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This paper aims to compare the tribological performances of four different types of tooth surface finishing, namely, form grinding, generating grinding, super finishing and grinding and coating, and to reveal the details at dry contact nodes.

Real measured roughness is input to a finite line contact mixed elastohydrodynamic lubrication model developed for helical gear pairs. Their tribological performances are compared. The variation throughout one meshing period is analyzed. The influence of the root mean square (RMS) is studied. The textures are also scaled to the same RMS values to make comparisons while excluding the influence of roughness amplitude.

Roughness is directly reflected in pressure and film thickness. Average film thickness sees major changes while entering and leaving the single-tooth-contact region. The textures have different performances even under the same RMS. Roughness peaks incurring dry contact are those higher than the smooth-situation film thickness plus the sum of variation in normal approach and elastic deformation compared with the smooth situation. To lower dry contact severity, the surface finishing process should take care of both the overall amplitude and the portion of peaks with maximum height. When RMS value is the same, the latter plays a decisive role.

This paper interprets the differences between the tribological performances of four different types of tooth surface finishing from the aspect of roughness features and presents a way to analyze the details at dry contact nodes.

This paper aims to present a dynamic reliability model of scraper chains based on the fretting wear process and propose a reasonable structural optimization method.

First, the dynamic tension of the scraper chain is modeled by considering the polygon effect of the scraper conveyor. Then, the numerical wear model of the scraper chain is established based on the tangential and radial fretting wear modes. The scraper chain wear process is introduced based on the diameter wear rate. Furthermore, the time-dependent reliability of scraper chains based on the fretting wear process is addressed by the third-moment saddlepoint approximation (TMSA) method. Finally, the scraper chain is optimized based on the reliability optimization design theory.

There is a correlation between the wear and the dynamic tension of the scraper conveyor. The unit sliding distance of fretting wear is affected by the dynamic tension of the scraper conveyor. The reliability estimation of the scraper chain with incomplete probability information is achieved by using the TMSA for the method needs only the first three statistical moments of the state variable. From the perspective of the chain drive system, the reliability-based optimal design of the scraper chain can effectively extend its service life and reduce its linear density.

The innovation of the work is that the physical model of the scraper chain wear is established based on the dynamic analysis of the scraper conveyor. And based on the physical model of wear, the time-dependent reliability and optimal design of scraper chains are carried out.

Using bibliometric techniques, the author analyzes a dataset of 276 articles on cross-border mergers and acquisitions (CBMAs) published in 13 management and international business journals. The author assesses the scientific impact and visualizes the intellectual landscape of research on CBMAs by analyzing publication and citation data and interconnections between publications. First, the author assesses annual publication trends and identifies highly cited articles and productive journals in the dataset that have significantly contributed to our understanding of CBMAs. Second, the author identifies main themes in recent research on CBMAs by focusing on frequently used keywords in publications. Third, the author identifies clusters of related research and explores their interrelationships to outline emerging trends, new perspectives, and directions for future research on CBMAs. Overall, this chapter contributes to the understanding of CBMAs by documenting the progress made to date and providing important insights for future research.

For a wide range of market actors, including policymakers, forecasting changes in commodity prices is crucial. As one of essential edible oil, peanut oil’s price swings are certainly important to predict. In this paper, the weekly wholesale price index for the period of January 1, 2010 to January 10, 2020 is used to address this specific forecasting challenge for the Chinese market.

The nonlinear auto-regressive neural network (NAR-NN) model is the forecasting method used. Forecasting performance based on various settings, such as training techniques, delay counts, hidden neuron counts and data segmentation ratios, are assessed to build the final specification.

With training, validation and testing root mean square errors of 5.89, 4.96 and 5.57, respectively, the final model produces reliable and accurate forecasts. Here, this paper demonstrates the applicability of the NAR-NN approach for commodity price predictions.

On the one hand, the findings may be used as independent technical price movement predictions. Conversely, they may be included in forecast combinations with forecasts derived from other models to form viewpoints of commodity price patterns for policy research.

