Journal of Manufacturing Processes 69 (2021) 630–647
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Journal of Manufacturing Processes
journal homepage: www.elsevier.com/locate/manpro
Image processing approach to automate feature measuring and process
parameter optimizing of laser additive manufacturing process
Deepika B. Patil *, Akriti Nigam, Subrajeet Mohapatra
Department of Computer Science and Engineering, Birla Institute of Technology Mesra, Ranchi 835215, Jharkhand, India
A R T I C L E I N F O
A B S T R A C T
Keywords:
Laser additive manufacturing
Image processing
Geometric features
Measurement
Optimization
Edge detection
The present research work focuses on the development of image processing technique that can automatically
extract the deposition geometric features and optimize the process parameters required for manufacturing
components by laser additive manufacturing process. This paper reports (i) manufacturing of vertical and hor­
izontal wall components and capturing its images, (ii) developing robust image-processing technique for feature
extraction and measurement, (iii) formulating a component sorting methodology with a capability to accept and
reject component, and (iv) developing the process parameter optimizing model to identify the optimized com­
bination of process parameters used to manufacture components. The developed image processing algorithm has
been validated against the manual measurement method and CAD model. It has been observed that the proposed
image processing algorithm can measure the geometric features of the vertical and horizontal wall components
with an error of less than 3%. The optimization study gave the optimized value of laser power as 820 W, 850 W,
800 W and 860 W, scanning speed as 500 mm/min, 500 mm/min, 730 mm/min and 700 mm/min, and powder
feed rate as 6 g/min, 10 g/min, 7 g/min and 7 g/min for effective vertical wall width, effective vertical wall
height, effective horizontal wall width and effective horizontal wall height, respectively. The optimized process
parameters were validated experimentally on laser based additive manufacturing process. The optimized values
of effective vertical wall width, effective vertical wall height, effective horizontal wall width and effective
horizontal wall height are 5.003, 14.003, 20.003 and 6.002, respectively, with corresponding experimental
values as 5.028, 14.016, 20.018 and 6.028, respectively. Therefore, for the fast-growing additive manufacturing
industry the proposed image processing methodology will offer benefits of automatic feature measuring process
and process parameter optimizing with high accuracy and less human interference. In future, the image pro­
cessing algorithm will be further developed for the real-time feature extraction of the depositions done by laser
based additive manufacturing process.
1. Introduction
Direct energy laser deposition process is widely used additive
manufacturing process for manufacturing complex, good quality, high
accuracy, 3D components. However, ensuring the accuracy of the
components is still a challenging task for additive manufacturing in­
dustries. Consequently, researchers are trying to bridge this challenge
either by proposing the optimized process parameters of the deposition
process or by developing novel measurement techniques that can ensure
the accuracy of the deposition process. Most challenging task faced by
the researchers working in the development of the measurement tech­
nique is by making use of image processing techniques. Capabilities of
image processing can be used for characterizing surface defects [1],
online monitoring of the manufacturing process [2], generating toolpath
in CNC machines [3], and assess the surface quality of products [4].
In previous literature, simple and regular geometry features were
extracted using different feature extraction techniques such as Hough
transformation with genetic algorithm method, standard Hough trans­
formation method, ellipse detection method and randomized Hough
transformation method [5–9]. For some of the deposition processes,
researchers have used image processing software integrated with me­
chanical measurement equipment. Alfano et al. [10] used an air plasma
spring process for the deposition of ceria and yttria co-stabilized zirconia
materials. They used image analysis technique on the images obtained
by Scanning Electron Microscopy (SEM) to examine the microstructure
and porosity in the deposition. For gas metal are deposition process,
* Corresponding author.
E-mail address: deepikapatil941@gmail.com (D.B. Patil).
https://doi.org/10.1016/j.jmapro.2021.07.064
Received 30 December 2020; Received in revised form 12 July 2021; Accepted 28 July 2021
Available online 19 August 2021
1526-6125/© 2021 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.
D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 1. Flow chain adopted in present study.
Thao and Kim [11] used image analysis software integrated with the
microscope to obtain the deposition geometry features, while Sathiya
et al. [12] measured deposition geometric features using an optical
microscope. Consequently, for the deposition geometry obtained by gas
metal arc welding and CO2 laser welding process, Rayes et al. [13] used
the stereotype microscope with image analysis software to measure the
deposition features. For the metal arc gas deposition process, Cao et al.
[14] used an image processing technique known as Canny edge detec­
tion to extract the features and shape of deposition. On the other hand,
for a similar process, Singh et al. [15] used an image analysis technique
to measure features of deposition geometry.
Researchers are also working on the development of robust image
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Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 2. Experimental setup of laser additive manufacturing used for building the components.
Fig. 3. CAD model of the components build using laser additive manufacturing (a) horizontal wall and (b) vertical wall.
manufacturing process, Vaidya and Anand [18] proposed an image
processing method that can slice the computer aided design model. This
approach has been known as IPSlicer. In this, the snapshot of the product
image has been captured in the perpendicular direction of the heat
source. Later this image is exported to IPSlicer to slice the desired ge­
ometry in the snapshot. This helps to get the CAD data by converting it to
the STL file. For the powder bed fusion additive manufacturing process,
Yao et al. [19] used an image processing technique known as multi­
fractal method. They used this method to detect the defects and char­
acterize the microstructure of the components, in which they identified
the dimension and shape of the defect and microstructure. Researchers
have used image processing techniques with machine learning to over­
come the problems of additive manufacturing. Straub [20] has effec­
tively used multiple cameras as an image processing technique to detect
the live defects generated in deposition geometry during deposition
process. Using a similar method Wang et al. [21] evaluated and
controlled the grain morphology of the components built by the laser
melting deposition process.
Past literature suggests that the image processing in additive
manufacturing has the ability to characterize the microstructure feature
of complex geometry and analysis of the deposition process. Conse­
quently, the aim of the present research work is to develop the robust
image-processing based technique that can automate the process of
geometric features extraction for the components manufactured by laser
additive manufacturing process. To achieve the aim, following objec­
tives were carried out in this research work:
Table 1
Combinations of process parameters used to manufacture components.
