1) The document discusses using ant colony optimization (ACO) algorithms to optimize the process planning for a combined punch and laser machine. This involves classifying operation features as punch-only, cut-only, or intermediate and determining the optimal sequence. 2) Key steps include identifying operation features from part geometry, classifying them based on punch and laser limitations, and applying rules to determine if intermediate features are punched or cut. 3) Applying ACO algorithms to optimize the tool path significantly reduced total travelling distance compared to an intuitively designed path, demonstrating the effectiveness of the proposed method.

1. 1
OPTIMAL PROCESS
PLANNING OF COMBINED
PUNCH AND LASER MACHINE
USING ANT COLONY
OPTIMIZATION
ARISH .I ROLL NO:4

2. 2
AMADA APELIO Combined Punch – and – Laser
Machine
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

3. 3
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

4. Decision I : Punch or cut ?
4

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

5. For Intermediate Group
5


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

6. 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
speed Vt
divided by positioning
n is the quantity of the operation feature
tstroke is the time per punch stroke
According to Rule II
6
min (Tc) <min (Tp) +tx

7. Decision II : what is the optimal
operation sequence ?
7

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?

8. ANT COLONY
OPTIMIZATION ALGORITHM
NEST
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.

9. 9
TRAVELLING SALESMAN
PROBLEM
Given 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
problem such as the TSP
optimization

10. FLOWCHART OF ACO
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
Have the
maximum
Iterations been
performed
NO
Determine the shortest
tour till now and
update pheromone
NO
Have all
cities been
visited
YES
Record the length of
tour and clear tabu list
YES
STOP
ACO

11. 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

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
ij
= 1/dij

12. 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.
ij
k
ij
(t )
ij
il (t )
p (t )
l J k (i )
0
il
if j
J k (i )
f j
J k (i )

13. 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
Q
where edge (i, j ) Tk t
k
Lk
ij t
if edge (i, j ) Tk t
0
 The pheromone decay in each edge of a tour is given by
ij
(t 1)
(1
)
ij
(t )
ij
(t )

14. SOLUTION TO PROBLEM
14

Operation feature with
largest
quantity
is
assigned for punching.

For 2 contours they have
to be cut.

15. 15

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.

16. Solution : Tool path optimization
16

17. 17
The total reduced travelling distance in a 1000 x 1120mm
sheet from 11942 to 10046 is 1896 mm.

18. 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
18

19. CONCLUSION
19



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.

20. References
20




[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 ColorniThe 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