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The purpose of variable loading conditions (392 N-785N-392N-785N) with break-in period were used to study interactions between zinc dialkyl dithiophosphate (ZDDP) 0.1 P% (phosphorus) and fine-grade molybdenum disulfide (MoS₂) 3%, in different mixtures of NLGI 2 lithium stearate grease. Four-ball wear tests were used to evaluate the tribological properties of different grease mixtures such as coefficient of friction and wear. ASTM 2266 as reported by earlier studies is useful, but it is not representative of real-life applications where variable loads and speeds and different break-in periods play a role and could change the results and the nature of tribofilms.

In this study, chemical and mechanical properties of tribofilms were examined. Moreover, design of experiment was used to examine the data and shorten experimentation time. Research described here is investigating variable loading conditions for real-life applications by using a break-in period of 2 min at the start to minimize asperities and establish a clean surface. Design expert (DOE) analyzes responses to reveal those variables that are single factor and those that are multifactor whether synergistically or antagonistically.

The results indicated that spectrum loading with break-in period showed reduction in wear when tested in greases with ZDDP/MoS₂ combinations. Ramping up or down the load every 7.5 min for a rotational speed of 1,200 rpm and a total of 36,000 revolutions or 30-min time slowed the wear properties of lithium-based grease under different MoS₂ and ZDDP concentrations. Experiments indicated that wear was largely dependent on the loading condition and ZDDP additives during specific break-in period at 1,200 rotational speed. It is believed that MoS₂ greases perform better under spectrum loading and under constant loading when mixed with ZDDP phosphorus.

This research indicates that there is a synergistic interaction between ZDDP, MoS₂ and variable loading especially when a break-in period is applied. The results indicated that wear was largely dependent on the specific speed used with spectrum loading as presented in the energy dispersive spectroscopy and the Auger electron spectroscopy analysis, and thus a 3% MoS₂ grease with ZDDP (phosphorus: 0.1 Wt.%) are needed to improve the wear resistance and improve the friction characteristics.

The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-01-2024-0016/

To simplify the circuit design and control complexity of the magnetic coupling resonant wireless charging system, the radio energy transmission constant current and constant voltage charging is realized.

The purpose of this study is to simplify the circuit design and control complexity of the magnetic coupling resonance wireless charging system, in order to achieve constant current and constant voltage charging for wireless energy transmission. First, the principle of LCC/S-S compensation structure is analyzed, and the equivalent mathematical model is established; then, the system characteristics under constant current and constant voltage mode are analyzed, and the design method of system parameters is given; finally, a simulation and experimental system is built to verify the correctness and feasibility of the theoretical analysis.

The results show that the proposed hybrid topology can achieve a constant current output of 2 A and a constant voltage output of 30 V under variable load conditions, and effectively suppress the current distortion problem under light load conditions. The waveform distortion rate of the inverter current is reduced from 33.97% to 10.45%.

By changing the high-order impedance characteristics of the compensation structure, the distortion of the current waveform under light load is suppressed, and the overall stability and efficiency of the system are improved.

The objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.

Effective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.

Based on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.

Early air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.

To the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

Fiber reinforced additive manufacturing (FRAM) is an emerging technology that combines additive manufacturing and composite materials. As a result, design freedom offered by the manufacturing process can be leveraged in design optimization. The purpose of the study is to propose a novel method that improves structural performance by optimizing 3D print orientation of FRAM components.

This work proposes a two-part design optimization method that optimizes 3D global print orientation and topology of a component to improve a structural objective function. The method considers two classes of design variables: (1) print orientation design variables and (2) density-based topology design variables. Print orientation design variables determine a unique 3D print orientation to influence anisotropic material properties. Topology optimization determines an optimal distribution of material within the optimized print orientation.

