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The purpose of this paper is to study the influence of pivot stiffness on the dynamic characteristics of tilting-pad journal bearings (TPJBs) and the stability of the bearing-rotor system.

A theoretical numerical model is established, and the influences of pivot stiffness on TPJBs and a bearing-rotor system are analyzed. Then, two kinds of pivot structures with different stiffness are designed and the vibration characteristics are tested on the vertical rotor bearing test bench.

The pivot stiffness has an obvious effect on the dynamic characteristics of the TPJBs and the stability of the bearing-rotor system. As a result of appropriate pivot stiffness, the critical speed and the vibration amplification factor can be reduced, the logarithmic decay rate and the stability of the rotor system can be effectively increased. While the journal whirl orbit is smoother and the rubbing is obviously reduced when the bearings have flexible pivots.

The influence of pivot stiffness on TPJBs and a vertical rotor-bearing system is studied by theoretical and experimental methods.

The purpose of this paper is to study the influence of different carbon chain lengths in coupling agents on the water resistance and compatibility of modified long afterglow phosphors and attempt to obtain their modification model and mechanism.

Three saturated-fatty-acid (caprylic, lauric, stearic acid)-based Al-Zr CAs (coupling agent) was synthesised and applied to modify the long afterglow phosphors SrMgAl₄O₈:Eu₂₊,Dy₃₊.

Results show that the coated amount on phosphors decreased from 13.41 to 6.53 per cent with the increased carbon chain length of fatty acid, and the better water resistant and suitability with organic resin can be obtained by lauric-based Al-Zr CA.

Considering that the decomposition process of modified phosphor was related with the decomposition performance of corresponding coupling agents and original phosphor, a method was first proposed to calculate the coated amount on phosphors by thermogravimetric analyser parameters.

The coal-based synthetic natural gas slag (CSNGS) is a solid waste remaining from the incomplete combustion of raw coal to produce gas. With the continuous promotion of efficient and clean utilization of coal in recent years, the stockpiling of CSNGS would increase gradually, and it would have significant social and environmental benefits with reasonable utilization of CSNGS. This study prepared a new geopolymer by mixing CSNGS with PC42.5 cement in a certain mass ratio as the precursor, with sodium hydroxide and sodium silicate solution as the alkali activators.

The formulation of coal-based synthetic natural gas slag geopolymer (CSNGSG) was determined by an orthogonal test, and then the strength mechanism and microstructure of CSNGSG were characterized by multi-scale tests.

The results show that the optimum ratio of CSNGSG was a sodium silicate modulus of 1.3, an alkali dosage of 21% and a water cement ratio of 0.36 and the maximum unconfined compressive strength of CSNGSG at 7 d was 26.88 MPa. The increase of curing temperature could significantly improve the compressive strength of CSNGSG, and the curing humidity had little effect on the compressive strength of CSNGSG. The development of the internal strength of CSNSG at high temperatures consumed SiO₂, Al₂O₃ and CaO and the intensity of corresponding crystalline peaks decreased.

Moreover, the vibration of chemical bonds in different wavenumbers also revealed the reaction mechanism of CSNSG from another perspective. Finally, the relevant test results indicated that CSNGS had practical application value as a raw material for the preparation of geopolymer cementing materials.

To ensure the motion attitude and stable contact force of massage robot working on unknown human tissue environment, this study aims to propose a robotic system for autonomous massage path planning and stable interaction control.

First, back region extraction and acupoint recognition based on deep learning is proposed, which provides a basis for determining the working area and path points of the robot. Second, to realize the standard approach and movement trajectory of the expert massage, 3D reconstruction and path planning of the massage area are performed, and normal vectors are calculated to control the normal orientation of robot-end. Finally, to cope with the soft and hard changes of human tissue state and body movement, an adaptive force tracking control strategy is presented to compensate the uncertainty of environmental position and tissue hardness online.

Improved network model can accomplish the acupoint recognition task with a large accuracy and integrate the point cloud to generate massage trajectories adapted to the shape of the human body. Experimental results show that the adaptive force tracking control can obtain a relatively smooth force, and the error is basically within ± 0.2 N during the online experiment.

