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Sliding wear is a common phenomenon in the iron ore handling industry. Large-scale handling of iron ore bulk-solids causes a high amount of volume loss from the surfaces of bulk-solids-handling equipment. Predicting the sliding wear volume from equipment surfaces is beneficial for efficient maintenance of worn equipment. Recently, the discrete element method (DEM) simulations have been utilised to predict the wear by bulk-solids. However, the sensitivity of wear prediction subjected to DEM parameters has not been systemically investigated at single particle level. To ensure the wear predictions by DEM are accurate and stable, this study aims to conduct the sensitivity analysis at the single particle level.

In this research, pin-on-disc wear tests are modelled to predict the sliding wear by individual iron ore particles. The Hertz–Mindlin (no slip) contact model is implemented to simulate interactions between particle (pin) and geometry (disc). To quantify the wear from geometry surface, a sliding wear equation derived from Archard’s wear model is adopted in the DEM simulations. The accuracy of the pin-on-disc wear test simulation is assessed by comparing the predicted wear volume with that of the theoretical calculation. The stability is evaluated by repetitive tests of a reference case. At the steady-state wear, the sensitivity analysis is done by predicting sliding wear volumes using the parameter values determined by iron ore-handling conditions. This research is carried out using the software EDEM^® 2.7.1.

Numerical errors occur when a particle passes a joint side of geometry meshes. However, this influence is negligible compared to total wear volume of a wear revolution. A reference case study demonstrates that accurate and stable results of sliding wear volume can be achieved. For the sliding wear at steady state, increasing particle density or radius causes more wear, whereas, by contrast, particle Poisson’s ratio, particle shear modulus, geometry mesh size, rotating speed, coefficient of restitution and time step have no impact on wear volume. As expected, increasing indentation force results in a proportional increase. For maintaining wear characteristic and reducing simulation time, the geometry mesh size is recommended. To further reduce simulation time, it is inappropriate using lower particle shear modulus. However, the maximum time step can be increased to 187% T_R without compromising simulation accuracy.

The applied coefficient of sliding wear is determined based on theoretical and experimental studies of a spherical head of iron ore particle. To predict realistic volume loss in the iron ore-handling industry, this coefficient should be experimentally determined by taking into account the non-spherical shapes of iron ore particles.

The effects of DEM parameters on sliding wear are revealed, enabling the selections of adequate values to predict sliding wear in the iron ore-handling industry.

The accuracy and stability to predict sliding wear by using EDEM^® 2.7.1 are verified. Besides, this research accelerates the calibration of sliding wear prediction by DEM.

Today, most of the car manufacturers world-wide have embraced the principles of lean manufacturing on strategic and operational level. On strategic level car companies like Toyota (Womack et al., 1990) shifted 63 per cent of the value of the car towards the first, second and third tier suppliers for the co-production and co-development of cars as an effect of lean implementation. However, lean implementation was also followed by for instance Ford and GM in the USA, the latter company faced a sudden disruption in 2009 due to the break-out of the financial crisis in 2008, while Ford survived. Could this be foreseen? The exclusive use of (classic) financial performance indicators may give a false image of a company’s current and future performance. There is a need for a model to identify “the stars and the laggards’ regarding car companies by taking into account non-financial and intangible dimensions as advocated by Neely et al. (2003) regarding the third generation of business performance measurement systems. The purpose of this paper is therefor to propose a method to measure and benchmark car company performance which includes the non-financial R&D dimension as well as supply chain, value creating and employee dimensions. These dimensions are present in the value leverage model (van Blokland et al., 2012a, 2012b) which can serve as a basis for this method. The aim is to contribute to the third generation business performance measurement systems by further development of the value leverage model towards a maturity model for benchmarking car company performance. The proposed method can provide a big picture and give insight regarding company performance and direction of the performance.

Value leverage can be measured by a correlation analysis regarding three dimensions, namely, supply chain, R&D and value creation, all relative to the employee or capita which results in the average value leverage (AVL) factor. This AVL factor can be used to compose a combined relative and absolute ranking. The score regarding the AVL results in a relative ranking expressing the level of stability regarding the car companies value chain and system. For the absolute ranking the car companies receive per variable parameter a score according to their absolute performance relative to the other car companies. The relative and absolute ranking are presented on the vertical and horizontal axes forming a matrix. The matrix is the basis for the stability-value leverage maturity model for measuring and benchmarking company performance. With the proposed method, the following main research question can be answered: “How can company performance be measured and benchmarked from a stability-value leverage perspective?”.