Two academic examples are used to demonstrate basic behavior of the method in tension and shear. Print orientation and sequential topology optimization improve structural compliance by 90% and 58%, respectively. An industry-level example, an aerospace component, is optimized. The proposed method is used to achieve an 11% and 15% reduction of structural compliance compared to alternative FRAM designs. In addition, compliance is reduced by 43% compared to an equal-mass aluminum design.

Current research surrounding FRAM focuses on the manufacturing process and neglects opportunities to leverage design freedom provided by FRAM. Previous FRAM optimization methods only optimize fiber orientation within a 2D plane and do not establish an optimized 3D print orientation, neglecting exploration of the entire orientation design space.

This study aims to explore and empirically test variables influencing material delivery schedule inaccuracies?

A mixed-method case approach is applied. Explanatory variables are identified from the literature and explored in a qualitative analysis at an automotive original equipment manufacturer. Using logistic regression and random forest classification models, quantitative data (historical schedule transactions and internal data) enables the testing of the predictive difference of variables under various planning horizons and inaccuracy levels.

The effects on delivery schedule inaccuracies are contingent on a decoupling point, and a variable may have a combined amplifying (complexity generating) and stabilizing (complexity absorbing) moderating effect. Product complexity variables are significant regardless of the time horizon, and the item’s order life cycle is a significant variable with predictive differences that vary. Decoupling management is identified as a mechanism for generating complexity absorption capabilities contributing to delivery schedule accuracy.

The findings provide guidelines for exploring and finding patterns in specific variables to improve material delivery schedule inaccuracies and input into predictive forecasting models.

The findings contribute to explaining material delivery schedule variations, identifying potential root causes and moderators, empirically testing and validating effects and conceptualizing features that cause and moderate inaccuracies in relation to decoupling management and complexity theory literature?

This article explores whether six broad categories of activities undertaken by Canadian business scholars’ academics: publications record, citations record, teaching load, administrative load, consulting activities, and knowledge spillovers transfer, are complementary, substitute, or independent, as well as the conditions under which complementarities, substitution and independence among these activities are likely to occur.

A multivariate probit model is estimated to take into account that business scholars have to consider simultaneously whether or not to undertake many different academic activities. Metrics from Google Scholar of scholars from 35 Canadian business schools, augmented by a survey data on factors explaining the productivity and impact performances of these faculty members, are used to explain the heterogeneities between the determinants of these activities.

Overall, the results reveal that there are complementarities between publications and citations, publications and knowledge spillovers transfer, citations and consulting, and between consulting and knowledge spillovers transfer. The results also suggest that there are substitution effects between publications and teaching, publications and administrative load, citations and teaching load, and teaching load and administrative load. Moreover, results show that public and private funding, business schools’ reputation, scholar’s relational resources, and business school size are among the most influential variables on the scholar’s portfolio of activities.

This study considers simultaneously the scholar’s whole portfolio of activities. Moreover, the determinants considered in this study to explain scholars’ engagement in different activities reconcile two conflicting perspectives: (1) the traditional self-managed approach of academics, and (2) the outcomes-focused approach of university management.

Interrelationships among some common factors of human resource (HR) management and quality management are still unexplored. Changes in work patterns due to the Covid-19 pandemic have aroused interest in some of these factors, such as working-hours, work pressure, work–life balance practices, job satisfaction. The purpose of this study is to explore the interrelationships among such factors. Specifically, the influence of work hours, work pressure, job rotation and work–life balance on job satisfaction is evaluated both directly and under the mediating influence of working conditions.

A questionnaire-based survey was conducted in Indonesia among diversified organisations. A total of 432 responses were gathered, and they were examined using hypothesis testing and partial least square based structural equation modelling.

The study confirms the statistically proven impact of work pressure, job rotations and work–life-balance practices on working conditions. Job rotations, work–life balance practices and working conditions directly influenced job satisfaction. Work pressure did not influence job satisfaction directly, but it significantly influenced working conditions, which eventually affected job satisfaction. Working hours neither affected working conditions nor job satisfaction in a significant manner.

