Site Loader
Rock Street, San Francisco

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.

Lu C, Huang HZ, Fuh
JYH, Wong YS (2008) A multi-objective disassembly planning approach with ant
colony optimization algorithm. Proc. Inst. Mech. Eng. B. J. Eng. Manuf.,
222(11), 1465–1474.

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!


order now

Tiwari MK, Prakash AK, Mileham
AR (2005) Determination of an optimal assembly sequence using the psychoclonal
algorithm. Proc. Inst. Mech. Eng. B. J. Eng. Manuf., 219(1), 137–149.

Lu C, Liu YC (2012) A
disassembly sequence planning approach with an advanced immune algorithm. Proc.
IME. C. J. Mech. Eng. Sci., 226(11), 2739–2749.

Cao PB, Xiao RB (2007) Assembly
planning using a novel immune approach. Int. J. Adv. Manuf. Technol., 31(7–8), 770–782.

Wang Y, Liu JH (2010)
Chaotic particle swarm optimization for assembly sequence planning. Robot
Comput. Integr. Manuf., 26(2), 212–222.

Lv H, Lu C (2010) An assembly
sequence planning approach with a discrete particle swarm optimization
algorithm. Int. J. Adv. Manuf. Technol., 50, 761–770.

Lu C, Wong YS, Fuh JYH
(2006) An enhanced assembly planning approach using a multi-objective genetic algorithm.
Proc. Inst. Mech. Eng. B J Eng Manuf, 220(2), 255–272.

Smith, G. C. and Smith,
S. S. F. (2002) An enhanced genetic algorithm for automated assembly planning.
Robotics Computer Integ. Mfg, 18, 355–364.

Guan, Q., Liu, J. H.,
and Zhong, Y. F. (2002) A concurrent hierarchical evolution approach to
assembly process planning. Int. J. Prod. Res., 40(14), 3357–3374.

Lit, P. D., Latinne,
P., Rekiek, B., and Delchambre, A. (2001) Assembly planning with an ordering
genetic algorithm. Int. J. Prod. Res., 39(16), 3623–3640.

Chen, S. F. and Liu, Y.
J. (2001) An adaptive genetic assembly sequence planner. Int. J. Computer  Integ. Mfg, 14(5), 489–500.

Lazzerini, B. and
Marcelloni, F.  (2000) A genetic
algorithm for generating optimal assembly plans. Artif. Intell. Engng, 14,
319–329.

Hong, D. S. and Cho, H.
S. (1999) A genetic-algorithm based approach to the generation of robotic
assembly sequence. Control Engng Practice, 7, 151–159.

Dini, G., Failli, F.,
Lazzerini, B., and Marcelloni, F. (1999) Generation of optimized assembly
sequence using genetic algorithms. Annals CIRP, 48(1), 17–20.

References

The strong urge of
manufacturing companies to become more flexible in their products and the
importance of assembly sequence planning place a high importance on characterizing
product flexibility in an assembly system. With little knowledge on this research
aspect, it is expected that it will contribute immensely to both theory and
practice by identifying the need for assembly sequence planning for flexible
product. This research will focus on assembly systems with semi-automatic assembly
lines.

Through assembly
sequence planning, a feasible and optimal assembly sequence can be obtained
through which the parts can be assembled into the product successfully and
efficiently with less assembly time or assembly cost. It has been seen that
there are several works on assembly sequence planning with some limitations.
Hence, this research study is proposing to utilize the generic algorithm based
approach for assembly sequence planning for flexible product in which changes
time of the assembly tools, assembly directions and assembly types will be used
in the fitness function to evaluate the assembly cost. Moreover, the influence
of tolerance and clearance on the product will be considered and non-dominated
solutions will be found. More constraints will be considered to improve the
stability in the assembly process which will help widen the capacity of
assembly sequence planning. Case studies will be given so as to verify the
proposed assembly sequence planning approach for flexible products.

For the research works on
assembly sequence planning, Lu et al. (2006) and Guan et al. (2002) proposed
the assembly planning approaches with genetic algorithm (GA), where the
assembly sequences are regarded as chromosomes, and the solutions are evolved
through crossover and mutation operation. Lv and Lu (2010) and Wang and Liu
(2010) proposed the particle swarm optimization approach to assembly sequence
planning, and this approach is easier to implement with the fewer computation
procedures and fewer parameters. Cao and Xiao (2007) proposed an immune
optimization approach to generate the optimal or near-optimal assembly sequence
by the immune operations, such as immune selection, inoculation, and immune
metabolism. Lu and Liu (2012) proposed a disassembly sequence planning approach
with an advanced immune algorithm, by which the optimal or near-optimal assembly
sequence can be derived by converting the generated disassembly sequences. Based
on the artificial immune system, Tiwari et al. (2005) proposed a psychoclonal
algorithm by applying need hierarchy theory and the theory of clonal selection
to address the assembly sequence planning problem with good evolution performance.
Besides the above approaches, Lu et al. (2008) and Wang et al. (2005) proposed
the ant colony optimization approach to disassembly planning or assembly
planning. The disassembly sequence or assembly sequence can be built step by step
with the mechanism of the ant colony optimization, and the optimal or
near-optimal sequences can be easily found.

