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Two common defects present in the LMD process are

porosities and cracks. Such defects are highly detrimental inthe case of critical applications, such as in the aerospace

industry. There are two main types of porosities; namely, gas

porosity and porosity caused due to lack of fusion between

deposited layers. Both of these kinds of porosities were

investigated in LMD (Ng et al., 2009). Figure 2 shows a void

present on an insert for a diecasting die that makes chainsaw

cylinders. The insert had to be re-machined and LMD had to

be performed again. There is considerable rework cost

including material, labor, and time which need to be

eliminated or minimized. The porosity caused due to lack of

fusion is due to the inability of the melt pool to melt the

powder particles due to low specific energy. This can be

caused by incorrect or varying standoff distance between the

deposition nozzle and substrate which leads to defocussing of

the laser beam and loss of power. The size and composition ofthe substrate also have a major role to play in heat diffusion

and size of the melt pool. Gas porosity is mostly due to overly

high powder flow rate which traps the shielding gas within the

melt pool and also lowers the specific energy of the melt pool.

Since most of the powders used in LMD are gas atomized,there is a possibility of entrapped gas within the powder

particles themselves. The Marangoni flow in the melt pool

was also determined to be a cause of gas retention bubbles

within the melt pool, causing large pores. Cracking typically

occurs when there is a difference in thermal coefficient of the

material being added and the substrate. It also occurs with

powder contamination in the powder feeder. All these defects

contribute to the variation in mechanical properties of each

deposit and must be detected as formed so as to take

corrective action. This proposed research is an effective

method to alert the user about a possible defect. The exact

type of defects, if interested, will still need to be investigated.

Since an LMD process deposits metal layer-by-layer, it is

possible to ensure the quality of the metal deposition process by

continuously monitoring the top layer, as it is being deposited,to ensurethat no defectsare present. Thetechniqueproposedin

this paper is based on this scenario, to detect defects in each

layer, and if a defect is found, certain actions, such as

machining, closed loop control, laser remelting, or additive

remedies, can be undertaken to resolve the problem.

Previous methods of defect detection

The size of the melt pool is one of the most important

parametersin LMD. In order to ensure uniformityof deposits, it

is necessary to ensure constant melt pool size and geometry.

Hence, monitoring the size and shape of the melt pool is an

important part of the quality control process. Conventional

image vision processing techniques use infrared filters along

with a high speed shutteralong with synchronizing the laser with

the shutter Kizaki et al. (1993). These proprietary vision

systems are relatively expensive to purchase and maintain.

Previous work in this field includes Kinsman and Duley (1993)

who used the number of bright pixels in the image to determine

the size of themeltpool. Some systems involve turning thelaser

off while taking a melt pool measurement. Voelkel and

Mazumder (1990) used an illuminating argon-ion laser to

illuminate, online, thewelding melt pool created by a CO2 laser.

Ciliberto e t a l. (2002) used nondestructive testing,

specifically ultrasound testing to detect porosities in

aeronautical structures. Roge et al. (2003) used the dielectric

Figure 1 Left figure: overview of LMD process illustrating formation of melt pool and solidified track during deposition in positive x-axis and rightfigure: actual system

Figure 2Small void discovered on surface of insert after LMD processand machining

Vision-based defect detection in laser metal deposition process

Shyam Barua, Frank Liou, Joseph Newkirk and Todd Sparks

Rapid Prototyping Journal

Volume 20 Number 1 2014 7786

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properties of the substrate to detect changes in capacitance due

to porosity or other defects. This was compared to destructive

testing using metallography and vacuum voltric measurement

using nitrogen absorption. Most of the above defect detection

methods areperformedpost process, when it is difficult to repair

and eliminate defects. Vision-based systems have become

popular as a defect detection system because of ease of

automation and reliability.Conventional vision defect detection techniques and vision

systems are relatively expensive to purchase and maintain. There

is also an added complexity and failure rate associated with the

process. Much work has been performed in studying the melt

pool formed during LMD. Meriaudeauet al.(1996) used CCD

cameras to obtain surface temperature measurements to

determine the mass flow rate of powder and also monitor the

height and width of the deposited track. A mathematical model

was developed for the three dimensional heat temperature

distribution of a laser with a Gaussian distribution power profile

(Pinkerton and Li, 2004). There are other related models.

A mathematical model to predict the temperature distribution

within the human eye when subjected to a laser source (193 nm)

was presented (Shahi et al., 2 01 0) . T he transient

three dimensional temperature distribution for a laser sintered

duraform fine polyamide part by a moving Gaussian laser beam

(Singh and Prakash, 2010). Previous approaches (Sparks et al.,

2009) to the problem of imaging the laser melt pool involved a

simple filter design to remove the infrared radiation emitted,

coupled with a short pass filter to remove wavelengths longer

than 700 nm. A 300mm pinhole lens was further used to cut

down the signal reaching the CMOS sensor.

Another approach, widely used in the industry, is the laser

strobe technique, in which a short duration pulsed laser is

projected onto the melt pool. The camera shutter is

synchronized to be open only during the pulse duration.