With the proposed method, stability-value leverage performance can be measured. The relative ranking on the vertical axis and the absolute ranking form together a matrix which is presented by a scatterplot. A matrix with four maturity levels emerged from the analysis by introducing the average score of all the car companies together in the data set crossing the matrix vertical and horizontal. The four levels are as follows: Level I, low stability – low value leverage; Level II, low stability – high value leverage; Level III, high stability – low value leverage; and Level IV, high stability – high value leverage. Stability-value leverage performance of car companies can be measured over time which makes it possible to observe to which direction the car company migrates for instance from Level I to Level III, before and after the financial crises in 2008. The car companies BMW, Daimler, Audi, Ford and Honda are the best performing companies in stability-value leverage over the period 2000-2014, as they are situated at Level IV. With the findings, the main research question can be answered. The value leverage indicators can be used for measuring and benchmarking company performance regarding four maturity levels of stability and value leverage. The direction of performance can be observed as well.

This research is limited to the car industry. Further research is devised to test the indicators for instance on the truck manufacturing industry. Further research towards new variables is part of the ongoing research.

With the value leverage maturity model, it is possible to inform stakeholders about stability, value leverage and value creation capability of car companies. Weak performing companies can be identified in an early stage with this method to anticipate for instance on possible discontinuation of a car company effecting in merger an acquisition processes.

With the method stakeholders such as employees, users of cars and investors can be informed about how and why car companies perform in an unstable or stable manner.

This research towards ranking and classification of car companies aligns with theories regarding lean manufacturing and maturity models, as these models are used to compare companies on their level of perfection or excellence.

Bulk material-handling equipment development can be accelerated and is less expensive when testing of virtual prototypes can be adopted. However, often the complexity of the interaction between particulate material and handling equipment cannot be handled by a single computational solver. This paper aims to establish a framework for the development, verification and application of a co-simulation of discrete element method (DEM) and multibody dynamics (MBD).

The two methods have been coupled in two directions, which consists of coupling the load data on the geometry from DEM to MBD and the position data from MBD to DEM. The coupling has been validated thoroughly in several scenarios, and the stability and robustness have been investigated.

All tests clearly demonstrated that the co-simulation is successful in predicting particle–equipment interaction. Examples are provided describing the effects of a coupling that is too tight, as well as a coupling that is too loose. A guideline has been developed for achieving stable and efficient co-simulations.

This framework shows how to achieve realistic co-simulations of particulate material and equipment interaction of a dynamic nature.

The purpose of this paper is to develop an approach to measuring the performance of motor vehicle manufacturers (MVMs) from economic and environmental (E&E) perspectives.

Eight measures are identified for benchmarking the performance from E&E perspectives. A new company performance index I_MVM is constructed to quantitatively generate the historical data of MVMs’ company performance. Autoregressive integrated moving average (ARIMA) models are built to generate the forecast data of the I_MVM. The minimum Akaike information criteria value is used to identify the model of the best fit. Forecast accuracy of the ARIMA models is tested by the mean absolute percentage error.

The construction of the index I_MVM is benchmarked against three frameworks by six benchmark metrics. The I_MVM satisfies all of its applicable metrics while the three frameworks are incapable to satisfy their applicable metrics. Out of 15, 4 MVMs are excluded for benchmarking future performance due to their non-stationary time series data. Based on the forecast I_MVM data, GM is the best performer among the 15 samples in the FY2018.

This research highlights the environmental perspective during vehicles’ production. The development of this approach is based on publicly available data and transparent about the methods it used. The data out of the approach can benefit stakeholders with insights by benchmarking the historical performance of MVMs as well as their future performance.

Current literature presents limited measurement methods of quantifying manufacturers' performance with environmental concerns. The purpose of this paper is to construct a company performance index for benchmarking motor vehicle manufacturers (MVMs) with environmental concerns.

Methods of constructing the index include regression analysis, a modified linear method for normalizing variables and a geometric mean for aggregating variables into a single index I_MVM (index for MVMs). A case study is conducted in 12 MVMs from 2008 to 2017. A sensitivity analysis with the simple additive weighting method is performed to analyze how different aggregation methods affect the final value. The index I_MVM is assessed through a benchmark with three existing indices.

Three realistic considerations are identified from MVMs, based on which proper and transparent methods are chosen to construct the I_MVM. The construction of the index I_MVM has been assessed through a benchmark against the methodologies of three other indices. The results indicate that the new measurement is feasible and effective for MVMs to measure their company performance from an environmental perspective.