Experiment
no
Power
(W)
Scanning
speed
(mm/min)
Powder
feed rate
(g/min)
Number of
passes for
vertical
wall
Number of
passes for
horizontal
wall
1
2
3
4
5
6
7
8
9
10
11
12
800
900
1000
800
900
1000
800
900
1000
800
900
1000
500
5
500
10
700
5
700
10
96
96
94
109
95
86
103
98
103
100
103
102
175
197
256
215
203
187
205
256
210
226
244
254
processing techniques that can improve the performance of the additive
manufacturing process. For laser cladding process, to build the compo­
nent of the aircraft engine, Tabernero et al. [16] used the image pro­
cessing method to get the porosity size, shape and number. For the
laminated Object Manufacturing process, Putthawong et al. [17] pro­
posed the image processing technique to generate the tool path. They
used adaptive cross hatching and intersection point detection technique
to extract the tool path information. For the laser additive
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Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 4. Cross section of the (a) horizontal wall and (b) vertical wall geometric features to be extracted by image processing.
1. Manufacturing of vertical and horizontal wall components by using
laser additive manufacturing process and acquiring the crosssectioned images of the manufactured components.
2. Develop a robust image-processing based technique for feature
extraction and measurement of the total and effective width, height,
and area of the vertical and horizontal wall components.
3. Propose a component sorting methodology with a capability to
accept or reject dimensions based on extracted features.
4. Develop the process parameter optimizing model using genetic al­
gorithm optimization technique for the laser additive manufacturing
process. Consequently, the accuracy of the proposed technique was
validated with a manual measuring method.
2. Methodology
Fig. 1 depicts the flow chain adopted in the present study to auto­
mate the geometric feature measuring process. The flow chain is divided
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Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 5. Image processing on (a) vertical wall and (b) horizontal wall obtained for each experiment.
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into four stages. Initially, in stage 1, the components with the vertical
and horizontal walls are manufactured using various process parameters
of the laser additive manufacturing process. In stage 2, the images for
the manufactured components are captured and the images are preprocessed to remove any noise, shadow condition, poor lighting condi­
tion, and perspective. Thereafter, the images are processed in stage 3 for
parametrization of the geometric features such as width, height, and
area of the deposition geometry. In the next stage i.e., stage 4, the
computed geometric features such as effective width, effective height,
and effective area are individually compared with the values obtained
from the CAD model. Based on which the decision of accepting or
rejecting the dimension is made. For the rejected dimensions, the opti­
mum combination of process parameters is identified by the process
parameter optimizing model. The optimized process parameters are sent
back to manufacture the components in stage 1 with geometry features
close to the CAD model.
2.1. Experimentation and image acquisition
In the present study, the laser additive manufacturing process has
been used to manufacture the components of Inconel 625 on the mild
steel substrate material. Fig. 2 shows the experimental setup of the laser
deposition process used for experimentation at Magod Fusion Technol­
ogies Pvt. Ltd., Pune, India. Two Computer-Aided Design (CAD) models
of deposition geometries named as vertical and horizontal walls as
depicted in Fig. 3a and b respectively were manufactured. The process
parameters such as laser power, powder feed rate, and travel rate of heat
source selected to obtain these deposition geometries are as given in
Table 1. Other parameters of setup consist of laser spot diameter as 2
mm, hatch spacing as 1 mm, slicing thickness as 1 mm and scan pattern
as zigzag. The total number of passes used to obtain vertical and hori­
zontal walls are included in Table 1.
The deposition geometries as shown in Fig. 3 were manufactured
using each combination of process parameters given in Table 1. There­
after, the depositions are sectioned along with the height of deposition
geometry using a wire-electro discharge machine. The sectioned sam­
ples were placed on a flat surface plate with black in its background to
eliminate the shadow effect. The images were acquired using Canon
(Model 1500 D) camera under natural light conditions. Camera has been
levelled parallel to the surface plate to avoid any asymmetricity in the
captured images. Fig. 4a depicts the vertical wall and Fig. 4b depicts
horizontal wall that was sectioned to acquire an image of deposition
geometries used for image processing.
Fig. 6. Schematic of vertical and horizontal lines drawn at the edge of depo­
sition to trace outer rectangle boundaries.
2.2. Image processing approach
This section describes the ability of the image processing approach to
automate the feature measuring process and process parameter opti­
mization required in manufacturing components by the laser additive
manufacturing process. In the present work, authors have proposed a
novel image processing algorithm that can identify the irregular shapes
appearing in the deposition geometries. It has features to automatically
detect and parametrize the dimensions of the components such as total
and effective width, height, and area of the deposition geometries. The
process is carried out into two stages, pre-processing (stage II) and
parameterization (stage III) of deposition geometry as shown in Fig. 1.
The following subsections describe the methodology adopted for image
processing.
2.2.1. Image pre-processing
In present study, the image analysis has been done to handle noise,
shadow condition, poor lighting condition, and perspective using steps
described below [22]. Firstly, the camera captured images for each
experiment are converted from coloured images to greyscale images as
shown in Fig. 5a and b. This conversion helps to identify the edges of the
deposition geometry. In the next step, gamma correction is applied to
Fig. 7. Pseudocode for the outer rectangle operations.
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Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 8. Schematic of (a) concavities in deposition edges and (b) vertical line drawn at the concavity intersection point to obtain boundaries for inner rectangle.
eliminate any darkness and shadow conditions existing in the images.
This is done to enhance the dynamic range of dark pixel intensities and
shrink the dynamic range of brighter pixel intensities. Further, the Dif­
ference of Gaussian (DoG) filtering as expressed in Eq. (1) is used to
remove the shadow effect and suppress illumination gradient. While
applying DoG with Gaussian filter, width σ1 = 1.0 is implemented
through convolution of image for creating the first smoothed image. The
image is again smoothed with width σ2 = 2.0.
(
(
)
)⎞
⎛
1 ⎜
⎜1
DoG = √̅̅̅̅̅ ⎜ exp
2π ⎝σ1
−
(x+y)2
2σ2
1
−
1
σ2
−
exp
(x+y)2
2σ 2
2
⎟
⎟
⎟
⎠
2.2.2. Parameterization
The width, height, and area of deposition geometry are measured on
the edges of deposition geometry extracted using the Canny edge
detection technique [23] technique. Stage III process has been divided
into two operations: 1) Outer rectangle trace, and 2) Inner rectangle
trace. Outer rectangle trace will give the values of total width, height,
and area of a deposition while the inner rectangle trace will give the
values of effective width, height, and area of deposition. Following is the
description of the traces used for developing novel image processing
algorithm that can parametrize the features of the deposition
geometries.