During the pulse duration, the main laser may be switched

off, thus ensuring that the illuminating laser intensity is more

than that of the laser creating the melt pool. Iravani-Tabrizipour and Toyserkani (2007) used a trinocular optical

detector composed of three CCD cameras and interference

filters for real time measurement of deposition height.

A neural network model was used to determine the optimal

threshold value f or their image. T he drawbacks of

concentrating on the melt pool for defect detection include

instability of the melt pool due to Marangoni effects, powder,

shielding gas, etc. The melt pool shape is also transient and

porosities may be resolved during solidification. The general

steps used in defect detection vision systems in LMD include:. Image acquisition system. This is typically an advanced

camera with a high speed mechanical shutter. The

shutters function is to prevent overexposure of the

image sensor due to high intensity light inherent in LMD.

Some additional components such as filters, beam

splitters, and magnification lenses which are specific to

the LMD system may be required.. Image processing. The data present in the image is

interspersed with noise signals. Noise leads to loss of

signal quality and increases error in the system. Hence, it

is necessary to preprocess the image to minimize noise.

Gray scaling, thresholding, convolution and other

morphological operations are some of the preprocessing

steps before applying the defect detection algorithm.. Detection algorithm. From the images obtained it is

necessary to extract the relevant data required such as

melt pool size, length, depth, etc. This is system

dependent depending on the kind of input required by

the control system. The proposed research is to use

gradient variation instead of the traditional pore detection.

Thus, it is more sensitive in terms of defect detection for

metal deposition process.. Control system. The output of the detection algorithm is

used as a factor in gauging the quality of deposition. Thisinformation is relayed to the control system which can

make appropriate changes in processing parameters to

ensure uniformity and quality of deposition. Once changes

in the process are detected, adjustments are made in the

control factors such as laser power, table velocity, etc. to

compensate accordingly.

Concept

The intensity of electromagnetic radiation and its wavelength has

been related to the temperature of the object through black body

radiation theory. For an ideal black body in a vacuum

environment, the relation between spectral radiance,

wavelength, and temperature is given by the Planck equation:

Ll C1

l5 expC2=l T 2 1 1

where Ll is the spectral radiance, Watts/steradian/m2, C1 and C2

are radiation constants, l is the wavelength of body in vacuum,

and T is thesurface temperature of thebody expressedin Kelvin.

The stainless steel substrate upon which the deposition is

being made is not a perfect black body. Thus, there is a

variance in the emissivity of the substrate, which is below 1.

Figure 3 shows the relationship between spectral radiance,

wavelength, and temperature of a black body. Spectral

radiance of thermal emitters at unit emissivity is derived

from Plancks equation. These curves give the radiance of a

black body at various temperatures (in degrees Kelvin)

(Kral and Matthews, 1996). As temperature of the objectincreases, there is an increase in spectral radiance with

decrease in wavelength of emitted light. The highest

Figure 3 Spectral radiance of thermal emitters at unit emissivityderived from Plancks equation

Parameter Values in K

Wavelength, , m

SpectralRadiance,

L

B(,

T),W/(cm2)(sr)(m)

104

102

10

102

104

106

108

0.1 0.5 1.0 5 10 50 100

6,000

5,000

4,000

3,000

2,000

1,500

1,000

800

600

400

273

200

Source: Kral and Matthews (1996)





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temperature recorded by an optical pyrometer in the melt

pool during LMD was 2,700 K. Hence, the region of interest

(ROI) is concentrated around the wavelength of 0.5-0.8 mm.

During LMD, the laser melts the substrate and the melt pool

is at the highest temperature. The rest of the substrate acts as a

heat sink and heat flows from the heat source (melt pool) to the

heat sink. During heat transfer across a metallic substrate, heat

flow is interrupted or disturbed by defects such as porosities orcracks (Yang et al., 2011). This leads to an increase in

temperature in the region around the defect. Due to the nature

of LMD, when three dimensional parts are made using tracks,

we seek to determine the presence of defects by observing the

surface temperature of the deposited track. Defects should lead

to sudden deviation from the expected temperature gradient of

the substrate, which would indicate the presence of a defect.

LMD system

The LMD system consists of a 1 kW laser diode system which

was run in continuous wave (CW) mode. The laser is coupled

to a five axis vertical machining center which is used for post

process machining after LMD. LabVIEW control system is

used to control various process parameters in LMD.

The powder used is gas atomized 316L stainless steel with a

meshsizeof280/270. Theelemental composition is shown in

Table I. The SEM image of the powder as shown in Figure 4

shows that the powder particles are generally non-uniform in

shape and size and may contain internal voids. Powder is fed

through a powder feeder system with argon as the carrier gas.

Argon gas is also supplied as a shielding gas through ports in the

cladding head to reduce oxidation of the deposit.

