Search results

This study aims to investigate the effect of vapor phase corrosion inhibitor (VCI) imidazoline, quinoline and urea on E36 ship steel in simulated marine atmospheric environment.

The inhibitive effect was investigated by weight loss, potentiodynamic polarization, electrochemical impedance and Raman spectroscopy.

The corrosion process was inhibited by adsorption of the three VCIs on the surface of ship steel. The corrosion inhibition mechanism is the adsorption film type. The corrosion inhibitor adsorbs on the metal surface to prevent Cl^- from reaching the surface of the substrate, so as to achieve the corrosion inhibition effect.

To the best of the authors’ knowledge, the work described was original research that has not been published previously and not under consideration for publication elsewhere, in whole or in part.

Despite the widespread expectation that additive manufacturing (AM) will become a disruptive technology to transform the spare parts supply chain, very limited research has been devoted to the quantitative modeling and analysis on how AM could fulfill the on-demand spare parts supply. On the other hand, the choice of using AM as a spare parts supply strategy over traditional inventory is a rising decision faced by manufacturers and requires quantitative analysis for their AM-or-stock decisions. The purpose of this paper is to develop a quantitative performance model for a generic powder bed fusion AM system in a spare parts supply chain, thus providing insights into this less-explored area in the literature.

In this study, analysis based on a discrete event simulation was carried out for the use of AM in replacement of traditional warehouse inventory for an on-demand spare parts supply system. Generic powder bed fusion AM system was used in the model, and the same modeling approach could be applied to other types of AM processes. Using this model, the impact of both spare parts demand characteristics (e.g. part size attributes, demand rates) and the AM operations characteristics (e.g. machine size and postpone strategy) on the performance of using AM to supply spare parts was studied.

The simulation results show that in many cases the AM operation is not as cost competitive compared to the traditional warehouse-based spare parts supply operation, and that the spare parts size characteristics could significantly affect the overall performance of the AM operations. For some scenarios of the arrival process of spare parts demand, the use of the batched AM production could potentially result in significant delay in parts delivery, which necessitates further investigations of production optimization strategies.

The findings demonstrate that the proposed simulation tool can not only provide insights on the performance characteristics of using AM in the spare parts supply chain, especially in comparison to the traditional warehousing system, but also can be used toward decision making for both the AM manufacturers and the spare parts service providers.

Focusing on the problem that the visual detection algorithm of navigation path line in intelligent harvester robot is susceptible to interference and low accuracy, a navigation path detection algorithm based on improved random sampling consensus is proposed.

First, inverse perspective mapping was applied to the original images of rice or wheat to restore the three-dimensional spatial geometric relationship between rice or wheat rows. Second, set the target region and enhance the image to highlight the difference between harvested and unharvested rice or wheat regions. Median filter is used to remove the intercrop gap interference and improve the anti-interference ability of rice or wheat image segmentation. The third step is to apply the method of maximum variance to thresholding the rice or wheat images in the operation area. The image is further segmented with the single-point region growth, and the harvesting boundary corner is detected to improve the accuracy of the harvesting boundary recognition. Finally, fitting the harvesting boundary corner point as the navigation path line improves the real-time performance of crop image processing.

The experimental results demonstrate that the improved random sampling consensus with an average success rate of 94.6% has higher reliability than the least square method, probabilistic Hough and traditional random sampling consensus detection. It can extract the navigation line of the intelligent combine robot in real time at an average speed of 57.1 ms/frame.

In the precision agriculture technology, the accurate identification of the navigation path of the intelligent combine robot is the key to realize accurate positioning. In the vision navigation system of harvester, the extraction of navigation line is its core and key, which determines the speed and precision of navigation.

The purpose of this study is to set up a dynamic model of material extrusion (ME) additive manufacturing plates for the prediction of their dynamic behavior (i.e. dynamic inherent characteristic, resonant response and damping) and also carry out its experimental validation and sensitivity analysis.

Based on the classical laminated plate theory, a dynamic model is established using the orthogonal polynomials method, taking into account the effect of lamination and orthogonal anisotropy. The dynamic inherent characteristics of the ME plate are worked out by Ritz method. The frequency-domain dynamic equations are then derived to solve the plates’ resonant responses, with which the damping ratio is figured out according to the half-power bandwidth method. Subsequently, a series of experimental tests are performed on the ME samples to obtain the measured data.

