The Experts below are selected from a list of 21084 Experts worldwide ranked by ideXlab platform
Hoda A Elmaraghy - One of the best experts on this subject based on the ideXlab platform.
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optimum Granularity Level of modular product design architecture
Cirp Annals-manufacturing Technology, 2013Co-Authors: Tarek Algeddawy, Hoda A ElmaraghyAbstract:Abstract In modular architectures, Design Structure Matrix (DSM) is used to cluster product components into modules with minimum interfaces externally and maximum internal integration between components. However, DSM is a flat connectivity map that does not capture the layered nature of the product structure. Hierarchical clustering (cladistics) is proposed to automatically build product hierarchical architecture from DSM. The resulting clustering tree represents product architecture while its depth represents its Granularity. The optimum Granularity Level and number of modules are determined, indicating the potential product and process platforms. A case study of automobile body-in-white of 38 components is used to demonstrate the capabilities and superior results quality of the presented technique.
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determining Granularity Level in product design architecture
2013Co-Authors: Tarek Algeddawy, Hoda A ElmaraghyAbstract:Product architecture represents components grouped into modules that can be assembled later to constitute a specific variant. Literature provides Methods of clustering components into weakly related modules with strong interconnections between components within modules. The number of modules and their hierarchical relationships shape product architecture and determine the balance between modular design and components integration. A novel hierarchical clustering approach, based on the biological Cladistics analysis, has been developed to cluster Design Structure Matrix (DSM) widely used to promote modularity. It evaluates different Granularity Levels of the resulting hierarchy and finds the best Granularity Level for maximum modularity. An automotive Body-in-White of 38 different components is used as a case study. Results showed the superiority of the recommended modularity pattern and synthesized product architecture over other clustering techniques.
Tarek Algeddawy - One of the best experts on this subject based on the ideXlab platform.
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optimum Granularity Level of modular product design architecture
Cirp Annals-manufacturing Technology, 2013Co-Authors: Tarek Algeddawy, Hoda A ElmaraghyAbstract:Abstract In modular architectures, Design Structure Matrix (DSM) is used to cluster product components into modules with minimum interfaces externally and maximum internal integration between components. However, DSM is a flat connectivity map that does not capture the layered nature of the product structure. Hierarchical clustering (cladistics) is proposed to automatically build product hierarchical architecture from DSM. The resulting clustering tree represents product architecture while its depth represents its Granularity. The optimum Granularity Level and number of modules are determined, indicating the potential product and process platforms. A case study of automobile body-in-white of 38 components is used to demonstrate the capabilities and superior results quality of the presented technique.
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determining Granularity Level in product design architecture
2013Co-Authors: Tarek Algeddawy, Hoda A ElmaraghyAbstract:Product architecture represents components grouped into modules that can be assembled later to constitute a specific variant. Literature provides Methods of clustering components into weakly related modules with strong interconnections between components within modules. The number of modules and their hierarchical relationships shape product architecture and determine the balance between modular design and components integration. A novel hierarchical clustering approach, based on the biological Cladistics analysis, has been developed to cluster Design Structure Matrix (DSM) widely used to promote modularity. It evaluates different Granularity Levels of the resulting hierarchy and finds the best Granularity Level for maximum modularity. An automotive Body-in-White of 38 different components is used as a case study. Results showed the superiority of the recommended modularity pattern and synthesized product architecture over other clustering techniques.
Kevin Galvin - One of the best experts on this subject based on the ideXlab platform.