(1)
(a) Outer rectangle: To extract the actual deposition geometry first,
compute the outer boundary rectangle corresponding to the
deposition object as shown in Fig. 6. The outer bounding rect­
angle gives the extent of the deposition object in the x and y di­
rections. The pixels in the image obtained after Canny edge
detection consists of rows from R1 to Ra (where a is the number of
rows) and columns from C1 to Cb (where b is the number of col­
umns). To compute the outer bounding rectangle of the deposi­
tion geometry the classic raster scan technique has been used.
Fig. 7 shows the pseudocode used to extract the outer bound
rectangle. For locating the topmost edge of the rectangle, the
scanning starts from the topmost left pixel of the image i.e., at
point 1. Further the image is scanned from left to right i.e., from
point 1 to point 2, going down one row at a time until the fore­
ground pixel intensity is obtained. The corresponding row Rtop is
selected for the point at which the foreground pixel intensity is
identified, and the straight-line for the topmost edge of the
rectangle is drawn (dotted horizontal line in Fig. 6). Conse­
quently, for locating the bottommost edge, the scanning will start
from the bottommost left pixel of the image i.e., from point 3 to
point 4. The scanning takes place from left to right in a similar
way but by going up, one row at a time (Rbottom). For locating the
leftmost edge of the rectangle, the scanning starts from the
topmost left pixel of the image i.e., at point 1. Further, the image
is scanned from top to bottom i.e., from point 1 to point 3 going
Thereafter, histogram equalization and thresholding are applied to
all inputs individually to eliminate the pixel intensity unevenness caused
due to dark and bright spots. It also improves the brightness of images.
Morphological operation and substrate removal has been done on ver­
tical and horizontal wall images as shown in Fig. 5. Morphological
opening and closing operation are applied on the images for removing
the light spots on dark background and as dark spots on light back­
ground. Opening operation (I ∘ E) as given in Eq. (2), helps to remove
noise such as small, bright spots mainly on the deposition geometry and
it preserves the level of intensity of the images. Similarly, closing
operation (I • E) as given in Eq. (3), helps to remove small holes present
in area of the deposition. Below equation represents the opening and
closing operations.
I∘E = I ⊖ E ⊕ E
(2)
I•E =I⊕E⊖E
(3)
Once the image enhancing operation is completed, the next step is to
remove the substrate area from the images captured along with substrate
material because the deposition geometry is the main requirement for
the image processing. Therefore, the substrate removed images are used
for parametrization in stage III.
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Journal of Manufacturing Processes 69 (2021) 630–647
where Cx and Cy are the coordinates of the centroid of the outer rect­
angle in the x-axis and y-axis, Outer_R, and Outer_L, is the column index
of the right and left edge of the outer rectangle, and Outer_B, Outer_T are
the row index of the bottom and top edge of the outer rectangle. To trace
the left boundary of the inner rectangle, the scanning process starts from
the pixel at the centroid. The ith column of pixels ‘Coli’ that represents a
vertical line is moved towards the left side with an increment of one
column at a time until the left bound of the outer rectangle is reached.
For each column scanned, the number of contiguous foreground in­
tensity regions is counted. The idea behind counting contiguous fore­
ground intensity regions is to identify the intersecting point at a concave
shape. In each column, there will be a minimum of two contiguous
foreground intensity regions indicating upper and lower object bound­
ary. Any extra contiguous region indicates the presence of a cave as
shown in Fig. 8a. The scanning process gets terminated at the first col­
umn which has more than two contiguous foreground intensity regions.
The column index at which the process gets terminated is the left bound
of the actual deposition. The above process is repeated in a similar
manner in the right direction from the centroid to locate the right bound
of the actual deposition. To locate the top bound, it is necessary to locate
point a and b as shown in Fig. 8b. For this process, the column which is
one pixel towards right of the inner left bound is scanned. This scanning
starts from the row corresponding to the centroid and moves upward.
The scanning stops when the first foreground pixel is encountered, and
that point is labelled as ‘a’ as shown in Fig. 8b. The same process is
repeated in the right direction. The column which is one pixel towards
the left of the inner right bound is scanned, moving upwards starting
from the row corresponding to the centroid. Point ‘b’ is located where
the first foreground pixel is obtained in the scanning process. If ‘a’ and
‘b’ are located at two different rows, then the one which is closer to the
centroid is selected and marked as the inner top bound. The bottom
bound of the inner rectangle remains the last row of the deposited object
from where the substrate has been removed. Using the left, right, top,
and bottom bounds, the inner rectangle for the actual deposition ge­
ometry is created. In the horizontal wall, the top bound is located by
using the process adopted to locate the left bound of the vertical wall.
While the bottom bound is the last row of the deposited object from
where the substrate has been removed. The left and right bound of the
horizontal wall are located using the process adopted for locating the top
bound in the vertical wall. Fig. 10a and b are the outcome of the outer
rectangle and inner rectangle trace performed on the actual deposition
geometries obtained on vertical and horizontal wall, respectively.
Fig. 9. Pseudocode for the inner rectangle trace.
right side, one column at a time until the foreground pixel in­
tensity is obtained. The corresponding column Cleft is selected for
the point at which the foreground pixel intensity is identified, and
the straight-line for the leftmost edge of the rectangle is drawn
(dotted vertical line in Fig. 6). Using a similar process and by
reversing the scanning strategy, the rightmost edge (Cright) of the
rectangle is identified. For the horizontal wall, the outer rectangle
is drawn using a similar method.
(c) Feature extraction: Using outer rectangle operation, total width,
height, and area of deposition as shown in Fig. 11a has been
measured. While using inner rectangle operation, effective width,
height, and area of deposition as shown in Fig. 11b have been
measured. Pseudocodes as depicted in Fig. 12 were used for
calculating these dimensions.