It is shown that the predictions and measurements in terms of dynamic behavior are in good agreement, validating the accuracy of the developed model. In addition, sensitivity analysis shows that increasing the elastic modulus or Poisson’s ratio will increase the corresponding natural frequency of the ME plate but decrease the resonant response. When the density is increased, both the natural frequency and resonant response will be decreased.

Future research can be focused on using the proposed model to investigate the effect of processing parameters on the ME parts’ dynamic behavior.

This study shows theoretical basis and technical insight into improving the forming quality and reliability of the ME parts.

A novel reliable dynamic model is set up to provide theoretical basis and principle to reveal the physical phenomena and mechanism of ME parts.

The purpose of this research is to develop a time-cost optimization model to schedule repetitive projects while considering limited resource availability.

The model is based on the constraint programming (CP) framework; it integrates multiple scheduling characteristics of repetitive activities such as continuous or fragmented execution, atypical activities and coexistence of different modes in an activity. To improve project performance while avoiding inefficient hiring and firing conditions, the strategy of bidirectional acceleration is presented and implemented, which requires keeping regular changes in the execution modes between successive subactivities in the same activity.

Two case studies involving a real residential building construction project and a hotel refurbishing project are used to demonstrate the application of the proposed model based on four different scenarios. The results show that (1) the CP model has great advantages in terms of solving speed and solution quality than its equivalent mathematical model, (2) higher project performance can be obtained compared to using previously developed models and (3) the model can be easily replicated or even modified to enable multicrew implementation.

The original contribution of this research is presenting a novel CP-based repetitive scheduling optimization model to solve the multimode resource-constrained time-cost tradeoff problem of repetitive projects. The model has the capability of minimizing the project total cost that is composed of direct costs, indirect costs, early completion incentives and late completion penalties.

This study developed two presentation modes of scientific articles and evaluated their usability with user experiments.

Two presentation modes of scientific articles, simply referred to as “genre presentation mode” and “argument presentation mode”, were constructed based on their genre structure and argument structure respectively. Their usability was evaluated by being compared against the existing RichHTML presentation mode in the experiments using eye-tracking and questionnaire methods.

The participants who were going to find the specific information of scientific articles rated the genre presentation mode higher than they did with either the argument or the RichHTML presentation mode for effectiveness. In contrast, those who were going to understand the general idea of scientific articles rated both the genre and argument presentation mode higher than they did with the RichHTML mode. In terms of efficiency, the participants took less reading time when the articles of non-native language were presented with the genre mode than they did when the articles were presented with the argument mode. When reading the articles in native language to understand the general idea, the participants took less reading time for the articles presented with the argument mode than they did for the articles presented with the genre mode. In comparison, they took less reading time when the articles were presented with the argument mode if they were going to find specific information. For satisfaction, the genre presentation mode was more popular than the argument mode and the RichHTML mode. However, the participants were less satisfied with the argument presentation mode than the other two when reading in native language and intending to find specific information.

The two presentation modes of scientific articles are found to improve the accuracy of information acquisition, shorten the total reading time and be more acceptable by readers.

The purpose of this paper is to investigate the effect of strut size on the compressive response for selective laser-melted lattice structure with a body-centered cubic (BCC) unit cell.

Theoretical analysis and numerical simulation were used to predict the compressive stiffness and strength of the lattice structures with different struts, and compression testing was conducted to validate the predicted results. The effect of strut size on actual porosity was determined with the dry weighting method. Scanning electron microscopy was used to observe the fracture morphologies.

The actual porosities in all the specimens turned out to be a little lower than the values expected from design. The maximum deviation appears at the strut size of 1.25 mm. The theoretical analysis reveals that the junctions of BCC unit cells are the most loaded points, and the maximum compression resistance load is proportional to the strut size. The stress–strain curves and collapse modes predicted by numerical simulation are in good agreement with the theoretical calculation and experimental results. The compression stress increases monotonously in strut size of 0.50–2.00 mm. The fracture morphologies reflect a transition from a mixed to ductile fracture mechanism. The lattice structure shows a stable plastic deformation without a destructive fracture for the strut size of 2.00 mm.