The edge of diesel engine crankshaft main bearing is more likely to fail in its real working condition. This paper aims to study the bearing failure mechanism by finding the relationship between bearing lubrication characteristics and its working condition.

This work builds the mixed lubrication model of crankshaft bearing to analyze the cause of bearing abnormal wear, and the finite difference method was used to solving the average Reynolds equation. During the analysis, journal misaligned angle, external load and roughness are considered.

The result shows that the wear of the diesel engine crankshaft bearing happens in engine startup phase and the bottom of the bearing are more prone to be excessively worn. Under the influence of journal misalignment, bearing asperity contact load and speed range of mixed lubrication will increase markedly. The edge of the bearing will be excessively worn. The effect of misalignment on bearing lubrication performance varies under different shaft rotation speed.

The former research studies on crankshaft bearing either just focused on its lubrication characteristics or interested in its failure types (wear, adhere, cavitation). This paper studies the relationship between bearing failure mechanism and lubrication performance.

To fabricate high performance parts, this paper aims to systematically study the pores characteristics and their formation mechanisms in selective laser melting (SLM) AlSi10Mg.

Cubes of 10 × 10 × 5 mm were manufactured in different laser power, scan speed and scan space. Optical microscope (OM) and scanning electron microscopes (SEM) were used to observe morphology of pores.

Round or irregular pores were found in SLMed AlSi10Mg parts. All the round pores have smooth inner walls and locate in the melt pool. The formation mechanisms of the round pores are contributed to the evaporation of elements in the melt pool, H₂O, high laser energy input and hollow powder. Irregular pores have rough inner walls. Big scan space, unevenness of the upper surface, large layer thickness, spatter and oxide are the main reasons of generating irregular pores which outside the melt pool. Instability of keyhole leads to the irregular pores locate in the bottom of keyhole mode melt pool.

Relationship between pores and melt pool were studied systematically for the first time. Researches of pores characteristics and their formation mechanisms in SLMed AlSi10Mg would be a valuable reference for researchers to obtain an important insight into and control the defect in SLMed Al alloy.

The purpose of this paper is to use a combination of numerical simulation and experiment to evaluate the performance of laser surface texturing (LST) in the field of gear lubrication, and to more accurately predict the lubrication characteristics of different surfaces.

The method used in this paper is developed on the basis of the deterministic solution of the three-dimensional (3D) mixed elasto-hydrodynamic lubrication (EHL) model and the model parameters are corrected by friction test. The film pressure, film thickness and friction coefficient of different micro-textured tooth surfaces are predicted on the basis of accurate 3D mixed EHL models.

The results demonstrate that the micro-texture structure of the tooth surface can increase the local film thickness and enhance the lubricating performance of the tooth surface without drastically reducing the contact fatigue life. The stress distribution and friction characteristics of the tooth surface can be optimized by adjusting the micro-texture arrangement and the size of the micro-textures.

A new evaluation method using a 3D hybrid EHL model and friction test to predict the lubrication characteristics of LST is proposed, which can effectively improve the processing economy and save time.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2020-0423

Forecasts of commodity prices are vital issues to market participants and policy makers. Those of corn are of no exception, considering its strategic importance. In the present study, the authors assess the forecast problem for the weekly wholesale price index of yellow corn in China during January 1, 2010–January 10, 2020 period.

The authors employ the nonlinear auto-regressive neural network as the forecast tool and evaluate forecast performance of different model settings over algorithms, delays, hidden neurons and data splitting ratios in arriving at the final model.

The final model is relatively simple and leads to accurate and stable results. Particularly, it generates relative root mean square errors of 1.05%, 1.08% and 1.03% for training, validation and testing, respectively.

Through the analysis, the study shows usefulness of the neural network technique for commodity price forecasts. The results might serve as technical forecasts on a standalone basis or be combined with other fundamental forecasts for perspectives of price trends and corresponding policy analysis.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.