(b) Inner rectangle: It will give the effective dimensions of the
deposition geometry. The deposition geometries have concavities
as represented in Fig. 8a. The concavity for the vertical wall
deposition geometry is on the left and right side of the deposition
Therefore, for the vertical wall, the left and the right-side edge of
the inner bound rectangle is extracted initially. For this, a vertical
line is drawn at the two intersection points obtained on the
concave-shaped object as shown in Fig. 8b. Pseudocode as
depicted in Fig. 9 was used for the inner rectangle trace. The ith
column of pixels is represented as ‘Coli’. The procedure to trace
the inner rectangle begins with calculating the centroid of the
outer bounding box as shown in Eqs. (4) and (5).
Cx =
Outer R − Outer L
2
(4)
Cy =
Outer B − Outer T
2
(5)
2.2.3. Component sorting process
The effective dimensions such as effective width, height and area of
the manufactured components are desired and should be according to
the CAD model as shown in Fig. 3. The effective dimensions for each
individual experiment measured by image processing were compared
against the CAD model width as 5 mm, height as 14 mm, and area as 70
mm2 for vertical wall while width as 20 mm, height as 6 mm, and area as
120 mm2 for the horizontal wall. Almost all the combinations listed in
Table 1 are capable of manufacturing vertical and horizontal wall, but
few combinations of process parameters are not capable of producing
the desired features according to CAD model. Tables 2 and 3 shows the
value of effective and total dimensions obtained using image processing
technique for the vertical and horizontal wall, respectively. From
Table 2 for vertical wall, it can be said that the component manufactured
by combinations of process parameters in experiment no. 3 and 5 did not
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Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 10. Images after performing parametrization stage-II operation on (a) vertical wall and (b) horizontal wall obtained for each experiment.
give the desired effective width (i.e., 5 mm) while the combinations used
for experiment no. 6 and 12 did not give the desired effective height (i.e.,
14 mm). From Table 3 for the horizontal wall, it can be said that the
component manufactured by the combination of process parameters in
experiment no. 3 did not give the desired effective height (i.e., 6 mm)
while experiment no. 6 and 7 did not give the desired effective width (i.
e., 20 mm). While comparing the effective dimensions obtained by
image processing with CAD model dimensions, tolerance of ±0.30 mm
for width and height were considered. Therefore, looking at the uncer­
tainty in the process parameters combinations it is necessary to identify
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Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 11. Geometric features of the additive manufactured components extracted using image processing (a) total and (b) effective geometry features.
(9) are used as the objective functions in GA. These equations were built
by using regression model and by using the values of effective width and
height given by image processing technique for 12 experimental runs.
The components manufactured in this study are rectangular in shape
hence instead of developing a separate model for effective area, the
product of width and height has been considered. Figs. 13 and 14 depicts
the comparison of the equation predicted effective width and height
with the image processed effective width and height of the vertical and
horizontal wall, respectively. Table 4 show the bound values of process
parameters and the constraints used in GA for optimization. The effec­
tive width and height of the vertical and horizontal wall has been con­
strained as shown in Table 4. In present study, the constrain functions
used for optimization were built according to the method proposed by
Gunaraj and Murugan [24]. Other values of parameters used in GA
optimization are included in Table 5. In the past literature for the direct
energy deposition process, the value of crossover fraction used was in
between 0.7 and 0.9 [25,26]. To identify the crossover fraction value for
the present study, the trial-and-error runs was carried out using 0.7,
0.75, 0.8, 0.85 and 0.9 crossover fraction values. It was observed that
the crossover fraction value of 0.8 and 0.85 gave the best fitness value
very close to the effective dimensions of horizontal and vertical walls.
Therefore, these values are used in present work. The objective functions
in this study are nonlinear therefore Augmented Lagrangian GA has
been used [27]. To identify the most appropriate optimized process
parameters values, optimization was carried out for various combina­
tions of GA parameters as shown in Table 6. The fitness values and
process parameter combinations for each GA parameter combinations
has been included as the supplementary material. While the combina­
tion of GA parameter that gave fitness value close to the width and
height of CAD model of vertical and horizontal wall has been included in
Tables 7 to 10. It has been observed that, for vertical wall, combination 4
gave the best fitness values of the effective width i.e., 5.003 (Table 7)
and combination 7 gave the best fitness values of the effective height i.e.,
Fig. 12. Pseudocodes for the feature extractions.
the optimum combination of process parameters for vertical and hori­
zontal wall.
2.2.4. Optimization of process parameter
In the present study, optimization of power, scanning speed and
powder feed rate of laser additive manufacturing process is done by
using Genetic Algorithm (GA). Optimization has been done to achieve
the targeted value of width as 5 mm and height as 14 mm for vertical
wall and width as 20 mm and height as 6 mm for the horizontal wall. For
vertical wall, Eqs. (6) and (7), while for the horizontal wall, Eqs. (8) and
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Table 2
Effective and total dimensions obtained for vertical wall.
Exp
no.
Image processed effective
width (mm)
Image processed effective
height (mm)
Image processed effective
area (mm2)
Image processed total
width (mm)
Image processed total
height (mm)
Image processed total
area (mm2)
1
2
3
4
5
6
7
8
9
10
11
12
4.95
4.98
3.75
4.98
3.71
4.91
4.99
5.06
5.01
4.96
4.95
5.12
13.98
13.96
13.91
14.05
13.83
13.07
14.02
13.99
13.94
14.01
14.15
12.59
69.2
69.5
52.1
70.0
50.9
64.2
70.0
70.8
69.8
69.5
70.0
64.5
6.35
6.62
6.38
7.22
6.15
6.02
7.14
6.86
6.96
6.40
6.49
7.23
15.17
14.43
14.72
15.05
15.50
14.23
14.39
14.28
14.83
15.55
15.87
14.10
70.63
75.06
63.86
82.62
60.73
70.63
74.93
73.94
76.48
77.68
70.68
76.33
Bold indicates the experiement combination that gave under or over dimensions for vertical wall.
Table 3
Effective and total dimensions obtained for horizontal wall.
Exp
no.
