Developing a selective assembly technique for sheet metal assemblies

被引:34
作者
Aderiani, Abolfazl Rezaei [1 ]
Warmefjord, Kristina [1 ]
Soderberg, Rikard [1 ]
Lindkvist, Lars [1 ]
机构
[1] Chalmers Univ Technol, Dept Ind & Mat Sci, Gothenburg, Sweden
关键词
selective assembly; digital twin; sheet metal assembly; computer-aided tolerancing; GENETIC ALGORITHM; SURPLUS PARTS; OPTIMIZATION; COMPONENTS; TOLERANCE;
D O I
10.1080/00207543.2019.1581387
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
Applying the concept of Digital Twin in production processes supports the manufacturing of products of optimal geometry quality. This concept can be further supported by a strategy of finding the optimal combination of individual parts to maximise the geometrical quality of the final product, known as selective assembly technique. However, application of this technique has been limited to assemblies where the final dimensions are just function of the mating parts' dimensions and this is not applicable in sheet metal assemblies. This paper develops a selective assembly technique for sheet metal assemblies and investigates the effect of batch size on the improvements. The presented method utilises a variation simulation tool (Computer-Aided Tolerancing tool) and an optimisation algorithm to find the optimal combination of the mating parts. The approach presented is applied to three industrial cases of sheet metal assemblies. The results show that using this technique leads to a considerable reduction of the final geometrical variation and mean deviation for these kinds of assemblies. Moreover, increasing the batch size reduces the amount of achievable improvement in variation but increases the amount of achievable improvement in the mean deviation.
引用
收藏
页码:7174 / 7188
页数:15
相关论文
共 34 条
[1]   A Multistage Approach to the Selective Assembly of Components Without Dimensional Distribution Assumptions [J].
Aderiani, Abolfazl Rezaei ;
Warmefjord, Kristina ;
Soderberg, Rikard .
JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME, 2018, 140 (07)
[2]  
[Anonymous], 53 STRUCT DYN MAT C
[3]  
[Anonymous], 2000, SURVEY MULTIOBJECTIV
[4]   Optimization of clearance variation in selective assembly for components with multiple characteristics [J].
Asha, A. ;
Kannan, Sm. ;
Jayabalan, V. .
INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY, 2008, 38 (9-10) :1026-1044
[5]   Virtual projective shape matching in targetless CAD-based close-range photogrammetry for efficient estimation of specific deviations [J].
Bergstrom, Per ;
Fergusson, Michael ;
Sjodahl, Mikael .
OPTICAL ENGINEERING, 2018, 57 (05)
[6]   Metaheuristics in combinatorial optimization: Overview and conceptual comparison [J].
Blum, C ;
Roli, A .
ACM COMPUTING SURVEYS, 2003, 35 (03) :268-308
[7]  
Bussieck MR, 2004, APPL OPTIMIZAT, V88, P137
[8]  
Chan K.C., 1998, Quality Engineering, V11, P221, DOI DOI 10.1080/08982119808919233
[9]   Design and management of manufacturing systems for production quality [J].
Colledani, Marcello ;
Tolio, Tullio ;
Fischer, Anath ;
Iung, Benoit ;
Lanza, Gisela ;
Schmitt, Robert ;
Vancza, Jozsef .
CIRP ANNALS-MANUFACTURING TECHNOLOGY, 2014, 63 (02) :773-796
[10]   A fast and elitist multiobjective genetic algorithm: NSGA-II [J].
Deb, K ;
Pratap, A ;
Agarwal, S ;
Meyarivan, T .
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, 2002, 6 (02) :182-197