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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?

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

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

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

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

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).