Search results

The purpose of this paper is to use de Sitter's design theory to show how organizational structures can be designed so as to attenuate organizational disturbances and amplify regulatory potential. It is argued that organizational structures with low values on so‐called design‐parameters are themselves no source of disturbances and have the required built‐in regulatory potential.

Key concepts from de Sitter's design theory are introduced and used to show how structures can attenuate disturbances and amplify regulatory potential.

The analysis in this paper deepens our understanding of the role of organizational structures for dealing with organizational complexity, and of the design parameters that should be manipulated to achieve structural attenuation and amplification.

Having a structure permitting organizations to attenuate and amplify is a crucial condition for organizational viability. This paper provides guidelines for the design of such structures.

This is one of a limited number of studies that makes apparent how general insights from (management) cybernetic (e.g. viability, attenuation and amplification) may be realized in organizations by their structural design.

Choice under risk has a large stochastic (unpredictable) component. This chapter examines five stochastic models for binary discrete choice under risk and how they combine with “structural” theories of choice under risk. Stochastic models are substantive theoretical hypotheses that are frequently testable in and of themselves, and also identifying restrictions for hypothesis tests, estimation and prediction. Econometric comparisons suggest that for the purpose of prediction (as opposed to explanation), choices of stochastic models may be far more consequential than choices of structures such as expected utility or rank-dependent utility.

This paper aims to overcome the problem that the single structure of the driving term of the grey prediction model is not adapted to the complexity and diversity of the actual modeling objects, which leads to poor modeling results.

Firstly, the nonlinear law between the raw data and time point is fully mined by expanding the nonlinear term and the range of order. Secondly, through the synchronous optimization of model structure and parameter, the dynamic adjustment of the model with the change of the modeled object is realized. Finally, the objective optimization of nonlinear driving term and cumulative order of the model is realized by particle swarm optimization PSO algorithm.

The model can achieve strong compatibility with multiple existing models through parameter transformation. The synchronous optimization of model structure and parameter has a significant improvement over the single optimization method. The new model has a wide range of applications and strong modeling capabilities.

A novel grey prediction model with structure variability and optimizing parameter synchronization is proposed.

The highlights of the paper are as follows:

1. A new grey prediction model with a unified nonlinear structure is proposed.

2. The new model can be fully compatible with multiple traditional grey models.

3. The new model solves the defect of poor adaptability of the traditional grey models.

4. The parameters of the new model are optimized by PSO algorithm.

5. Cases verify that the new model outperforms other models significantly.

A new grey prediction model with a unified nonlinear structure is proposed.

The new model can be fully compatible with multiple traditional grey models.

The new model solves the defect of poor adaptability of the traditional grey models.

The parameters of the new model are optimized by PSO algorithm.

Cases verify that the new model outperforms other models significantly.

This paper aims to focus on studying the influence of gradient parameters, namely, thickness coefficient, length coefficient and height ratio of auxetic structure on responses such as strength, stiffness and specific energy absorption (SEA) under compressive loading. Optimization of significant parameters is also performed to maximize responses. Further, efforts have also been made to develop regression models for strength, stiffness and SEA of auxetic structure.

Central composite design of response surface methodology is used for planning experiments. Auxetic structures of acrylonitrile butadiene styrene (ABS) and poly-lactic acid (PLA) materials are fabricated by the material extrusion (ME) technique of additive manufacturing. Fabricated structures are tested under in-plane uniaxial compressive loading. Grey relational analysis is used for the optimization of gradient parameters of the unit cell of auxetic structure to maximize responses and minimize weight and time of fabrication.

From the analysis of variance of experimental data, it is found that the compressive strength of auxetic structures increases with a decrease in length coefficient and height ratio. In the case of ABS structures, stiffness increases with a decrease in thickness coefficient and length coefficient, while in the case of PLA structures, stiffness increases with a decrease in length coefficient and height ratio. SEA is influenced by length coefficient and thickness coefficient in ABS and PLA structures, respectively. Based on the analysis, statistical non-linear quadratic models are developed to predict strength, stiffness and SEA. Optimal configuration of auxetic structure is determined to maximize strength, stiffness, SEA and minimize weight and time of fabrication.

The present study is limited to re-entrant type of auxetic structures made of ABS and PLA materials only under compressive loading. Also, results from the current study are valid within a selected range of gradient parameters. The findings of the present study are useful in the optimal selection of gradient parameters for the fabrication of auxetic structures of maximum strength, stiffness and SEA with minimum weight and time of fabrication. These outcomes have wide applications in domains such as automotive, aerospace, sports and marine sectors.

Limited literature is available on studying the influence of gradient parameters of ME manufactured auxetic structure of ABS and PLA materials on responses, namely, strength, stiffness and SEA under compressive loading. Also, no work has been reported on studying the influence of gradient parameters on mechanical properties, weight and time of fabrication of auxetic structures. The present study is an attempt to fulfil the above research gaps.

This paper seeks to develop an approach to problem localization and an algorithm to address the issue of determining the dependencies among system metrics for automated system management in ubiquitous computing systems.

This paper proposes an approach to problem localization for learning the knowledge of dynamic environment using probabilistic dependency analysis to automatically determine problems. This approach is based on Bayesian learning to describe a system as a hierarchical dependency network, determining root causes of problems via inductive and deductive inferences on the network. An algorithm of preprocessing is performed to create ordering parameters that have close relationships with problems.

The findings show that using ordering parameters as input of network learning, it reduces learning time and maintains accuracy in diverse domains especially in the case of including large number of parameters, hence improving efficiency and accuracy of problem localization.

An evaluation of the work is presented through performance measurements. Various comparisons and evaluations prove that the proposed approach is effective on problem localization and it can achieve significant cost savings.

