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Pipe route design plays a prominent role in ship design. Due to the complex configuration in layout space with numerous pipelines, diverse design constraints, and obstacles, it is a complicated and time-consuming process to obtain the optimal route of ship pipes. In this article, an optimized design method for branch pipe routing is proposed to improve design efficiency and to reduce human errors. By simplifying equipment and ship hull models and dividing workspace into three-dimensional grid cells, the mathematic model of layout space is constructed. Based on the proposed concept of pipe grading method, the optimization model of pipe routing is established. Then an optimization procedure is presented to deal with pipe route planning problem by combining maze algorithm (MA), nondominated sorting genetic algorithm II (NSGA-II), and cooperative coevolutionary nondominated sorting genetic algorithm II (CCNSGA-II). To improve the performance in genetic algorithm procedure, a fixed-length encoding method is presented based on improved maze algorithm and adaptive region strategy. Fuzzy set theory is employed to extract the best compromise pipeline from Pareto optimal solutions. Simulation test of branch pipe and design optimization of a fuel piping system were carried out to illustrate the design optimization procedure in detail and to verify the feasibility and effectiveness of the proposed methodology.

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Measured load data play a crucial role in the fatigue durability analysis of mechanical structures. However, in the process of signal acquisition, time domain load signals are easily contaminated by noise. In this paper, a signal denoising method based on variational mode decomposition (VMD), wavelet threshold denoising (WTD), and singular spectrum analysis (SSA) is proposed. Firstly, a simple criterion based on mutual information entropy (MIE) is designed to select the proper mode number for VMD. Detrended fluctuation analysis (DFA) is adopted to obtain the noise level of the noisy signal, which can optimize the selection of MIE threshold. Meanwhile, the noisy signal is adaptively decomposed into band-limited intrinsic mode functions (BLIMFs) by using VMD. In addition, weighted-permutation entropy (WPE) is applied to divide the BLIMFs into signal-dominant BLIMFs and noise-dominant BLIMFs. Then, the signal-dominant BLIMFs are reconstructed with the noise-dominant BLIMFs processed by WTD. Finally, SSA is implemented for the reconstructed signal. Experimental results of synthetic signals demonstrate that the presented method outperforms the conventional digital signal denoising methods and the related methods proposed recently. Effectiveness of the proposed method is verified through experiments of the measured load signals.

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Permanent WRAP url:http://wrap.warwick.ac.uk/78027 Copyright and reuse:The Warwick Research Archive Portal (WRAP) makes this work by researchers of the University of Warwick available open access under the following conditions. Copyright © and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable the material made available in WRAP has been checked for eligibility before being made available.Copies of full items can be used for personal research or study, educational, or not-forprofit purposes without prior permission or charge. Provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. A note on versions:The version presented here may differ from the published version or, version of record, if you wish to cite this item you are advised to consult the publisher's version. Please see the 'permanent WRAP url' above for details on accessing the published version and note that access may require a subscription. Abstract: Accurate and rapid modeling and performance evaluation over the entire workspace is a crucially important issue in the design optimization of parallel kinematic machines (PKMs), especially for those dedicated for high-speed machining where high rigidity and high dynamics are the essential requirements. By taking a 3-DOF spindle head named A3 head as an example, this paper presents a feature-based CAD-CAE integration methodology for the static and dynamic analyses of PKMs. The approach can be implemented by four steps: (1) creation of a parameterized geometric (CAD) model with analysis features in SolidWorks; (2) extraction of the features from the CAD model using the Application Programming Interface (API) available in SolidWorks; (3) formulation of a CAD model in SAMCEF by mapping the configuration features from SolidWorks to SAMCEF; and (4) conversion of the analysis features into a scripting language named Bacon for Finite Element Analysis (FEA). The merit of this approach lies in that the FE model at different configurations can be updated automatically in batch mode, and PKM having different topologies can be modeled with ease thanks to the down to link/joint level featuring. The experiment is also carried out to verify the effectiveness of the proposed approach.Keywords: Parallel kinematic machines, CAD-CAE Integration, Performance evaluation, Finite Element Analysis IntroductionStatic and dynamic compliances are the significant performance indices of parallel kinematic machines (PKMs) [1-2] especially for those dedicated to implementing high-speed machining and forced assembly, etc., where high rigidity and high dynamics are crucially required. In order to avoid time and cost-intensive manufacturing and testing of physical prototypes, it is preferable to employ "virtual prototyping" technology [3][4][5] such that a computer simulation model can be built, ana...

Parallel kinematic machines have been applied in aerospace and automotive manufacturing due to their potentials in high speed and high accuracy. However, there exists coupling in parallel kinematic machines, which makes dynamic analysis, rigidity enhancement, and control very complicated. In this article, coupling characteristics of a 5-degree-of-freedom (5-dof) hybrid manipulator are analyzed based on a local index and a global index. First, velocity analysis as well as acceleration analysis of the robot is conducted to provide essential information for dynamic modeling. Then the dynamic model is built based on the principle of virtual work. Whereas the mass matrix is off-diagonal, a local coupling index as well as a global index is defined, based on which coupling characteristics of the robot are analyzed. Results show that distributions of coupling indices are symmetric due to its structural features. And dimensional parameters, structural parameters, as well as mass parameters have a large influence on the system’s coupling characteristics. Research conducted in the article is of great help in optimal design and control. Meanwhile, the method proposed in the article can be applied to other types of parallel kinematic machines or hybrid manipulators.