Image processed effective
width (mm)
Image processed effective
height (mm)
Image processed effective
area (mm2)
Image processed total
width (mm)
Image processed total
height (mm)
Image processed total
area (mm2)
1
2
3
4
5
6
7
8
9
10
11
12
19.71
20.01
20.30
20.29
20.14
22.41
21.45
19.95
19.79
20.02
19.91
19.99
6.12
6.06
6.72
5.92
5.95
6.07
5.93
5.98
6.10
6.02
6.04
5.99
120.59
121.33
136.81
120.30
119.90
136.08
127.11
119.33
120.72
120.48
120.26
119.75
24.90
26.92
31.50
30.61
29.40
28.55
29.27
32.21
30.03
27.64
31.53
31.93
7.02
7.30
8.12
7.03
6.91
6.55
6.99
7.95
6.98
8.16
7.73
7.96
159.30
168.84
194.09
168.57
172.47
158.91
173.32
209.14
176.48
222.67
200.89
206.51
Bold indicates the experiement combination that gave under or over dimensions for horizontal wall.
Fig. 13. Comparison of the equation predicted with the image processed effective width and height for vertical wall.
14.003 (Table 8). For horizontal wall, combination 6 gave the best
fitness values of the effective width i.e., 20.003 (Table 9) and combi­
nation 2 gave the best fitness values of the effective height i.e., 6.002
(Table 10). Fig. 15 depicts the fitness values of effective vertical wall
width (Fig. 15a), effective vertical wall height (Fig. 15b), effective
horizontal wall width (Fig. 15c) and effective horizontal wall height
(Fig. 15d) captured at various combinations of GA parameters.
h = 6.27 + 0.86 × 10− 2 P + 0.56 × 10− 2 Ss + 0.977Pf − 5.64773 × 10− 6 P × Ss
− 6.63 × 10− 5 Ss × Pf − 1.1 × 10− 3 P × Pf
(7)
b) Horizontal wall
a) Vertical wall
w = 18.82 − 1.71 × 10− 2 P − 1.41 × 10− 2 Ss − 0.586Pf + 1.81 × 10− 5 P × Ss
+1.54 × 10− 5 Ss × Pf + 6.4 × 10− 4 P × Pf
(6)
640
w = 19.71 + 0.13 × 10− 2 P − 0.154 × 10− 2 Ss + 0.051Pf
(8)
h = 5.68 + 0.11 × 10− 2 P − 0.66 × 10− 3 Ss − 0.031Pf
(9)
D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 14. Comparison of the equation predicted with the image processed effective width and height for the horizontal wall.
Table 4
Bounds and constraints used in GA for optimization.
Laser power ‘P’
Scanning speed ‘Ss’
Powder feed rate ‘Pf’
Effective vertical wall width
Effective vertical wall height
Effective horizontal wall width
Effective horizontal wall height
Table 7
Fitness values for the effective vertical wall width for different generations.
800 ≤ P ≤ 1000
500 ≤ Ss ≤ 700
5 ≤ Pf ≤ 10
Vw − 5.02 ≤ 0
Vw − 4.98 ≥ 0
Vh − 14.02 ≤ 0
Vh − 13.98 ≥ 0
Hw − 20.02 ≤ 0
Hw − 19.98 ≥ 0
Hh − 6.02 ≤ 0
Hh − 5.98 ≥ 0
Upper limit
Lower limit
Upper limit
Lower limit
Upper limit
Lower limit
Upper limit
Lower limit
Combination 4
Table 5
Values of GA parameters used in optimization.
GA parameters
Values
Number of variables
Population size
Population type
Creation function
Number of generations
Operator
Crossover function
Crossover fraction
Mutation function
Elite count
Nonlinear constraint algorithm
3
50 and 100
Double vector
Uniform
100
Roulette wheel
Single point
0.80 and 0.85
Adaptive feasible
1 and 2
Augmented Lagrangian
Table 6
Combination of GA parameters used in optimization.
Combination
Population size
Crossover fraction
Elite count
1
2
3
4
5
6
7
8
50
50
50
50
100
100
100
100
0.80
0.80
0.85
0.85
0.80
0.80
0.85
0.85
1
2
1
2
1
2
1
2
Generation
Laser power
Scanning speed
Powder feed rate
Fitness value
0
1
2
3
4
5
6
7
8
9
10
20
21
28
29
30
31
32
33
34
39
40
41
50
55
56
64
65
80
81
82
819.175
819.175
817.138
819.175
819.175
819.138
819.175
819.175
819.675
819.675
819.675
819.957
819.925
819.957
819.957
819.968
819.968
819.988
819.988
819.988
819.999
819.999
819.999
819.999
819.999
819.999
819.999
819.999
820.000
820.000
820.000
517.633
517.633
502.058
502.631
501.631
501.058
501.058
501.058
501.058
501.058
500.558
500.183
500.058
500.027
500.027
500.058
500.058
500.121
500.027
500.027
500.043
500.027
500.020
500.005
500.005
500.001
500.001
500.000
500.000
500.000
500.000
5.849
5.849
5.916
5.849
5.849
5.916
5.916
5.916
5.916
5.916
5.916
5.978
5.994
5.994
5.994
5.997
5.997
5.997
5.997
5.998
5.998
5.998
5.998
5.998
6.000
6.000
6.000
6.000
6.000
6.000
6.000
5.029
5.029
5.021
5.017
5.016
5.012
5.012
5.012
5.010
5.010
5.009
5.005
5.004
5.004
5.004
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
5.003
Italic indicates that the process parameters stabilizes after attaining minimum
fitness value.
study, for manual measurement of the deposition geometry features
Catia V5 software is used. To measure the values of width, height and
area, the deposition geometry is divided by known dimension grids. To
obtain the dimensions, a total number of grids covering the desired
features is multiplied with the single grid dimension.
4. Results and discussion
3. Manual measurement of deposition geometry features
4.1. Comparison of total dimensions against manual measurement
In present study, manual measurement method has been used to
validate the deposition geometry features extracted by image process­
ing. Currently, the additive manufacturing industries use a manual
measurement technique and measure the deposition width, height, and
area by using CAD software known as AutoCAD [28]. In the present
In this section, the image processed deposition geometry features
such as total width, height and area of the vertical and horizontal wall
were compared with the manual measuring method. Figs. 16 and 17
depicts the comparison of the total deposition width and height for the
641
D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Table 8
Fitness values for the effective vertical wall height for different generations.
Table 10
Fitness values for the effective horizontal wall height for different generations.
