Abstract
An assembly process plan for a given product provides the sequence of assembly operations, their times as well as the required tools and fixtures for each operation. Much research has been done on automating and optimizing assembly sequence generation as the most important part of an assembly process plan. A novel method for generating the assembly sequence of a given product based on available assembly sequence data of similar products is presented. The proposed method uses a binary tree form to represent the assembly sequences of an existing family of products. A Genetic Algorithm is employed to find the consensus tree that represents the set of all assembly sequence trees with minimum total dissimilarity distance. This is similar to defining Generic Bill-of-Material. The generated consensus tree serves as a master assembly sequence for the product family. The assembly sequence for a new product variant that falls within, or significantly overlaps with, the scope of the considered family of products can be directly extracted from the derived master assembly sequence tree. The developed method is demonstrated using a family of three control valves. This novel method greatly simplifies and enhances automatic assembly sequence generation and minimizes subsequent modifications, hence, reduces assembly planning cost and improves productivity.
Similar content being viewed by others
References
De Fazio TL, Whitney DE (1987) Simplified generation of all mechanical assembly sequences. IEEE J Robotics Autom RA-3 (6):640–658
ElMaraghy HA, Knoll L (1991) Design and automatic assembly sequence generation of a d.c. motor. Int J Veh Des 12(5–6):672–683
ElMaraghy HA, Laperriere L (1992) Modelling and sequence generation for robotized mechanical assembly. Robotics Auton Syst 9(3):137–147. doi:10.1016/0921-8890(92)90050-9
ElMaraghy HA, Rondeau JM (1992) Automatic robot program synthesis for assembly. Robotica 10:113–123
Laperriere L, ElMaraghy HA (1994) Assembly sequences planning for simultaneous engineering applications. Int J Adv Manuf Technol 9(4):231–244
Dini G, Failli F, Lazzerini B, Marcelloni F (1999) Generation of optimized assembly sequences using genetic algorithms. CIRP Ann Manuf Technol 48(1):17–20
Sinanoglu C, Borklu HR (2005) An assembly sequence-planning system for mechanical parts using neural network. Assembly Autom 25(1):38–52. doi:10.1108/01445150510578996
Wang Y, Liu JH (2010) Chaotic particle swarm optimization for assembly sequence planning. Robotics Comput Integr Manuf 26(2):212–222. doi:10.1016/j.rcim.2009.05.003
Romanowski CJ, Nagi R (2004) A data mining approach to forming generic bills of materials in support of variant design activities. J Comput Inf Sci Eng 4(4):316–328. doi:10.1115/1.1812556
Hegge HMH, Wortmann JC (1991) Generic bill-of-material. A new product model. Int J Prod Econ 23(1–3):117–128. doi:10.1016/0925-5273(91)90055-x
Martinez MT, Favrel J, Ghodous P (2000) Product family manufacturing plan generation and classification. Concurr Eng Res Appl 8(1):12–23. doi:10.1106/7p7h-5ejt-glct-nu8d
Lai H-Y, Huang C-T (2003) Integrated assembly plan generation system for grouped product families. Int J Prod Res 41(17):4041–4061
Gupta S, Krishnan V (1998) Product family-based assembly sequence design methodology. IIE Trans 30(10):933–945
Adams EN (1972) Consensus techniques and the comparison of taxonomic trees. Syst Zool 21(4):390–397
Dong J, Fernandez-Baca D, McMorris FR, Powers RC (2010) Majority-rule (+) consensus trees. Math Biosci 228(1):10–15. doi:10.1016/j.mbs.2010.08.002
Azab A, Samy S, ElMaraghy H (2008) Modeling and optimization in assembly planning. Paper presented at the 2nd CIRP Conference on Assembly Technologies and Systems (CATS), Toronto, Canada
Whitney DE (2004) Mechanical assemblies: their design, manufacture, and role in product development, vol 1. Oxford University Press, USA
Azab A, ElMaraghy HA (2007) Mathematical modeling for reconfigurable process planning. CIRP Ann Manuf Technol 56(1):467–472. doi:10.1016/j.cirp.2007.05.112
Nilsson NJ (1980) Principles of artificial intelligence. Springer, Berlin
Miller JM, Hoffman RL (1989) Automatic assembly planning with fasteners. In: IEEE International conference on robotics and automation, Scottsdale, AZ, USA, pp 69–74
Homem de Mello LS, Sanderson AC (1989) A correct and complete algorithm for the generation of mechanical assembly sequences. In: IEEE International Conference on Robotics and Automation, Washington, DC, USA, pp 56–61. doi:10.1109/robot.1989.99967
Marian RM, Luong LHS, Abhary K (2002) Assembly sequence planning and optimisation using genetic algorithms.I. Automatic generation of feasible assembly sequences. Appl Soft Comput 2(2):223–253
Hong DS, Cho HS (1997) Generation of robotic assembly sequences with consideration of line balancing using simulated annealing. Robotica 15:663–673. doi:10.1017/s0263574797000799
Wang JF, Liu JH, Zhong YF (2005) A novel ant colony algorithm for assembly sequence planning. Int J Adv Manuf Technol 25(11–12):1137–1143. doi:10.1007/s00170-003-1952-z
Pan C, Smith SS, Smith GC (2006) Automatic assembly sequence planning from STEP CAD files. Int J Comput Integr Manuf 19(8):775–783. doi:10.1080/09511920500399425
Jones RE, Wilson RH, Calton TL (1998) On constraints in assembly planning. IEEE Trans Robotics Autom 14(6):849–863. doi:10.1109/70.736770
Wolter JD (1992) A combinatorial analysis of enumerative data structures for assembly planning. J Desi Manuf 2(2):93–104
Page RDM (1994) Maps between trees and cladistic analysis of historical associations among genes, organisms, and areas. Syst Biol 43(1):58–77
Robinson DF, Foulds LR (1981) Comparison of phylogenetic trees. Math Biosci 53(1–2):131–147. doi:10.1016/0025-5564(81)90043-2
Holland JH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control and artificial intelligence. MIT press, Cambridge
ElMaraghy HA, AlGeddawy T (2012) Co-evolution of products and manufacturing capabilities and application in auto-parts assembly. Flex Serv Manuf J 24(2):142–170. doi:10.1007/s10696-011-9088-1
Jain AK, Murty MN, Flynn PJ (1999) Data clustering: a review. ACM Comput Surv (CSUR) 31(3):264–323
Pattengale ND, Gottlieb EJ, Moret BM (2007) Efficiently computing the Robinson-Foulds metric. J Comput Biol 14(6):724–735
Asano T, Jansson J, Sadakane K, Uehara R, Valiente G (2010) Faster computation of the Robinson-Foulds distance between phylogenetic networks. In: Amir A, Parida L (eds) Combinatorial pattern matching. Springer, Berlin, pp 190–201
Day WH (1985) Optimal algorithms for comparing trees with labeled leaves. J Classif 2(1):7–28
Dorot (2001) Dorot automatic control valves. Catalogue
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Kashkoush, M., ElMaraghy, H. Consensus tree method for generating master assembly sequence. Prod. Eng. Res. Devel. 8, 233–242 (2014). https://doi.org/10.1007/s11740-013-0499-6
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11740-013-0499-6