Covid-19 necessitated working from home, which is a peculiar work–life balance situation. The findings are helpful for organisations in planning strategies related to work–life-balance, working hours, multi-skilling, working conditions and other quality of work life factors in both regular working conditions and under Covid-19 conditions.

The proven influence of work pressure and work–life-balance practices may result in the formation of informal organisations, social groups and increased social networking. As working hours are not diagnosed as an influencing factor for job satisfaction, organisations may think about increasing them, affecting the social fabric of the working community.

Previously unexplored interrelationships among various quality of work life factors are established. Under Covid-19 circumstances, factors such as working hours, work–life-balance and work pressure are investigated in a novel manner. The factors and their interrelationships are important to both quality management professionals and HR professionals.

Although extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.

In order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.

A real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.

This study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

This paper aims to contribute novel insights into the analysis of thin functionally graded material (FGM) plates with variable thickness, considering both temperature-dependent and independent material properties, focusing on critical linear buckling temperature rise and the effect of critical linear moisture for various moisture concentrations.

The study derives stability and equilibrium equations for thin rectangular FGM plates under hygrothermal loading, employing classical plate theory (CPT). Buckling behavior is examined using Galerkin’s method to obtain pre-buckling force resultants.

The findings highlight significant increases in critical buckling temperature with aspect ratio, distinct temperature sensitivity between materials and increasing moisture susceptibility with larger aspect ratios. These insights inform material selection and design optimization for FGM plates under hygrothermal loading, enhancing engineering applications.

This research primarily focuses on hypothetical scenarios and mathematical model development and analysis.

This paper presents original contributions in the field by addressing the hygrothermal buckling analysis of thin FGM rectangular plates with variable thickness, utilizing CPT, thereby enriching the understanding of structural behavior in varying environmental conditions.

On the one hand, this paper is to further understand the residents' differentiated power consumption behaviors and tap the residential family characteristics labels from the perspective of electricity stability. On the other hand, this paper is to address the problem of lack of causal relationship in the existing research on the association analysis of residential electricity consumption behavior and basic information data.

First, the density-based spatial clustering of applications with noise method is used to extract the typical daily load curve of residents. Second, the degree of electricity consumption stability is described from three perspectives: daily minimum load rate, daily load rate and daily load fluctuation rate, and is evaluated comprehensively using the entropy weight method. Finally, residential customer labels are constructed from sociological characteristics, residential characteristics and energy use attitudes, and the enhanced FP-growth algorithm is employed to investigate any potential links between each factor and the stability of electricity consumption.

Compared with the original FP-growth algorithm, the improved algorithm can realize the excavation of rules containing specific attribute labels, which improves the excavation efficiency. In terms of factors influencing electricity stability, characteristics such as a large number of family members, being well employed, having children in the household and newer dwelling labels may all lead to poorer electricity stability, but residents' attitudes toward energy use and dwelling type are not significantly associated with electricity stability.

This paper aims to uncover household socioeconomic traits that influence the stability of home electricity use and to shed light on the intricate connections between them. Firstly, in this article, from the perspective of electricity stability, the characteristics of the power consumption of residents' users are refined. And the authors use the entropy weight method to comprehensively evaluate the stability of electricity usage. Secondly, the labels of residential users' household characteristics are screened and organized. Finally, the improved FP-growth algorithm is used to mine the residential household characteristic labels that are strongly associated with electricity consumption stability.

1. The stability of electricity consumption is important to the stable operation of the grid.

2. An improved FP-growth algorithm is employed to explore the influencing factors.

3. The improved algorithm enables the mining of rules containing specific attribute labels.

4. Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.

The stability of electricity consumption is important to the stable operation of the grid.

An improved FP-growth algorithm is employed to explore the influencing factors.

The improved algorithm enables the mining of rules containing specific attribute labels.

Residents' attitudes toward energy use are largely unrelated to the stability of electricity use.