Fixture layout optimization of large compliant ship part assembly for reducing and straightening butt clearance

IF 2.2 3区工程技术 Q2 ENGINEERING, MULTIDISCIPLINARY

Engineering Optimization Pub Date : 2023-10-05 DOI:10.1080/0305215x.2023.2258067

Ge Hong, Shuo Gao, Tangbin Xia, Juan Du, Xuancheng Jin, Ershun Pan, Lifeng Xi

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引用次数: 0

Abstract

AbstractFixture layout is an essential factor in intelligent manufacturing in the shipbuilding industry, affecting assembly quality and efficiency. Its optimization has become an urgent problem in the assembly process of compliant parts. Traditional fixture layout depends on workers’ experience, which leads to butt clearance along the assembly interface. Thus, this article proposes a butt clearance control-oriented fixture layout optimization (BCCFLO) methodology. First, a method for calculating the part dimensional variation under the influence of fixture layout and fixture locating error is developed. Then, a constrained multi-objective integer nonlinear programming (CMINP) model is innovatively formulated to optimize the fixture layout. Furthermore, the non-dominated sorting genetic algorithm-II based on Latin hypercube sampling is designed to address the CMINP model. The case study illustrates and validates that the fixture layout achieved by the proposed method could significantly control the butt clearance for large compliant part assembly in the shipbuilding industry.KEYWORDS: Compliant part assemblyfixture layout optimizationbutt clearance controlfinite element equationlarge ship panels AcknowledgementsThis research is supported by the National Key Research and Development Program of China [2022YFF0605700], National Natural Science Foundation of China [51875359 and 72001139], Natural Science Foundation of Shanghai [20ZR1428600], CSSC-SJTU Marine Equipment Forward Looking Innovation Foundation [22B010432] and Oceanic Interdisciplinary Program of Shanghai Jiao Tong University [SL2021MS008].Disclosure statementNo potential conflict of interest was reported by the authors.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article.Additional informationFundingThis work was supported by the National Key Research and Development Program of China [grant number 2022YFF0605700]; National Natural Science Foundation of China [grant number 51875359]; Natural Science Foundation of Shanghai Municipality [grant number 20ZR1428600]; CSSC-SJTU Marine Equipment Forward Looking Innovation Foundation [grant number 22B010432]; Oceanic Interdisciplinary Program of Shanghai Jiao Tong University [grant number SL2021MS008].

查看原文本刊更多论文

减小和矫直对接间隙的大型柔性船舶零件装配夹具布局优化

摘要夹具布局是船舶智能制造的关键因素，影响着装配质量和效率。其优化已成为柔性零件装配过程中亟待解决的问题。传统的夹具布局取决于工人的经验，这导致沿装配界面对接间隙。因此，本文提出了一种面向对接间隙控制的夹具布局优化(BCCFLO)方法。首先，提出了一种计算夹具布局和夹具定位误差影响下零件尺寸变化的方法。然后，创新地建立了约束多目标整数非线性规划(CMINP)模型来优化夹具布局。针对CMINP模型，设计了基于拉丁超立方采样的非支配排序遗传算法- ii。实例研究表明，通过该方法得到的夹具布局可以有效地控制船舶工业中大型柔性零件装配的对接间隙。关键词:本研究得到国家重点研发计划项目[2022YFF0605700]，国家自然科学基金项目[51875359和72001139]，上海市自然科学基金项目[20ZR1428600]，中国科学院-上海交通大学海洋装备前瞻性创新基金[22B010432]和上海交通大学海洋学科交叉项目[SL2021MS008]。披露声明作者未报告潜在的利益冲突。数据可用性声明作者确认在文章中可以获得支持本研究结果的数据。项目资助:国家重点研发计划项目[批准号2022YFF0605700];国家自然科学基金项目[批准号51875359];上海市自然科学基金[批准号20ZR1428600];csc -交大船舶装备前瞻性创新基金[批准号22B010432];上海交通大学海洋学科交叉项目[批准号:SL2021MS008]。

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来源期刊

Engineering Optimization 管理科学-工程：综合

CiteScore

5.90

自引率

7.40%

发文量

审稿时长

3.5 months

期刊介绍： Engineering Optimization is an interdisciplinary engineering journal which serves the large technical community concerned with quantitative computational methods of optimization, and their application to engineering planning, design, manufacture and operational processes. The policy of the journal treats optimization as any formalized numerical process for improvement. Algorithms for numerical optimization are therefore mainstream for the journal, but equally welcome are papers which use the methods of operations research, decision support, statistical decision theory, systems theory, logical inference, knowledge-based systems, artificial intelligence, information theory and processing, and all methods which can be used in the quantitative modelling of the decision-making process. Innovation in optimization is an essential attribute of all papers but engineering applicability is equally vital. Engineering Optimization aims to cover all disciplines within the engineering community though its main focus is in the areas of environmental, civil, mechanical, aerospace and manufacturing engineering. Papers on both research aspects and practical industrial implementations are welcomed.