The work of Dini et al.
(1999) proposed a method using genetic algorithms to generate and evaluate the
assembly sequence, and adopted a fitness function considering simultaneously
the geometric constraints and some assembly process, including the minimization
of gripper changes and object orientations, and the possibility of grouping
similar assembly operations. Hong and Cho (1999) proposed a GA-based approach
to generate the assembly sequence for robotic assembly, and the fitness
function is constructed based on the assembly costs that are reflected by the
degree of motion instability, and assembly direction changes are assigned with
different weights. Lazzerini and Marcelloni (2000) used GA to generate and
assess the assembly plans. The fitness function is constructed through
assigning different weights to three criteria: number of orientation changes,
number of the gripper replacements, and grouping of similar assembly operations;
and the different assembly planning results are derived through adjusting the
weights in the fitness function in the experiments. Chen and Liu (2001)
proposed an adaptive genetic algorithm (AGA) to find global optimal or
near-global-optimal assembly sequences. In this algorithm, the genetic-operator
probabilities are varied according to certain rules, and calculated by a
simulation function. The calculated genetic operator probability settings are
then used to optimize dynamically the AGA search for an optimal assembly
sequence. Lit et al. (2001) proposed an original ordering GA to plan the
assembly sequence. In this approach, a multi-objective cost function was
proposed, including five technical criteria: the number of reorientations, the
stability of subsets, the parallelism between operations, and the latest or
earliest components to be put in the plan. The algorithm is based on a
multi-criterion decision-aided method whereby the decision maker assigns and
adjusts the respective weights until good solutions can be found. Guan et al. (2002)
proposed the concept of gene-group to consider the assembly process planning.
One gene-group includes the component to be assembled, tool used to handle the
component, assembly direction, and type of the assembly operation to express
the information of the assembly process. The change times of the assembly
tools, assembly directions, and assembly types are used in the fitness function
to evaluate the assembly costs. Smith and Smith (2002) proposed an enhanced
genetic algorithm based on the traditional genetic algorithm. This approach
does not choose the next generation assembly sequence based on the fitness.
Instead it periodically repopulates with high-fitness assembly plans to find
optimal or near-optimal assembly plans more reliably and quicker than the
traditional approaches. Some success has been achieved in the abovementioned
GA-based assembly planning works. In these works, generally the geometric
precedence constraints are used during assembly planning to ensure the generation
of a feasible assembly sequence. However, the influence of tolerance and
clearance on product assemblability in different assembly sequences was not
considered. In addition, to deal with a multi-objective optimization problem,
these works generally used constant weights to build the fitness function by
some form of evolutionary trial. The search direction was fixed, and sometimes
the optimal or near-optimal solution, and other non-dominated solutions, could
not be found. According to the above limitations, more research effort is
needed in this area to enhance the function of assembly planning. The tolerance
and clearance influence on product assemblability should be considered, and
more non-dominated solutions should be found.

Assembly sequence
planning is one of the best-known productions scheduling problems and proved to
be a strongly NP-hard problem. It has a focus of determining the order of
processing jobs in the assembly line, to save the assembly cost or shorten the
assembly time. Recently, some artificial intelligence-based technologies have
been utilized in the assembly sequence planning. Knowledge-based approach and
Generic algorithm-based approach are the two areas in which artificial
intelligence based approach can be divided. Although the mechanism of
knowledge-based approach can find feasible assembly sequence, however; when
assembly has many parts and components, and many alternative assembly sequences
exist, it is difficult to find optimal assembly sequence without an optimal search
algorithm. Generic algorithm-based approach for assembly planning has received
greater research interest because both the optimal and near optimal solution
can be found with high computing efficiency being achieved. Hence, the generic
algorithm is a promising approach to be utilized for this study.

Assembly planning is an
important step during product development. Flexible product development is the
ability to make changes to the product being developed or how it is being
developed without being too disruptive. It is worthy to state that assembly planning
main objective is to find a feasible assembly sequence with the minimum
assembly cost and assembly time.  To
improve profit margin, effective assembly planning is important so as to
significantly reduce the product development cost.

Post Author: admin

x

Hi!
I'm Dora!

Would you like to get a custom essay? How about receiving a customized one?

Check it out