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- 实验方案
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White light interferometry and MountainsMap? - case studies in static load capacity of bearings and surface finish optimisation of orthotic knee joints
摘要： This paper deals with two case studies demonstrating the combined use of white light interferometry and MountainsMap? software to: 1) accurately measure indentation diameters on race elements with an accuracy of a 1/100th of a micrometer and to examine static load capacity of bearings compared to the industry standard. In this case study, static load capacity of the ball bearings was shown to be 31% lower than that specified by ISO and ANSI/ABMA bearing standards; 2) characterise machined surfaces with 2D and 3D parameters, with the goal of producing optimal surface finish on orthotic knee joints used by Cerebral Palsy patients. In this case study, 2D roughness parameters Ra, Rq, Rp, Rz, and 3D roughness parameters Sa, Sq, Sp, Sz, were improved by 34%, 31%, 8%, 20%, and 36%, 33%, 6%, 6% respectively. Both case studies demonstrate the significance of modern topography techniques in enhancing the measurement accuracy of surface characteristics.
关键词： robust design,MountainsMap?,surface roughness,signal-to-noise (S/N) ratio,interferometry,WLI,static load capacity,white light,ball bearing indentations
更新于2025-09-23 15:22:29
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Integrating WLI fuzzy clustering with grey neural network for missing data imputation
摘要： This paper proposes a novel approach, grey neural network (GNN) that is composed of Levenberg-Marquardt neural network and grey wolf optimiser. The WLI fuzzy clustering mechanism predicts the data by clustering the data into groups, and the neural network trains the missing attribute in the dataset. The Levenberg-Marquardt neural network is trained based on the grey wolf optimiser that determines the optimal weight. Finally, the two imputed values are combined significantly to impute the data where the missing data occurs. Experimentation using the medical dataset proves the accuracy of the proposed hybrid model and the results of the proposed GNN are compared with the existing methods like KNN, WLI and GWLMN. The proposed method exhibits a good efficiency with minimum values of MSE and RMSE compared to the existing methods. This method also attains a minimum RMSE of 0.11 which ensures the efficient data imputation.
关键词： neural network,grey wolf optimiser,data imputation,missing data,WLI fuzzy clustering
更新于2025-09-09 09:28:46