The construction of the index I_MVM can support policymakers with accurate statistics for decision-making. As a response to current imperative climate policies, this paper raises awareness of CO₂ emissions in vehicles' production. For statistical organizations and stakeholders in the investment world, this paper provides available and reliable statistics for trend analysis of different MVMs.

A new method is designed for constructing a company performance index for MVMs. Three environmental variables are identified based on literature, their environmental impact as well as their data availability from public documents. A ranking by manufacturer with environmental concerns is generated. This index can contribute with available statistics and useful insights toward decision-making.

The tensile test is one of the fundamental experiments used to evaluate material properties. Simulating a tensile test can be a replacement of experiments to determine mechanical parameters of a continuous material. The paper aims to discuss these issues.

This research uses a new approach to model a tensile test of a high-carbon steel on the basis of discrete element method (DEM). In this research, the tensile test specimen was created by using a DEM packing theory. The particle-particle bond model was used to establish the internal forces of the tensile test specimen. The particle-particle bond model was first tested by performing two-particle tensile test, then was adopted to simulate tensile tests of the high-carbon steel by using 3,678 particles.

This research has successfully revealed the relationships between the DEM parameters and mechanical parameters by modelling a tensile test. The parametric study demonstrates that the particle physical radius, particle contact radius and bond disc radius can significantly influence ultimate stress and Young’s modulus of the specimen, whereas they slightly impact elongation at fracture. Increasing the normal and shear stiffness, the critical normal and shear stiffness can enable the increase of ultimate stress, however, up to maximum values.

To improve the particle-particle bond model to simulate a tensile test for high-carbon steel, the damping factors for compensating energy loss from transition of particle motions and failure of bonds are required.

This work reinforces the knowledge of applying DEM to model continuous materials.

This research illustrates a new approach to model a tensile test of a high-carbon steel on the basis of DEM.

The macroscopic properties of dried sand can be correctly modelled when the accurate determination of the microscopic properties is available. The microscopic properties between the particles such as the coefficients of rolling (µ _r) and sliding (µ _s), are numerically determined in two different ways: with and without considering the fluid effect. In an earlier study, the microscopic properties were determined by discrete element method (DEM) and without considering the air effect on the macroscopic properties such as the Angle of Repose. The purpose of this paper is to recalibrate the microscopic properties through a coupling between the DEM and computational fluid dynamics (CFD).

The first step is dedicated to the calibration of the CFD-DEM model through modelling a single particle sedimentation within air, water, and silicon oil. The voidage and drag models, the grid size ratio (D/dx), the domain size ratio (W/D), and the optimum coupling interval between the CFD and DEM were investigated through comparing the CFD-DEM results with the analytical solution and experimental data. The next step is about modelling an Hourglass with the calibrated CFD-DEM model to recalibrate the µ _r and µ _s of dried sand particles.

It was concluded that the air has a minor effect on the macroscopic properties of the dried sand and the µ _r and µ _s that were obtained with the DEM can be utilized in the CFD-DEM simulation.

Utilizing the granulometry of dried quartz sand in the calibration process of the CFD-DEM method has raised the possibility of using the µ _r and µ _s for other applications in future studies.

This study aims to present an overview of leading research trends in the lean six sigma domain published in the International Journal of Lean Six Sigma (IJLSS) since its inception.

The study analyses articles published between 2010 and 2019 in IJLSS using the bibliometric technique. The results of data analysis identify the most prolific authors, their affiliation, citation trends and highly cited articles from the journal. Further, a graphical analysis involving bibliographic coupling and co-citation analysis of the corpus enriches the investigation.

The results of the bibliometric analysis suggest that the number of IJLSS’s publications and citations grew markedly over time (from 4 citations in 2010 to nearly 1,324 in 2019). The organizational diversity and collaboration among authors publishing in IJLSS are trending upwards. Case study and focus group are the two most common research designs in publications. In the study, three major themes emerged: implementation of lean on business, integration of lean and six sigma and the effects of lean six sigma on businesses.

The study finding informs and educates practitioners and scholars about various qualitative research tools, applications and methods of implementing lean six sigma tools in different industry sectors.

The study uses bibliometric analysis to propose a novel categorization of research published in IJLSS and to report on the utilization of various lean tools in the journal. The study provides guidance for new future research besides offering a thorough introspection of the lean and six sigma domains.