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identifying optimal Granularity Level of modular assembly supply chains based on complexity modularity trade off
IEEE Access, 2021Co-Authors: Bugra Alkan, Seth Bullock, Kevin GalvinAbstract:Complexity has been argued to limit operational efficiency, hinder decision-making and induce disruption in supply chain networks. The main aim of this paper is to investigate the architectural trade-off between complexity and modularity in modular assembly supply chain networks. Towards this, an information-entropic complexity model is developed and applied to the domain of assembly supply chains and logistics. This approach characterises complexity as a combination of the intrinsic complexity of the system modules/interfaces and the influence of the topological composition of the network. The model is then used within an optimisation framework, where the optimal Granularity Level for assembly supply chain design solutions for a given assembly product can be automatically verified by considering the trade-off between complexity and network modularity. It is concluded that the proposed methodology could help to minimise the complexity of supply chain assembly configurations while maximising their modularity and thereby help to increase both the reliability and performance of supply chain networks.
Shin'ichi Warisawa - One of the best experts on this subject based on the ideXlab platform.
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A physiology-based approach for estimation of mental fatigue Levels with both high time resolution and high Level of Granularity
'Elsevier BV', 2021Co-Authors: Shinji Nakatsuru, Yuki Ban, Rui Fukui, Shin'ichi WarisawaAbstract:Mental fatigue (MF) monitoring is essential for eliminating accidents in high-risk tasks and providing better productivity management in daily work tasks involving human operators. Previous works only built MF monitoring systems with either high time resolution or high Granularity Level. We proposed a physiological-based approach to estimate MF Level every 2 seconds in a regression manner, a system that realized both high time resolution and high Granularity Level. The approach consists of an accurate MF Level assessment method using the alignment score within a modified N-back task that is free from the lure effect, a long short-term memory (LSTM) deep learning framework, and a performance-reliability validation process. We used multiple physiological signals including ECG, respiration, and pupil diameter. As a result, we provided feasible estimating performance not worse than existing studies while realizing both high time resolution and high Level of Granularity. The interpretation analysis utilized accumulated local effects (ALE) to illustrate how black-box models make estimations and to improve the reliability of this approach
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a physiology based approach for estimation of mental fatigue Levels with both high time resolution and high Level of Granularity
Informatics in Medicine Unlocked, 2021Co-Authors: Shinji Nakatsuru, Yuki Ban, Rui Fukui, Shin'ichi WarisawaAbstract:Abstract Mental fatigue (MF) monitoring is essential for eliminating accidents in high-risk tasks and providing better productivity management in daily work tasks involving human operators. Previous works only built MF monitoring systems with either high time resolution or high Granularity Level. We proposed a physiological-based approach to estimate MF Level every 2 seconds in a regression manner, a system that realized both high time resolution and high Granularity Level. The approach consists of an accurate MF Level assessment method using the alignment score within a modified N-back task that is free from the lure effect, a long short-term memory (LSTM) deep learning framework, and a performance-reliability validation process. We used multiple physiological signals including ECG, respiration, and pupil diameter. As a result, we provided feasible estimating performance not worse than existing studies while realizing both high time resolution and high Level of Granularity. The interpretation analysis utilized accumulated local effects (ALE) to illustrate how black-box models make estimations and to improve the reliability of this approach.
Bugra Alkan - One of the best experts on this subject based on the ideXlab platform.
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identifying optimal Granularity Level of modular assembly supply chains based on complexity modularity trade off
IEEE Access, 2021Co-Authors: Bugra Alkan, Seth Bullock, Kevin GalvinAbstract:Complexity has been argued to limit operational efficiency, hinder decision-making and induce disruption in supply chain networks. The main aim of this paper is to investigate the architectural trade-off between complexity and modularity in modular assembly supply chain networks. Towards this, an information-entropic complexity model is developed and applied to the domain of assembly supply chains and logistics. This approach characterises complexity as a combination of the intrinsic complexity of the system modules/interfaces and the influence of the topological composition of the network. The model is then used within an optimisation framework, where the optimal Granularity Level for assembly supply chain design solutions for a given assembly product can be automatically verified by considering the trade-off between complexity and network modularity. It is concluded that the proposed methodology could help to minimise the complexity of supply chain assembly configurations while maximising their modularity and thereby help to increase both the reliability and performance of supply chain networks.