This paper incorporates deep learning, 3D reconstruction and impedance control, the robot can understand the shape features of the massage area and adapt its planning massage path to carry out a stable and safe force tracking control during dynamic robot–human contact.

The purpose of this paper is to solve the problem of low accuracy in new product demand forecasting caused by the absence of historical data and inadequate consideration of influencing factors.

A hybrid new product demand forecasting model combining clustering analysis and deep learning is proposed. Based on the product similarity measurement, the weight of product similarity attributes is realized by using the method of fuzzy clustering-rough set, which provides a basis for the acquisition and collation of historical sales data of similar products and the determination of product similarity. Then the prediction error of Bass model is adjusted based on similarity through a long short-term memory neural network model, where the influencing factors such as product differentiation, seasonality and sales time on demand forecasting are embedded. An empirical example is given to verify the validity and feasibility of the model.

The results emphasize the importance of considering short-term impacts when forecasting new product demand. The authors show that useful information can be mined from similar products in demand forecasting, where the seasonality, product selling cycles and sales dependencies have significant impacts on the new product demand. In addition, they find that even in the peak season of demand, if the selling period has nearly passed the growth cycle, the Bass model may overestimate the product demand, which may mislead the operational decisions if it is ignored.

This study is valuable for showing that with the incorporation of the evaluation method on product similarity, the forecasting model proposed in this paper achieves a higher accuracy in forecasting new product sales.

The purpose of this paper is to obtain the combination of working parameters suitable for pulsating negative pressure shale shaker through simulation, which is conducive to efficient recovery of clean drilling fluid and relatively dry cuttings.

Shale shaker is still one of the main equipment in solid–solid and solid–liquid separation processes in drilling industry. This research is based on a new drilling fluids circulation treatment device, namely pulsating negative pressure shale shaker. In this work, a numerical study of particle flow and separation in the pulsating negative pressure shale shaker is carried out by coupling computational fluid dynamics/discrete element method (CFD-DEM). The effect of vibration parameters and negative pressure parameters are studied in terms of conveyance velocity and percent through screen.

The results show that, conveyance velocity of particle is mainly affected by vibration parameters, negative pressure in pulsating form can effectively prevent cuttings from sticking to the screen. Vibration parameters and pulsating airflow velocity peak have great influence on percent through screen, while vibration frequency and screen slope have influence on the time when the percent through screen reaches stability.

In this paper, the authors put forward a new kind of drilling waste fluid treatment equipment, and focused on the study of particle movement law. The results have important guiding significance for the selection of structural design parameters and rational use of equipment. In addition, the new device provides a new idea for solid–liquid separation method, which is one of the hot topics in current research.

The amount, type and addition conditions of additives of lubricants should be continuously adjusted to obtain appealing performance. To obtain the optimal pretreatment parameters and reduce the cost of time-consuming experiments, the purpose of this paper is to establish an optimal back propagation neural network (BPNN) model combined with genetic algorithm (GA) in this work.

Using trimethylolpropane trioleate as the base oil and three types of phosphorus compounds as additives, 25 sets of lubricant formulas were designed regarding lubricant performances of average friction coefficient, average spot diameter, disk wear volume and extreme pressure. The data set was used for training and learning of BPNN and then combined with GA to optimize BPNN with continuously optimization by adjusting various parameters.

Comparing prediction data of BPNN with actual test data, correlation coefficients were above 90%, indicating that the model could accurately predict the performance of lubricants. When combined with GA, all performance errors were less than 5%, indicating that BPNN could be optimized by GA to obtain an accurate combined model for prediction of lubricant performance. The best additive formula with excellent performances was obtained from the BPNN–GA model.

This work developed a new method to study lubricant compounding. The combined model was expected to provide a theoretical basis and guidance for the compounding optimization of lubricant additives with high efficiency and low cost and to expand the scope to practical applications.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-05-2020-0165/

High residual stress caused by the high temperature gradient brings undesired effects such as shrinkage and cracking in selective laser melting (SLM). The purpose of this study is to predict the residual stress distribution and the effect of process parameters on the residual stress of selective laser melted (SLMed) Inconel 718 thin-walled part.

