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OPTIMAL PROCESS PLANNING OF COMBINED PUNCH AND LASER MACHINE USING ANT
COLONY OPTIMIZATION
ARISH .I ROLL NO:4
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A combined Punch- and – Laser Machine was first invented in 1980 by Clark and Carbone and it integrates a punch tool with a laser beam cutter into one machine.
AMADA APELIO Combined Punch – and – Laser Machine
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PROCESS PLANNING PROBLEM
Two components 4 Different
operations features 23 small holes of
Φ50 4 large holes of
Φ180 4 contours for first
component 7 contours for
second component
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Decision I : Punch or cut ?
Identify each operation feature from
geometric data.
According to the limitations of punch and
laser cutting operations classify all the
operation features to punch, laser cutting
and an intermediate group.
Decide an operation for each feature in
the intermediate group.
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For Intermediate Group
Rule 1 : Operation feature with largest quantity assign for punching.
Rule 2: For rest of features : If min (Tc) <min (Tp) +tx, the feature is to be fabricated by laser cutting; otherwise, it is to be punched.
Tc is the total laser cutting time
Tp is the total punch time tx is the tool exchange time
between the punch-and-laser cutter
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tc - actual laser cutting time = cutting length Lc
divided by the laser cutting speed Vc
tt - travelling time between identical operation
features = total length of travelling Lt divided by positioning speed Vt
n is the quantity of the operation feature tstroke is the time per punch stroke
According to Rule II min (Tc) <min (Tp) +tx
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Decision II : what is the optimal operation sequence ?
Is it more efficient to perform the punch
operations all at once?
What is the manufacturing order for
different features with the same
operations?
What is the shortest travelling path to
fabricate all the features?
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ANT COLONY OPTIMIZATION ALGORITHM
NEST FOODNEST FOODNEST FOOD
Ants secrete pheromone while traveling from the nest to food, and vice versa in order to communicate with one another to find the shortest path.
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TRAVELLING SALESMAN PROBLEMGiven a set of n cities, the Traveling Salesman Problem
requires a salesman to find the shortest route between
the given cities and return to the starting city, while
keeping in mind that each city can be visited only once.
The ACO relies on the co-operation
of a group of artificial ants to obtain
a good solution to a discrete
optimization problem such as the
TSP
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Have all cities been
visited
Have the maximum
Iterations been performed
START ACO
Locate ants randomly in cities across the grid and store the
current city in a tabu list
Determine probabilistically as to which city to visit next
Move to next city and place this city in the
tabu list
Record the length of tour and clear tabu list
Determine the shortest tour till now and
update pheromone
NO
YES
STOPACO
YESNO
FLOWCHART OF ACO
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KEY PARAMETERS Trail intensity is given by value of ij which
indicates the intensity of the pheromone on the
trail segment, (ij)
Trail visibility is ij = 1/dij
The importance of the intensity in the
probabilistic transition is The importance of the visibility of the trail
segment is The trail persistence or evaporation rate is given
as Q is a constant and the amount of pheromone
laid on a trail segment employed by an Ant; this
amount may be modified in various manners
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PROBABILISTIC CITY SELECTION
Helps determine the city to visit next while the
ant is in a tour
Determined by variables such as the pheromone
content in an edge (i,j) at time instant t.
)(
)(
0
)(
)(
)()(
iJjf
iJjift
t
tp
k
kilil
ijij
kij
ikJl
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PHEROMONE UPDATING
Using the tour length for the k-th Ant, Lk, the
quantity of pheromone added to each edge
belonging to the completed tour is given by
tTjiedgeif
tTjiedgewhereL
Qt
k
k
k
kij
),(
),(
0
)()()1()1( ttt ijijij
The pheromone decay in each edge of a tour is
given by
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SOLUTION TO PROBLEM
Operation feature with largest quantity is assigned for punching.
For 2 contours they have to be cut.
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For large holes By equation (3) to calculate the time for each alternative. Assume that the maximum laser cutting speed is 10 m/min, the tool exchange time is 3s, and the maximum punch stroke is 900/min.
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Solution : Tool path optimization
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The total reduced travelling distance in a 1000 x 1120mm sheet from 11942 to 10046 is 1896 mm.
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Identify operation features.
Classify features to punch-only, cut-only and intermediate groups based on the capacity of the punch and lasercutter.
Move the first feature of the largest quantity from the intermediate group to the punch-only group.
Apply Rule II for rest features in the intermediate group to complete the classification.
Optimise the tool path for all the features using the ACO Algorithms
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CONCLUSION
The proposed method integrates knowledge, quantitative analysis and numerical optimization to achieve the goal.
From Example , it is shown that proposed method should lead to high manufacturing efficiency.
The ACO algorithms are effectively applied and yield significant savings than intuitively designed operation paths.
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References
[1]. G. G. Wang And S. Q. Xie Optimal process planning for a combined punch-and-laser cutting machine using ant colony optimization- International Journal of Production Research, Vol. 43, No. 11, 1 June 2010.
[2]. Marco Dorigo, Vittorio Maniezzo and Alberto Colorni-The Ant System: optimization by a colony of cooperating agents- IEEE Transactions on SystemsVol.26, No.1, 1996.
[3]. Dorigo, M., Ant colony optimization, 2003 http://www.aco-metaheuristic.org/publications.html (accessed December 2004).
[4]. Kalpakjian, S. and Schimid, S.R., Manufacturing Processes for Engineering Materials, 2003(Upper Saddle River, NJ: Prentice Hall).