Search results

Visitor center plays an important role in the normal operation and sustainable development of scenic spots, especially as a portal image of its management. This paper presents resilience theory for visitor centers to identify some common issues in designing visitor centers in China scenic spots, including the lack of function, loss of architectural characteristics, and difficultly in adapting to changes in the number of visitors with periodic variations. The framework of resilience theory was set from four dimensions, namely, resilience and match in the composition of ontology function, the extended function, integration of buildings into the surrounding environment, and alternative construction technologies and materials. This theory was explained and analyzed with the application of the theory in practice in combination with the design of Mount Hua visitor center. Results showed that resilience theory yields good application effect.

It is a challenging task to analysis oxide wear particles when they are stuck together with other types of wear particles in complex ferrography images. Hence, this paper aims to propose a method of ferrography image segmentation to analysis oxide wear debris in complex ferrography images.

First, ferrography images are segmented with watershed transform. Then, two region merging rules are proposed to improve the initial segmentation results. Finally, the features of each particle are extracted to detect and assess the oxide wear particles.

The results show that the proposed method outperforms other methods of ferrography image segmentation, and the overlapping wear particles in complex ferrography images can be well separated. Moreover, the features of each separated wear particles can be easily extracted to analysis the oxide wear particles.

The proposed method provides a useful approach for the automatic detection and assessment of oxide wear particles in complex ferrography images.

The colours, edges and position information of wear debris are considered in the proposed method to improve the segmentation result. Moreover, the proposed method can not only detect oxide wear particles in ferrography images but also evaluate oxide wear severity in ferrography images.

This study aims to investigate the synergistic friction reduction and antiwear effects of lyophilized graphene loading nano-copper (RGO/Cu) as lubricating oil additives, compared with graphene.

The friction performance of freeze-drying graphene (RGO) and RGO/Cu particles was investigated at different addition concentrations and under different conditions.

Graphene plays a synergistic friction reduction and antiwear effect because of its large specific surface area, surface folds and loading capacity on the nanoparticles. The results showed that the average friction coefficients of RGO and RGO/Cu particles were 22.9% and 6.1% lower than that of base oil and RGO oil, respectively. In addition, the widths of wear scars were 62.3% and 55.3% lower than those of RGO/Cu particles, respectively.

The RGO single agent is suitable for medium-load and high-speed conditions, while the RGO/Cu particles can perform better in the conditions of heavy load and high speed.

Facility structures in liquefied natural gas (LNG) plants require tremendous amounts of scaffolding to facilitate relevant industrial operation and maintenance. As such, the productivity of scaffolding operations in turnaround maintenance (TAM) has attracted much attention in recent years. In addition, health and safety issues have been recognised as a key contributor along with productivity improvement in the LNG industry. This study aims to integrate work posture analysis into value stream mapping to achieve an optimised and balanced improvement in both productivity and health and safety.

A case study approach is adopted to integrate lean and work posture analysis in a TAM site. The lean improvement is conducted through value stream mapping, and the work posture analysis is conducted through the Ovako Working Posture Analysis System method. A three-step optimisation strategy is then developed for achieving optimised performance in waste reduction and work posture improvement.

It is found that the implementation of value stream mapping can help eliminate waste in the installation process, therefore eliminating potential health and safety risks. However, health and safety of onsite workers does not always improve as lean implementation intensifies. There is an optimised erection schedule that has the lowest health and safety risk within a waste reduction target.

In contradiction to previous studies, which rely on qualitative assessment to identify the a positive correlation between lean and health and safety, this study reveals the distinct difference between lean attributes and health and safety attributes through a quantitative assessment and is more readily to be implemented at the site level for simultaneous improvement in lean and health and safety.

This paper aims to explore the impact of Sc element on the microstructure and corrosion properties of Mg-0.5Zn alloy.

Three kinds of Mg-0.5Zn-xSc (x = 0.1, 0.3 and 0.5 Wt.%) alloys were obtained, and the microstructure and corrosion properties were both analyzed.

As the Sc concentration increases, the corrosion resistance of the alloys initially improves and subsequently deteriorates. The trace addition of Sc can effectively reduce the grain size of Mg-Zn-Sc alloys and enhance the density of the corrosion products film. Consequently, an appropriate amount of Sc can reduce the corrosion rate of Mg-0.5Zn alloy.

However, the addition of Sc also introduces the second phase particles in the alloy, leading to galvanic corrosion, which adversely affects the corrosion resistance of Mg-0.5Zn alloy. Therefore, the amount of Sc added should be carefully controlled.

The purpose of this paper is to fill low content of silica to improve the wear resistance of graphite/polytetrafluoroethylene (PTFE) composites, and to discuss the effect of low content of silica on the morphology of terrace-like transfer film.

The tribological properties of silica/graphite/PTFE composites were tested by rotating pin-on-disk friction and wear tester, and the morphology of terrace-like transfer film was observed by 3D laser scanning microscope and quantitatively analyzed through the multi-Gaussian function linear combination model.