This study contributes to research into the application of probabilistic dependency analysis in localizing the root cause of problems and predicting potential problems at run time after probabilities propagation throughout a network, particularly in relation to fault management in self‐managing systems.

This paper aims to develop a set of process parameters tailored for lattice structures and test them against standard process (SP) parameters. Selective laser melting (SLM) is a commonly known and established additive manufacturing technique and is a key technology in generating intricately shaped lattice structures. However, SP parameters used in this technology have building time and accuracy disadvantages for structures with a low area-to-perimeter ratio, such as thin struts.

In this research work, body-centred cubic structure specimens are manufactured using adapted process parameters. Central to the adapted process parameters is the positioning of the laser beam, the scan strategy and the linear energy density. The specimens are analysed with X-ray micro-computed tomography for dimensional accuracy. The final assessment is a comparison between specimens manufactured using adapted process parameters and those using SP parameters.

Standard parameters for lattice structures lead to a significant shift from the nominal geometry. An extensive manufacturing and computation time due to several exposure patterns (e.g. pre-contours, post-contours) was observed. The tailored process parameters developed had good dimensional accuracy, reproducible results and improved manufacturing performance.

The results are based on a distinctive geometry of the lattice structure and a specific material. Future research should be extended to other geometries and materials.

Optimisation of process parameters for the part geometry is a critical factor in improving dimensional accuracy and performance of SLM processes.

This study demonstrates how application-tailored process parameters can lead to superior performance and improved dimensional accuracy. The results can be transferred to other lattice structure designs and materials.

This paper aims to present ball bearings with a composite structure based on the bionics principle and shows the comparison between five types of different structures.

By means of the finite element method, the stress and other parameters between different structures are compared and verified. Finally, the comprehensive parameters of different structures are evaluated by the analytic hierarchy process method.

The evaluation of the comprehensive parameters of five types of structures is shown here.

The value of this paper is calculated and compared to the parameters of five types of different structures, and the parameter score evaluation of each structure is given. Different structures can be selected according to different parameter requirements, which to provide a theoretical basis for the design of ball bearings.

The peer review history for this article is available at: https://publons.com/publon10.1108/ILT-10-2019-0413

The purpose of this paper is to establish an adaptive assembly, to realize the adaptive changing of the models and to improve the flexibility and reliability of assembly change. For a three-dimensional (3D) computer-aided design (CAD) assembly in a changing process, there are two practical problems. One is delivering parameters’ information not smoothly. The other one is to easily destroy an assembly structure.

The paper establishes associated parameters design structure matrix of related parts, and predicts possible propagation paths of the parameters. Based on the predicted path, structured storage is made for the affected parameters, tolerance range and the calculation relations. The study combines structured path information and all constrained assemblies to build the adaptive assembly, proposes an adaptive change algorithm for assembly changing and discusses the extendibility of the adaptive assembly.

The approach would improve the flexibility and reliability of assembly change and be applied to different CAD platform.

The examples illustrate the construction and adaptive behavior of the assembly and verify the feasibility and reasonability of the adaptive assembly in practical application.

The adaptive assembly model proposed in the paper is an original method to assembly change. And compared with other methods, good results have been obtained.

The manufacturability of extremely fine porous structures in the SLM process has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. The research objective is to find out the process limitation and key processing parameters for printing fine porous structures so as to give reference for design and manufacturing planning.

In metallic AM processes, the difficulty of geometric modeling and manufacturing of structures with pore sizes less than 350 μm exists. The manufacturability of porous structures in selective laser melting (SLM) has rarely been investigated, leading to unpredicted manufacturing results and preventing steady medical or industrial application. To solve this problem, a comprehensive experimental study was conducted to benchmark the manufacturability of the SLM process for extremely fine porous structures (less than 350 um and near a limitation of 100 um) and propose a manufacturing result evaluation method. Numerous porous structure samples were printed to help collect critical datasets for manufacturability analysis.

The results show that the SLM process can achieve an extreme fine feature with a diameter of 90 μm in stable process control, and the process parameters with their control strategies as well as the printing process planning have an important impact on the printing results. A statistical analysis reveals the implicit complex relations between the porous structure geometries and the SLM process parameter settings.

It is the first time to investigate the manufacturability of extremely fine porous structures of SLM. The method for manufacturability analysis and printing parameter control of fine porous structure are discussed.

Laser sintering of polyamide lattice-based lightweight fairing components for subsequent racetrack testing requires a high quality and a reliable design. Hence, the purpose of this study was to develop a design methodology for such additively manufactured prototypes, considering efficient generation and structural simulation of boundary conformal non-periodic lattices, optimization of production parameters as well as experimental validation.

Multi-curved, sandwich structure-based demonstrators were designed, simulated and experimentally tested with boundary conformal lattice cells. The demonstrator’s non-periodic lattice cells were simplified by forward homogenization processes. To represent the stiffness of the top and bottom face sheet, constant isotropic and mapped transversely isotropic simulation approaches were compared. The dimensional accuracy of lattice cells and demonstrators were measured with a gauge caliper and a three-dimensional scanning system. The optimized process parameters for lattice structures were transferred onto a large volume laser sintering system. The stiffness of each finite element analysis was verified by an experimental test setup including a digital image correlation system.

The stiffness prediction of the mapped was superior to the constant approach and underestimated the test results with −6.5%. Using a full scale fairing the applicability of the development process was successfully demonstrated.

The design approach elaborated in this research covers aspects from efficient geometry generation over structural simulation to experimental testing of produced parts. This methodology is not only relevant in the context of motor sports but is transferrable for all additively manufactured large scale components featuring a complex lattice sub-structure and is, therefore, relevant across industries.