Combination 7
Combination 2
Generation
Laser power
Scanning speed
Powder feed rate
Fitness value
Generation
Laser power
Scanning speed
Powder feed rate
Fitness value
0
1
2
3
4
5
6
7
8
9
10
11
12
18
19
20
21
22
23
24
25
26
27
28
29
30
36
37
38
39
40
60
61
62
849.321
849.321
849.321
849.321
849.321
849.321
849.821
849.821
849.821
849.821
849.821
849.821
849.946
849.961
849.961
849.961
849.961
849.977
849.993
849.993
849.993
849.993
849.993
849.993
849.993
849.996
849.988
849.996
849.996
849.999
850.000
850.000
850.000
850.000
504.584
501.236
503.584
501.761
500.761
500.761
501.261
500.261
500.261
500.236
500.236
500.236
500.511
500.136
500.011
500.011
500.011
500.136
500.011
500.011
500.011
500.011
500.011
500.011
500.011
500.105
500.103
500.011
500.003
500.011
500.011
500.000
500.000
500.000
10.137
10.118
10.085
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.023
10.008
10.008
10.008
10.008
10.000
10.000
10.000
10.000
10.000
10.000
10.000
10.000
14.009
14.008
14.008
14.007
14.007
14.007
14.005
14.004
14.004
14.004
14.004
14.004
14.004
14.004
14.004
14.004
14.004
14.004
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
14.003
0
1
2
3
4
5
6
7
12
13
14
15
16
17
18
19
25
26
34
45
59
60
61
860.330
860.330
860.330
861.463
861.963
860.330
860.463
860.330
860.080
860.080
860.080
860.048
860.048
860.048
860.048
860.017
860.017
860.005
860.005
860.005
860.001
860.001
860.001
686.832
687.832
687.832
698.540
698.540
698.540
698.540
699.540
699.790
699.790
699.790
699.915
699.915
699.915
699.977
699.977
699.997
699.977
699.997
699.997
700.000
700.000
700.000
6.990
6.990
6.990
6.893
6.990
6.990
6.998
6.990
6.998
6.998
6.998
6.998
6.998
6.998
6.998
6.998
6.999
6.999
7.000
7.000
7.000
7.000
7.000
6.011
6.011
6.011
6.008
6.005
6.004
6.004
6.003
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
6.002
Italic indicates that the process parameters stabilizes after attaining minimum
fitness value.
vertical wall (Fig. 16a) and the horizontal wall (Fig. 17a) respectively.
Figs. 16b and 17b depict the total area of vertical and horizontal wall,
respectively. The maximum percentage difference between the features
obtained by image processing and manual measuring methods is 1.9%
for total deposition width (Fig. 16a), 2.1% for deposition height
(Fig. 16a), and 1.9% for total deposition area (Fig. 16b) for a vertical
wall. While for the horizontal wall, the maximum percentage difference
is 2.2% for total deposition width (Fig. 17a), 2.3% for deposition height
(Fig. 17a) and 2.3% for total deposition area (Fig. 17b). Examining the
uncertainty in the manual measured geometric features it was observed
that the inaccuracy of the measuring method and the importance of the
image processing technique to improve and automate the measuring
process.
Italic indicates that the process parameters stabilizes after attaining minimum
fitness value.
Table 9
Fitness values for the effective horizontal wall width for different generations.
Combination 6
Generation
Laser power
Scanning speed
Powder feed rate
Fitness value
0
1
2
3
4
5
6
7
8
9
10
11
19
20
21
22
29
32
46
56
57
58
805.763
805.064
801.307
801.307
800.825
800.307
800.307
800.307
800.057
800.057
800.057
800.057
800.025
800.088
800.088
800.025
800.010
800.017
800.002
800.002
800.002
800.002
727.615
727.615
729.774
729.774
729.615
729.774
729.774
729.774
729.774
729.774
729.774
729.899
729.978
729.961
729.990
729.978
729.996
729.992
729.999
730.000
730.000
730.000
7.009
7.009
7.033
7.009
7.009
7.009
7.009
7.009
7.009
7.009
7.009
7.009
7.009
7.001
7.001
7.001
7.001
7.001
7.000
7.000
7.000
7.000
20.014
20.014
20.007
20.005
20.005
20.004
20.004
20.004
20.004
20.004
20.004
20.003
20.003
20.003
20.003
20.003
20.003
20.003
20.003
20.003
20.003
20.003
4.2. Comparison of effective dimensions against manual measurement
Effective geometry features are the actual geometry features of the
final components that should match the CAD model. This section de­
scribes the comparison of the image processed effective width, height
and area of the vertical and horizontal wall with the manual measuring
method and dimensions of CAD model. Figs. 18 and 19 show the com­
parison of the effective deposition width, and height (Figs. 18a and 19a),
and deposition area (Figs. 18b and 19b) of the vertical and horizontal
wall respectively. The maximum percentage difference between image
processed and manual measured effective deposition width, height and
area of the vertical wall is 2.1%. While for the horizontal wall, the
maximum difference between the image processed and manual
measured effective deposition width is 2.1%, 1.9% for effective depo­
sition height and 2.1% for effective deposition area.
In the ideal deposition, the effective geometry features are equal to
the features designed in the CAD model. In the present study, features of
deposition geometries such as width, height and area in the CAD model
of the vertical wall (Fig. 3a) are 5 mm, 14 mm and 70 mm2, respectively
and of the horizontal wall (Fig. 3b) are 20 mm, 6 mm and 120 mm2,
respectively. Comparing the effective geometry feature with the CAD
model, it has been observed that the vertical wall manufactured by the
process parameters in experiments 3, 5, 6 and 12 gave unacceptable
dimensions. For the horizontal wall manufactured by the process
Italic indicates that the process parameters stabilizes after attaining minimum
fitness value.
642
D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 15. Fitness values of the (a) effective vertical wall width, (b) effective vertical wall height, (c) effective horizontal wall width and (d) effective horizontal wall
height obtained for various combination GA parameters.
643
D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 15. (continued).
Fig. 16. Deposition geometry features (a) total deposition width and height
and (b) total deposition area of the vertical wall.
Fig. 17. Deposition geometry features (a) total deposition width and height
and (b) total deposition area of the horizontal wall.
parameters in experiments 3, 6 and 7 gave unacceptable dimensions.