The results showed that adding 3 wt.% silica in graphite/PTFE composites can reduce the wear rate by 87%. More importantly, the morphology of terrace-like transfer film changed significantly after filling a small amount of silica, including the increase of the number of layers and the coverage rate of transfer film.

This work is of great significance for further understanding the transfer film and achieving its morphology control to optimize the wear of high performance PTFE composites.

Using computer technology to realize ferrographic intelligent fault diagnosis technology is fundamental research to inspect the operation of mechanical equipment. This study aims to effectively improve the technology of deep learning technology in the field of ferrographic image recognition.

This paper proposes a binocular image classification model to solve ferrographic image classification problems.

This paper creatively proposes a binocular model (BesNet model). The model presents a more extreme situation. On the one hand, the model is almost unable to identify cutting wear particles. On the other hand, the model can achieve 100% accuracy in identifying Chunky and Nonferrous wear particles. The BesNet model is a bionic model of the human eye, and the used training image is a specially processed parallax image. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The work presented in this thesis is original, except as acknowledged in the text. The material has not been submitted, either in whole or in part, for a degree at this or any other university. The BesNet model developed in this article is a brand new system for ferrographic image recognition. The BesNet model adopts a method of imitating the eyes to view ferrography images, and its image processing method is also unique. After combining the MCECNN model, it is changed to BMECNN model, and its classification accuracy has reached the highest level in the industry.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2023-0150/

Online reviews increasingly present the characteristic of bidirectional communication with the advent of Web 2.0 era and tend to be asymmetrical and individualized in linguistic information. The authors aim to develop a new linguistic conversion model that exploits the asymmetric and personalized information from online reviews to express such linguistic information. A new online recommendation approach is provided.

The necessity of new linguistic conversation model is elucidated, and a leverage factor is incorporated into the linguistic label of negative review to handle the asymmetry problems of linguistic scale. A possible value range of the leverage factor is studied. A new linguistic conversation model is accordingly established with an unbalanced linguistic label and a cloud model. The authors develop a new online recommendation approach based on several modules, such as initialization, conversion, user-clustering and recommendation models.

The unbalanced effect between negative and positive reviews is verified with real data and measured using indirect methods. A new online recommendation approach of electronic products is proposed and used as an illustrative example to prove the practicality, effectiveness and feasibility of the proposed approach.

Due to the unavailable transaction information of customers, the limitation of this study is the effectiveness of the authors’ established recommendation system for platform or website cannot be verified.

In most existing studies, the influence of negative review is counterbalanced by positive review, and the unbalanced effect between negative and positive reviews is ignored. The negative review receives much attention from consumers and businesses. This study thus highlights the influence of negative review.

The purpose of this paper is to analyze the stiffness reliability of harmonic drive (HD) considering contact pairs wear.

In terms of theoretical calculation, the contact pairs wear of HD are calculated based on Archard wear formula and the relative motion characteristics of contact pairs. According to the motion trajectory of flexspline teeth, the teeth backlash and the number of meshing teeth, the meshing stiffness and overall stiffness of HD are analyzed considering the wear and its randomness of contact pairs. Combined with Monte Carlo Simulation, the stiffness reliability evaluation method considering contact pairs wear is proposed, and the result of this method is verified by the stiffness reliability result deduced from the stiffness degradation measurement data.

Considering contact pairs wear, during operation, the teeth backlash increases, the number of meshing teeth decreases, the meshing stiffness decreases, ultimately leading to a gradual decrease in the overall stiffness of HD. When only one type of contact pair wear is considered, the influence of flexspline and circular spline contact pair wear on HD stiffness reliability is greater. Compared with the stiffness reliability evaluation results obtained from the stiffness degradation data in the literature, the mathematic expectation of stiffness degradation failure life distribution obtained from the proposed method is relatively bigger.

The stiffness reliability evaluation method of HD considering contact pairs wear is firstly proposed. The stiffness reliability evaluation result from theoretical calculation is verified by the stiffness reliability results deduced from HD stiffness degradation measurement.

The purpose of this research is to provide a theoretical framework for complex equipment quality risk evaluation. The primary aim of the framework is to enhance the ability to identify risks and improve risk control efficiency during the development phase.

A novel framework for quality risk evaluation in complex equipment is proposed, which integrates probabilistic hesitant fuzzy set-quality function deployment (PHFS-QFD) and grey clustering. PHFS-QFD is applied to identify the quality risk factors, and grey clustering is used to evaluate quality risks in cases of poor quality information during the development stage. The unfolding function of QFD is applied to simplify complex evaluation problems.

The methodology presents an innovative approach to quality risk evaluation for complex equipment development. The case analysis demonstrates that this method can efficiently evaluate the quality risks for aircraft development and systematically trace back the risk factors through hierarchical relationships. In comparison to traditional failure mode and effects analysis methods for quality risk assessment, this approach exhibits superior effectiveness and reliability in managing quality risks for complex equipment development.

This study contributes to the field by introducing a novel theoretical framework that combines PHFS-QFD and grey clustering. The integration of these approaches significantly improves the quality risk evaluation process for complex equipment development, overcoming challenges related to data scarcity and simplifying the assessment of intricate systems.