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The purpose of this study is to explore a new assembly process planning and execution mode to realize rapid response, reduce the labor intensity of assembly workers and improve the assembly efficiency and quality.

Based on the related concepts of digital twin, this paper studies the product assembly planning in digital space, the process execution in physical space and the interaction between digital space and physical space. The assembly process planning is simulated and verified in the digital space to generate three-dimensional visual assembly process specification documents, the implementation of the assembly process specification documents in the physical space is monitored and feed back to revise the assembly process and improve the assembly quality.

Digital twin technology enhances the quality and efficiency of assembly process planning and execution system.

It provides a new perspective for assembly process planning and execution, the architecture, connections and data acquisition approaches of the digital twin-driven framework are proposed in this paper, which is of important theoretical values. What is more, a smart assembly workbench is developed, the specific image classification algorithms are presented in detail too, which is of some industrial application values.

Negative Poisson’s ratio (NPR) material has huge potential applications in various industrial fields. However, lower Young’s modulus due to the porous form limits its further applications. Based on the topology optimization technique, this paper aims to optimize the structure consisting two isotropic porous materials with positive Poisson’s ratio (PPR) and NPR and void.

Under prescribed dual-volume fraction constraints, the structural compliance is taken as the objective. Young’s modulus and Poisson’s ratio are, respectively, interpolated and expressed with Lamé’s parameters for easier programming. Accordingly, the sensitivities can be derived through the chain rule. Several two- and three-dimensional illustrative examples are presented to demonstrate the capability and effectiveness of the proposed approach. The influences of Poisson’s ratios, volume fractions and Young’s moduli on the optimized results are investigated.

For NPR materials having unique load responses, the resulting topologies of PPR and NPR materials have distinct material distributions in comparison of the results from two PPR materials. Furthermore, it is observed that higher structural stiffness can be achieved from the hybrid of PPR and NPR materials than that obtained from the structures made of individual constituent materials.

A topology optimization methodology is proposed to design structures composed of PPR and NPR materials.

The purpose of this study is to achieve multi-variety and small-batch assembly through direct cooperation between equipment and people and to improve assembly efficiency as well as flexibility.

Firstly, the concept of the human–computer interaction is designed. Secondly, the machine vision technology is studied theoretically. Skin color filter based on hue, saturation and value color model is put forward to screen out images that meet the skin color characteristics of the worker, and a multi-Gaussian weighted model is built to separate moving objects from its background. Both of them are combined to obtain the final images of the target objects. Then, the key technology is applied to the smart assembly workbench. Finally, experiments are conducted to evaluate the role of the human–computer interaction features in improving productivity for the smart assembly workbench.

The result shows that multi-variety and small-batch considerable increases assembly time and the developed human–computer interaction features, including prompting and introduction, effectively decrease assembly time.

This study proves that the machine vision technology studied in this paper can effectively eliminate the interferences of the environment to obtain the target image. By adopting the human–computer interaction features, including prompting and introduction, the efficiency of manual operation is improved greatly, especially for multi-variety and small-batch assembly.

This paper aims to clarify the complex path of negative externalities in the sharing economy and proposes corresponding policy recommendations.

This paper aims to establish an analytical framework for the negative externalities of the sharing economy and to extract the main factors that produce negative externalities, and then, through qualitative comparative analysis method find out how these factors interact to form a negative externality.

Negative externalities in the sharing economy come from the joint effect of the sharing degree of the product or service and constraint mechanism, and the current main modes of the shared economy increase the possibility of negative externalities.

The paper proposes a complex path resulting from negative externalities in the shared economy.

The purpose of this paper is to fill the knowledge gap in the microscopic origin of high corrosion resistance in the passivated 316 L stainless steel.

Here, the pitting corrosion potential of the passivated 316 L stainless steel is measured, as well as the non-passivated one. Using the aberration-corrected scanning transmission electron microscopy, the microstructure of the passive film is unambiguously revealed. Combining the electron energy loss spectroscopy with the X-ray photoelectron spectroscopy, the depth profiling analysis is conducted and the variations in composition from the very surface of the passive film to the internal steel are clarified.

By optimizing the passivation treatment process, the authors significantly increase the pitting corrosion potential of the passivated 316 L stainless steel by 300 mV, compared with the non-passivated one. The passive film features a unique amorphous multilayer structure. On the basis of the depth profiling analysis, the origin of the high corrosion resistance achieved is unraveled, in which the redistribution of elements in the multilayer passive film, especially the enrichment of Cr in the topmost layer and Ni at the film-metal interface, prevent the oxidization of the inner iron of the steel.

This study advances understanding of the nature of the passive film from a microscopic view, which can be helpful for the further improvement of the corrosion resistance performance.

This study introduces a model for the multilayer structure of passive films that reveals the reconstitution of the passive films after the opportune passivation treatments. Due to the redistribution of elements caused by passivation, the enrichment of Cr in the outer layer and Ni near the film-metal interface leads to enhance corrosion resistance performance.