Process parameters in other experimental runs gave the effective di­
mensions in an acceptable range. Therefore, it can be said that some
combination of process parameters can give dimensions desired to CAD
model while some combinations are not suitable. Therefore, it become
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D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 19. Deposition geometry features (a) effective deposition width and height
and (b) effective deposition area of the horizontal wall.
Fig. 18. Deposition geometry features (a) effective deposition width and height
and (b) effective deposition area of the vertical wall.
Table 11
Optimized process parameters obtained from GA.
most important to identify the optimum combination of process pa­
rameters that can give the desired dimensions.
Vertical wall
4.3. Validation of optimized process parameters
Horizontal
wall
Table 11 shows the optimized combination of process parameters
using GA that can be used to manufacture vertical and horizontal wall. It
has been observed that, to manufacture the vertical wall with the
acceptable effective wall width the optimized value of laser power as
820 W, scanning speed as 500 mm/min and powder feed rate as 6 g/min
should be used. While for the acceptable effective wall height the opti­
mized value of laser power as 850 W, scanning speed as 500 mm/min
and powder feed rate as 10 g/min should be used. To manufacture the
horizontal wall with the acceptable effective wall width the optimized
value of laser power as 800 W, scanning speed as 730 mm/min and
powder feed rate as 7 g/min should be used. While for the acceptable
effective wall height the optimized value of laser power as 860 W,
scanning speed as 700 mm/min and powder feed rate as 7 g/min should
be used. Fig. 20 depicts the vertical wall (Fig. 20a) and the horizontal
wall (Fig. 20b) manufactured by the optimized process parameters.
Table 12 shows the values of the effective width, height and area ob­
tained using optimized process parameters for the vertical and hori­
zontal wall. Comparing the effective dimensions with the CAD model
Features
Power
(W)
Scanning speed
(mm/min)
Powder feed rate
(g/min)
Width
Height
Width
Height
820
850
800
860
500
500
730
700
6
10
7
7
dimensions it was observed that for the vertical wall, the maximum
percentage difference is 0.56% for width, 0.11% for height and 0.63%
for area. Consequently, for the horizontal wall, the maximum percent­
age difference is 0.09% for width, 0.46% for height and 0.53% for the
area. Comparison shows that the optimized process parameters can
manufacture the vertical and horizontal wall in good agreement with the
CAD model.
5. Conclusion
The present research work has been mainly focused on the devel­
opment of the robust image-processing technique to automate the pro­
cess of geometry features extraction, compare the extracted geometry
features with CAD model, and identify the optimum combination of
process parameters with a capability to manufacture components by
laser additive manufacturing process. Based on the objectives of the
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D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
Fig. 20. Deposition geometries manufactured by using modified process parameters on laser additive manufacturing process.
• Comparing CAD model dimensions against the dimensions of the
deposition geometry obtained by optimized process parameters
confirmed that for the vertical wall, the accuracy of measurement is
0.56% for width, 0.11% for height and 0.63% for area. Consequently,
for the horizontal wall, the accuracy of measurement is 0.09% for
width, 0.46% for height and 0.53% for the area.
Table 12
Effective dimensions obtained by optimized process parameters.
Vertical wall
Horizontal
wall
Image processed
effective width
(mm)
Image processed
effective height
(mm)
Image processed
effective area
(mm2)
5.028
5.021
20.018
20.013
14.010
14.016
6.015
6.028
70.442
70.374
120.408
120.638
In the past literature, it was observed that to measure the features of
the deposition geometries obtained by direct energy deposition process,
manual measurement techniques were used. This leads to compromise
the accuracy and the speed of the measuring process. However, in the
fast-growing additive manufacturing industry, to overcome this issue,
the proposed image processing technique will benefit the measuring
process by automatically extracting the complex features of the
component with high accuracy and less human interference. In future,
the image processing algorithm of this research will be further devel­
oped for the real-time feature extraction of the depositions done by laser
based additive manufacturing process.
current research following conclusions are drawn.
• The proposed robust methodology for image processing is highly
reliable to automate the process of deposition geometry feature
extraction and has ability to measure the total and effective geometry
features with good accuracy.
• Comparing the dimensions between the image processing and
manual measurement method, the accuracy is 2.1% for the total
width and 2.0% for the total height of vertical wall and the accuracy
for the total width and height of the horizontal wall is 2.2% and 2.3%
respectively.
• Comparison of the effective geometric features by image processing
against the manual measuring method it was observed that the
maximum percentage difference for vertical wall is 2.2% for width,
2.1% height and 2.1% for area. While for horizontal wall, the
maximum percentage difference is 2.2% for width, 1.8% for height
and 2.2% for area.
• The maximum percentage error between the geometric feature
extracted by image processing technique is less than 3% this signifies
that the ability and accuracy to measure the geometric features of
additive manufactured.
• The optimized process parameters suitable for effective vertical wall
width are, the laser power as 820 W, scanning speed as 500 mm/min
and powder feed rate as 6 g/min. For effective vertical wall height
are, the laser power as 850 W, scanning speed as 500 mm/min and
powder feed rate as 10 g/min.
• The optimized process parameters suitable for effective horizontal
wall width are, the laser power as 800 W, scanning speed as 730 mm/
min and powder feed rate as 7 g/min. For effective horizontal wall
height are, the laser power as 860 W, scanning speed as 700 mm/min
and powder feed rate as 7 g/min.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Acknowledgements
The authors acknowledge the financial support given under SEED
money scheme of Birla Institute of Technology Mesra, Ranchi, Jhark­
hand (India).
Appendix A. Supplementary data
Supplementary data to this article can be found online at https://doi.
org/10.1016/j.jmapro.2021.07.064.
References
[1] Ranjan R, Khan AR, Parikh C, Jain R, Mahto RP, Pal S, et al. Classification and
identification of surface defects in friction stir welding: an image processing
approach. J Manuf Process 2016;22:237–53. https://doi.org/10.1016/j.
jmapro.2016.03.009.
646
D.B. Patil et al.
Journal of Manufacturing Processes 69 (2021) 630–647
[2] Bhat NN, Kumari K, Dutta S, Pal SK, Pal S. Friction stir weld classification by
applying wavelet analysis and support vector machine on weld surface images.
J Manuf Process 2015;20:274–81. https://doi.org/10.1016/j.jmapro.2015.07.002.
