satisfy the demands of micro-part processing and micro-device development. Taking micro-shafts as an example, the related developmental technology, based on wire electric discharge grinding (WEDG) as the core method, is one of the key technologies used to prepare high-precision micro-shafts. To enable efficient and high-precision machining of micro-shafts with target diameters, instead of performing multiple repeated on-machine measurements and reprocessing, a geometric constraint strategy is proposed based on the previously introduced twin-mirroring-wire tangential feed electrical discharge grinding (TMTF-WEDG). This strategy encompasses the tool setting method, tangential feed distance compensation, and an equation that establishes the relationship between tangential distance and diameter variation. These components are derived from a key points analysis of the geometric constraints. The micro-shafts with diameters of 50 µm and consistencies of ±1.5 µm are repeatedly processed. A series of micro-shafts with diameters ranging from 30 µm to 120 µm achieve geometric constraints with a diameter accuracy of ±2 µm, accompanied by the complete continuous automation of the entire process. Accordingly, it can be concluded that the geometric constraint strategy is flexible and stable and can be controlled with high precision in the TMTF-WEDG process.

Abstract Vertical picking method is a predominate method used to harvest cotton crop. However, a vertical picking method may cause spindle bending of the cotton picker if spindles collide with stones on the cotton field. Thus, how to realize a precise height control of the cotton picker is a crucial issue to be solved. The objective of this study is to design a height control system to avoid the collision. To design it, the mathematical models are established first. Then a multi-objective optimization model represented by structure parameters and control parameters is proposed to take the pressure of chamber without piston, response time and displacement error of the height control system as the optimization objectives. An integrated optimization approach that combines optimization via simulation, particle swarm optimization and simulated annealing is proposed to solve the model. Simulation and experimental test results show that the proposed integrated optimization approach can not only reduce the pressure of chamber without piston, but also decrease the response time and displacement error of the height control system.

Permanent magnet synchronous motors (PMSM) have been substantially used in electric vehicles (EVs) due to their advantages such as low loss, large torque, and high power density. With the continuous improvement of the PMSM performance requirements, its heat dissipation has also attracted increasing attention. This paper proposes a cooling system to realize the heat dissipation of the motor through internal oil circulation and external water circulation. Meanwhile, to obtain the best cooling system parameters, an optimization framework is developed for heat dissipation optimization of the motor. First, the most suitable Latin hypercube sampling (LHS) method is selected for sampling through the Coordinates exchange algorithm. Second, we separately study the modeling accuracy of thirteen surrogate models and finally select the back propagation (BP) neural network model. Then, we use six multi-objective optimization algorithms (MOOAs) to optimize the model, and select the optimal solution via the utopian point method. Finally, the motor heat dissipation situation is effectively improved, and the effectiveness and reliability of the optimization framework are proved, which provides an alternative mean for the heat dissipation design optimization of the motor and has prominent practical significance.

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. However, the stochastic natures inherent in the remanufacturing processes complicate its scheduling. This paper undertakes the challenge and presents a remanufacturing job shop scheduling approach by integrating alternative routing assignment and machine resource dispatching. A colored timed Petri net is introduced to model the dynamics of remanufacturing process, such as various process routings, uncertain operation times for cores, and machine resource conflicts. With the color attributes in Petri nets, two types of decision points, recovery routing selection and resource dispatching, are introduced and linked with places in CTPN model. With time attributes in Petri nets, the temporal aspect of recovery operations for cores as well as the evolution dynamics in cores' operational stages is mathematically analyzed. A hybrid meta-heuristic algorithm embedded scheduling strategy over CTPN is proposed to search for the optimal recovery routings for worn cores and their recovery operation sequences on workstations, in minimizing the total production cost. The approach is demonstrated through the remanufacturing of used machine tool and its effectiveness is compared against another two cases: baseline case with fixed recovery process routings and case 2 using standard SA/MST.

Magnesium oxychloride cement (MOC), a type of special construction material, has drawn much research attention in solid waste utilization and environmental protection due to its eco-friendly production. Ammonia soda residue (ASR), a by-product generated from sodium carbonate manufacturing, is one of the industrial wastes that can be recycled in MOC systems. However, ASR exhibits adverse effects on the fresh performance and volume stability of MOC pastes. This paper aims at improving the properties of ASR-MOC by introducing fly ash (FA), solid waste from the power industry. Firstly, the roles of FA in MOC pastes are evaluated and analyzed. Then, three substitution ratios of FA (33.3%, 50% and 66.7% in weight) for ASR are designed for MOC pastes with 10% to 40% industrial wastes. Flowability, setting, strength and expansion of all mixtures were experimentally studied. Furthermore, X-ray diffraction (XRD) and scanning electron microscope (SEM) approaches were adopted to illustrate the microstructure changes. Results show that by adding different amounts of FA, the inferior flowability of MOC caused by ASR can be improved by 6–23%, the setting process can be prolonged by 30–55% and the expansion ratio can be reduced by 14–66%. The intensity of characteristic peaks of 5-phase and Mg(OH)2, together with the degrees of crystallization in XRD curves, well explain the strength variation and volume stability of ASR-MOC pastes. According to the regulation of relative specification, up to 20% of solid wastes in weight (10% FA + 10% ASR) can be consumed, contributing greatly to the greener sustainable development of construction materials.

Remanufacturing is a practice of growing importance due to increasing environmental awareness and regulations. Facility layout design, as the cornerstone of effective facility planning, is concerned about resource localization for a well-coordinated workflow that leads to lower material handling costs and reduced lead times. However, due to stochastic returns of used products/components and their uncontrollable quality conditions, the remanufacturing process exhibits a high level of uncertainty challenging the facility layout design for remanufacturing. This paper undertakes this problem and presents an optimization method for remanufacturing dynamic facility layout with variable process capacities, unequal processing cells, and intercell material handling. A dynamic multirow layout model is presented for layout optimization and a modified simulated annealing heuristic is proposed toward the determination of optimal layout schemes. The approach is demonstrated through a machine tool remanufacturing system.