Due to the miniaturization of electronic devices, the increased current density through solder joints leads to the occurrence of electromigration failure, thereby reducing the reliability of electronic devices. The purpose of this study is to propose a finite element-artificial neural network method for the prediction of temperature and current density of solder joints, and thus provide reference information for the reliability evaluation of solder joints.

The temperature distribution and current density distribution of the interconnect structure of electronic devices were investigated through finite element simulations. During the experimental process, the actual temperature of the solder joints was measured and was used to optimize the finite element model. A large amount of simulation data was obtained to analyze the neural network by varying the height of solder joints, the diameter of solder pads and the magnitude of current loads. The constructed neural network was trained, tested and optimized using this data.

Based on the finite element simulation results, the current is more concentrated in the corners of the solder joints, generating a significant amount of Joule heating, which leads to localized temperature rise. The constructed neural network is trained, tested and optimized using the simulation results. The ANN 1, used for predicting solder joint temperature, achieves a prediction accuracy of 96.9%, while the ANN 2, used for predicting solder joint current density, achieves a prediction accuracy of 93.4%.

The proposed method can effectively improve the estimation efficiency of temperature and current density in the packaging structure. This method prevails in the field of packaging, and other factors that affect the thermal, mechanical and electrical properties of the packaging structure can be introduced into the model.

The purpose of this paper is to present state-of-the-art approaches for precise operation of a robotic manipulator on a macro- to micro/nanoscale.

This paper first briefly discussed fundamental issues associated with precise operation of a robotic manipulator on a macro- to micro/nanoscale. Second, this paper described and compared the characteristics of basic components (i.e. mechanical parts, actuators, sensors and control algorithm) of the robotic manipulator. Specifically, commonly used mechanisms of the manipulator were classified and analyzed. In addition, intuitive meaning and applications of its actuator explained and compared in details. Moreover, related research studies on general control algorithm and visual control that are used in a robotic manipulator to achieve precise operation have also been discussed.

Remarkable achievements in dexterous mechanical design, excellent actuators, accurate perception, optimized control algorithms, etc., have been made in precise operations of a robotic manipulator. Precise operation is critical for dealing with objects which need to be manufactured, modified and assembled. The operational accuracy is directly affected by the performance of mechanical design, actuators, sensors and control algorithms. Therefore, this paper provides a categorization showing the fundamental concepts and applications of these characteristics.

This paper presents a categorization of the mechanical design, actuators, sensors and control algorithms of robotic manipulators in the macro- to micro/nanofield for precise operation.

As a typical resource energy-intensive industry, the scale of construction industry has been expanding rapidly owing to the large-scale urbanization and the economic booming in China, which results in a sharp increase in the energy consumption of construction industry. However, it is infeasible to mitigate the energy consumption by reducing the production activities of construction industry. Therefore, improving the energy efficiency of construction industry is essential for energy saving. Construction industry has close relationships with other industries. The production activities have not only consumed a great deal of energy but they have also generated a massive energy consumption from other industries. Previous literature studied the efficiency of energy consumed directly by the construction industry. However, no research has been found focusing on the efficiency of energy consumed directly by the construction industry and indirectly by the related industries. The purpose of this paper is to put forward a total energy efficiency evaluation framework to measure the energy efficiency of construction industry in depth.

This paper employs the data envelopment analysis (DEA) method and the framework of embodied energy efficiency (EEE) to establish a total energy efficiency evaluation model. Next, the comprehensive analysis of direct energy efficiency (DEE) and EEE in different provinces with various levels of urbanization and various economic levels is conducted.

The results show that the embodied energy intensity and its regularities differ greatly between provinces. From the comparison of DEE and EEE, the provinces of Zhejiang and Jiangsu remain DEA-effective and Hainan is the only province in which the EEE is higher than DEE in 2002, 2007 and 2012. Besides, the DEE and EEE in the provinces with higher levels of urbanization and high economic levels are not more effective than those in the provinces with relatively lower levels of urbanization and low economic levels.

Previous literature studied the efficiency of energy consumed directly by the construction industry while ignoring the energy consumed indirectly by the related industries. Besides, no research has been found focusing on the regulation of energy efficiency in different provinces with different levels of urbanization and different economic levels. It can be concluded that the increasing levels of urbanization and higher economic levels have not brought development and benefits for improving DEE and EEE. Therefore, under the condition that the self-regulation of construction industry and market fail to facilitate the improvement of DEE and EEE in China, policymakers should develop policies and market incentive mechanism to encourage construction industry for employing new technologies to improve the energy efficiency. Since the EEE can reveal the energy efficiency in depth, the evaluation method of EEE should be paid more attention. Besides the fact that the EEE is lower than the DEE in almost all provinces, except Hainan province, the industrial structure is essential to develop the EEE. Hence, improving the energy structure, increasing the energy efficiency and developing new and renewable energy are the basic energy strategies in China.