[3] Yuan Y, Zhang D, Zhu H, Ehmann KF. Machining of micro grayscale images on
freeform surfaces by vibration-assisted cutting. J Manuf Process 2020;58:660–7.
https://doi.org/10.1016/j.jmapro.2020.08.051.
[4] Rane K, Castelli K, Strano M. Rapid surface quality assessment of green 3D printed
metal-binder parts. J Manuf Process 2019;38:290–7. https://doi.org/10.1016/j.
jmapro.2019.01.032.
[5] Duda RO, Hart PE. Use of the Hough transformation to detect lines and curves in
pictures. Commun ACM 1972;15:11–5. https://doi.org/10.1145/361237.361242.
[6] Kiryati N, Eldar Y, Bruckstein AM. A probabilistic Hough transform. Pattern
Recognit 1991;24:303–16. https://doi.org/10.1016/0031-3203(91)90073-E.
[7] McLaughlin RA. Randomized Hough transform: improved ellipse detection with
comparison. Pattern Recognit Lett 1998;19:299–305. https://doi.org/10.1016/
S0167-8655(98)00010-5.
[8] Chen S, Xia R, Zhao J, Chen Y, Hu M. A hybrid method for ellipse detection in
industrial images. Pattern Recognit 2017;68:82–98. https://doi.org/10.1016/j.
patcog.2017.03.007.
[9] Kasemir KU, Betzler K. Detecting ellipses of limited eccentricity in images with
high noise levels. Image Vis Comput 2003;21:221–7. https://doi.org/10.1016/
S0262-8856(02)00155-5.
[10] Alfano M, Di Girolamo G, Pagnotta L, Sun D. Processing, microstructure and
mechanical properties of air plasma-sprayed ceria-yttria co-stabilized zirconia
coatings. Strain 2010;46:409–18. https://doi.org/10.1111/j.14751305.2009.00659.x.
[11] Thao DT, Kim IS. Interaction model for predicting bead geometry for lab joint in
GMA welding process. Arch Comput Mater Sci Surf Eng 2009;1:237–44.
[12] Sathiya P, Aravindan S, Ajith P, Arivazhagan B, Haq AN. Microstructural
characteristics on bead on plate welding of AISI 904 L super austenitic stainless
steel using gas metal arc welding process. Int J Eng Sci Technol 2011;2:189–99.
https://doi.org/10.4314/ijest.v2i6.63710.
[13] El Rayes M, Walz C, Sepold G. The influence of various hybrid welding parameters
on bead geometry. Weld J 2004;83:147.
[14] Cao Y, Zhu S, Liang X, Wang W. Overlapping model of beads and curve fitting of
bead section for rapid manufacturing by robotic MAG welding process. Robot
Comput Integr Manuf 2011;27:641–5. https://doi.org/10.1016/j.
rcim.2010.11.002.
[15] Singh AK, Dey V, Rai RN, Debnath T. Weld bead geometry dimensions
measurement based on pixel intensity by image analysis techniques. J Inst Eng
(India) C 2019;100:379–84. https://doi.org/10.1007/s40032-017-0392-8.
[16] Tabernero I, Lamikiz A, Martínez S, Ukar E, Figueras J. Evaluation of the
mechanical properties of Inconel 718 components built by laser cladding. Int J
[17]
[18]
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
647
Mach Tool Manuf 2011;51:465–70. https://doi.org/10.1016/j.
ijmachtools.2011.02.003.
Putthawong S, Koomsap P, Chansri N. Image-based adaptive crosshatch toolpath
generation for laminated object manufacturing. Virtual Phys Prototyp 2014;9:
233–49. https://doi.org/10.1080/17452759.2014.954823.
Vaidya R, Anand S. Image processing assisted tools for pre- and post-processing
operations in additive manufacturing. Procedia Manuf 2016;5:958–73. https://doi.
org/10.1016/j.promfg.2016.08.084.
Yao B, Imani F, Sakpal AS, Reutzel EW, Yang H. Multifractal analysis of image
profiles for the characterization and detection of defects in additive manufacturing.
J Manuf Sci Eng 2018:140. https://doi.org/10.1115/1.4037891.
Straub J. Initial work on the characterization of additive manufacturing (3D
printing) using software image analysis. Machines 2015;3:55–71. https://doi.org/
10.3390/machines3020055.
Wang T, Zhu YY, Zhang SQ, Tang HB, Wang HM. Grain morphology evolution
behavior of titanium alloy components during laser melting deposition additive
manufacturing. J Alloys Compd 2015;632:505–13. https://doi.org/10.1016/j.
jallcom.2015.01.256.
Patil D, Nigam A, Mohapatra S. Automation of geometric feature computation
through image processing approach for single-layer laser deposition process. Int J
Comput Integr Manuf 2020;33:895–910. https://doi.org/10.1080/
0951192X.2020.1815843.
Canny J. A computational approach to edge detection. IEEE Trans Pattern Anal
Mach Intell 1986;PAMI-8:679–98. https://doi.org/10.1109/
TPAMI.1986.4767851.
Gunaraj V, Murugan N. Prediction and optimization of weld bead volume for the
submerged arc process –part 2. Weld J Res Suppl 2000;11:331–8.
Liu H, Qin X, Huang S, Jin L, Wang Y, Lei K. Geometry characteristics prediction of
single track cladding deposited by high power diode laser based on genetic
algorithm and neural network. Int J Precis Eng Man 2018;19:1061–70. https://doi.
org/10.1007/s12541-018-0126-8.
Kumar A, Maji K. Selection of process parameters for near-net shape deposition in
wire arc additive manufacturing by genetic algorithm. J Mater Eng Perform 2020;
29:3334–52. https://doi.org/10.1007/s11665-020-04847-1.
Conn AR, Gould NIM, Toint PL. A globally convergent augmented Lagrangian
algorithm for optimization with general constraints and simple bounds. SIAM J
Numer Anal 1991;28:545–72. https://doi.org/10.1137/0728030.
Sawant MS, Jain NK, Nikam SH. Theoretical modeling and finite element
simulation of dilution in micro-plasma transferred arc additive manufacturing of
metallic materials. Int J Mech Sci 2019:164. https://doi.org/10.1016/j.
ijmecsci.2019.105166.