The findings of this study can provide theoretical and experimental support for the choice of strut size under different stress conditions. In addition, they are conductive to in-depth study of the compressive properties for lattice structures with different geometrical dimensions fabricated by selective laser melting.

The main purpose of this paper is to explore the influence mechanism of corporate social responsibility (CSR) for smart cities on consumers' purchase intention. The authors aim to identify the key components of CSR for smart cities based on the perspective of consumers, namely responsibility toward consumers, environment and community and validate their relationship.

The authors exploit data collected by questionnaire surveys to estimate the effects of CSR for smart cities on consumers' purchase intentions and to investigate the statistical causality between them. The multilinear regression model is used to figure out the different impact levels of the three dimensions of CSR for smart cities on consumers' purchase intention.

The results illustrate that CSR for smart cities and its three dimensions all have significant positive impacts on consumers' purchase intentions. Besides, consumer–corporate identity (CCI) exerts a partial mediation effect on this influence mechanism.

This research is based on a rather small sample size. Besides, due to the time limitation and other factors, some other control variables are neglected in the regression model. Therefore, the impact level could be distorted.

The authors put forward management implications according to research conclusions. Corporates should actively fulfill the CSR in the field of consumer responsibility to boost consumers' purchase intention. Corporate should strengthen the interaction with consumers to improve their corporate identity.

The main contribution of this paper is to provide convincing evidence of the impacts of CSR for smart cities on consumer purchase intention (CPI), thus proposing effective measures for corporates to win more consumers by taking on social responsibility for smart cities. This paper takes CCI as mediating variable to deepen the understanding of the impacts of CSR for smart cities on CPI, which is innovative and beneficial to enriching literature in related fields.

Accurately, positioning is a fundamental requirement for vision measurement systems. The calculation of the harvesting width can not only help farmers adjust the direction of the intelligent harvesting robot in time but also provide data support for future unmanned vehicles.

To make the length of each pixel equal, the image is restored to the aerial view in the world coordinate system. To solve the problem of too much calculation caused by too many particles, a certain number of particles are scattered near the crop boundary and the distribution regularities of particles’ weight are analyzed. Based on the analysis, a novel boundary positioning method is presented. In the meantime, to improve the robustness of the algorithm, the back-projection algorithm is also used for boundary positioning.

Experiments demonstrate that the proposed method could well meet the precision and real-time requirements with the measurement error within 55 mm.

In visual target tracking, using particle filtering, a rectangular is used to track the target and cannot obtain the boundary information. This paper studied the distribution of the particle set near the crop boundary and proposed an improved particle filtering algorithm. In the algorithm, a small amount of particles is used to determine the crop boundary and accurate positioning of the crop boundary is realized.

Interrupting work continuity provides a way to improve some project performance, but unexpected and harmful interruptions may impede the implementation. This paper aims to mitigate the negative impact caused by work continuity uncertainty based on the notion of robustness.

This paper develops a float-based robustness measurement method for the work continuity uncertainty in repetitive projects. A multi-objective optimization model is formulated to generate a schedule that achieves a balance between crew numbers and robustness. This model is solved using two modules: optimization module and decision-making module. The Monte Carlo simulation is designed to validate the effectiveness of the generated schedule.

The results confirmed that it is necessary to consider the robustness as an essential factor when scheduling a repetitive project with uncertainty. Project managers may develop a schedule that is subject to delays if they only make decisions according to the results of the deadline satisfaction problem. The Monte Carlo simulation validated that an appropriate way to measure robustness is conducive to generating a schedule that can avoid unnecessary delay, compared to the schedule generated by the traditional model.

Available studies assume that the work continuity is constant, but it cannot always be maintained when affected by uncertainty. This paper regards the work continuity as a new type of uncertainty factor and investigates how to mitigate its negative effects. The proposed float-based robustness measurement can measure the ability of a schedule to absorb unpredictable and harmful interruptions, and the proposed multi-objective scheduling model provides a way to incorporate the uncertainty into a schedule.