A three-dimensional (3D) indirect sequentially coupled thermal–mechanical finite element model was developed to predict the residual stress distribution of SLMed Inconel 718 thin-walled part. The material properties dependent on temperature were taken into account in both thermal and mechanical analyses, and the thermal elastic–plastic behavior of the material was also considered.

The residual stress changes from compressive stress to tensile stress along the deposition direction, and the residual stress increases with the deposition height. The maximum stress occurs at both ends of the interface between the part and substrate, while the second largest stress occurs near the top center of the part. The residual stress increases with the laser power, with the maximum equivalent stress increasing by 21.79 per cent as the laser power increases from 250 to 450 W. The residual stress decreases with an increase in scan speed with a reduction in the maximum equivalent stress of 13.67 per cent, as the scan speed increases from 500 to 1,000 mm/s. The residual stress decreases with an increase in layer thickness, and the maximum equivalent stress reduces by 33.12 per cent as the layer thickness increases from 20 to 60µm.

The residual stress distribution and effect of process parameters on the residual stress of SLMed Inconel 718 thin-walled part are investigated in detail. This study provides a better understanding of the residual stress in SLM and constructive guidance for process parameters optimization.

The paper debunks Peter P. Li’s assertion that Yin-Yang is superior to any other cognitive frames or logical systems for paradox research. The purpose of this paper is to alert the Chinese indigenous management researchers to the danger of Chinese exceptionalism and over-confidence.

To show that Peter P. Li’s assertion is doubtful, the authors identify the flaws in his analysis.

The authors find that there are three serious flaws in Peter P. Li’s analysis. First, there are four defects in the typology of cognitive frames he built in order to compare Yin-Yang with the others. Second, his understanding of dialectics in general and Hegelian dialectics in particular is flawed. And finally, without resorting to Yin-Yang, many scholars can develop theories that are equivalent to those derived from Yin-Yang.

Due to the page limit, this paper only focuses on arguing that Yin-Yang is not superior to other cognitive frames or logical systems without going one step further to explain in which situations Yin-Yang are valuable and might be more suitable than others for helping us understand some research issues.

This paper implies that we should not blindly believe that the Chinese way of thinking and acting is superior to other people’s. Chinese people should be open-minded in the globalized era, not only promoting their own culture but also appreciating and learning from other cultures.

The reduction of cultural exceptionalism and ethnocentrism can make cross-cultural communication and interaction smoother.

This paper is a rigorous critique on the “Yin-Yang being superior” assertion of Peter P. Li.

The electronic nose is an array of chemical or gas sensors and associated with a pattern-recognition framework competent in identifying and classifying odorant or non-odorant and simple or complex gases. Despite more than 30 years of research, the robust e-nose device is still limited. Most of the challenges towards reliable e-nose devices are associated with the non-stationary environment and non-stationary sensor behaviour. Data distribution of sensor array response evolves with time, referred to as non-stationarity. The purpose of this paper is to provide a comprehensive introduction to challenges related to non-stationarity in e-nose design and to review the existing literature from an application, system and algorithm perspective to provide an integrated and practical view.

The authors discuss the non-stationary data in general and the challenges related to the non-stationarity environment in e-nose design or non-stationary sensor behaviour. The challenges are categorised and discussed with the perspective of learning with data obtained from the sensor systems. Later, the e-nose technology is reviewed with the system, application and algorithmic point of view to discuss the current status.

The discussed challenges in e-nose design will be beneficial for researchers, as well as practitioners as it presents a comprehensive view on multiple aspects of non-stationary learning, system, algorithms and applications for e-nose. The paper presents a review of the pattern-recognition techniques, public data sets that are commonly referred to as olfactory research. Generic techniques for learning in the non-stationary environment are also presented. The authors discuss the future direction of research and major open problems related to handling non-stationarity in e-nose design.

The authors first time review the existing literature related to learning with e-nose in a non-stationary environment and existing generic pattern-recognition algorithms for learning in the non-stationary environment to bridge the gap between these two. The authors also present details of publicly available sensor array data sets, which will benefit the upcoming researchers in this field. The authors further emphasise several open problems and future directions, which should be considered to provide efficient solutions that can handle non-stationarity to make e-nose the next everyday device.