In the LMD process, there are certain control parameter

values which are known to yield good deposits for a particular

powder and substrate material. In this case, LMD was

performed with standard parameters for depositing SS316L

powder as shown in Table II. The deposit was carried out to a

height of eight layers (about 4 mm total) with the depositionbeing performed only in the positive direction along the x-axis.

Image acquisition system overview

As observed in Figure 5, the camera is mounted onto a tripod

which is placed such that the camera is perpendicular to the

direction of deposition. This is because, with a single camera,

the field of view is restricted to only one axis while

maintaining depth of focus. Macro lenses are used to obtain

a close up view of the deposited track surface. Figure 6 is an

image of the incandescent track sent to the computer for

analysis during deposition so that the defects are detected

on demand. Neutral density filters are fitted onto the lens to

reduce the intensity of the image and avoid saturation of the

sensor. They also serve the dual purpose of protecting the lens

from reflected powder particles off the substrate. The camera

is connected to a laptop using a USB cable.

Although accurate temperature may not be needed to detect

defects, RAW image is obtained from the camera in which RGB

values of each pixel are linearly related to the intensity of light

Figure 4 SEM image of SS316L metal powder used in depositionprocess

Figure 5Experimental setup of camera with respect to nozzle whereinthe camera is perpendicular to travel of table in the x-axis

Nozzle

Camera mounted perpendicular

to direction of depositionDesposited track

Note: Deposition is performed only in the positive direction

of x-axis

Table I Composition of SS316L powder

Element Percentage

Carbon 0.03

Phosphorus 0.045

Silicon 1

Nickel 10-14

Iron 61.9-68.9

Manganese 2

Sulfur 0.03

Chromium 16-18

Molybdenum 2-3

Table II LMD process parameters

Parameter Value

Laser power 1,000 Watt

Powder feed rate 8 g/min

Table velocity 250mm/min

Length of track 20mm

Layer thickness About 0.5 mm

Layer width About 2.5 mm

Powder utilization About 80 percent





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collected at that pixel location. These RGBvalues arecalibrated

with known temperature readings to calibrate the camera for

temperature measurement.For calibration of the camerasensor

with respect to temperature, a stainless steel test substrate isplaced in a box furnace and heated incrementally in steps of

508C from 08C to 1,0008C. The measurements were tabulated

and are shown in Figure 7(a). Images of the surface are taken at

periodic intervals using the SLR camera. As shown in

Figure 7(b), there is a visible change in color of substrate with

heat application.

Regression analysis is performed using a general linear

model and the resultant equation for determining surface

temperature using RGB values is calculated up to three

significant digits (Panditrao and Rege, 2009). The regression

equation is shown in equation (2):

T 19300:603R0:706G 2 4:98B 2

where T is the calculated temperature value in degreesCelsius, R, G, and B are the red, green, and blue values of a

pixel, respectively. This equation is used to approximate

temperature of each pixel in the ROI. Although emissivity

may affect the sensor reading, since the proposed research is

focus on the pattern of the temperature gradient, the

emissivity may not be an important factor in this approach.

Defect simulation

The defects present in the laser deposition process are

porosities and cracks which are normally detected using

ultrasound method and X-ray computed tomography

(XRCT) (Wang et al., 2009). These methods are time

consuming and expensive such that on demand detection is

not possible. Pores were simulated by drilling holes in SS316

substrates (Figure 8) and by performing deposition over

the holes. The holes were filled with SS316 powder (same as

the powder used for deposition). The powder was filled in the

holes using a measuring spoon such that there exists

40-60 percent porosity in the hole. This ensures that the

laser is unable to melt the holes completely and the air

bubbles have insufficient time to escape, thereby creating a

porous deposit. The track width of this LMD process is

approximately 0.100 and the lay height is about 0.200. The

substrate is substantially larger than the track and acts as a

heat sink during LMD.

Two stainless steel 316L metal substrates of dimensions 200

by 100 by 0.2500 were joined together in a vice and tack welded

at both ends as shown in Figure 9. LMD was carried out over

the crack so that any deviation in the temperature gradientcan be observed. The deposit was created as a thin wall due to

better thermal radiance properties through the thin wall.

Image acquisition

Initially, the camera is lined up such that it is in focus with the

same plane as the melt pool. A 180 mm macro lens is used to

obtain sufficient magnification of the track. All other settings

such as shutter speed, aperture, and ISO on the camera are

the same as the calibrated settings. The melt pool is kept out of

the viewfinder so as to not damage the expensive CMOS

image sensor. The camera is able to take pictures on demand

and transfer them to thecomputerfor processing.Thereis a cycle

time delay between consecutive images which consists of time

takento capture theimage and time takento transfer theimage tothe computer. The time taken to capture an image varies from

0.13 to 0.33 s depending on theselectedframe rate of thecamera

and imageresolution. Thedownload time is dependentupon the

size of the image and is approximately 7,324 kB/s. Using an

image resolution of 5,184 pixels by 3,456 pixels and a memory

size of 2.9 MB, the cycle time is approximately 1.4 s.

Figure 6Incandescent track demonstrating the color gradient from themelt pool to the beginning of the deposited track

Note:Some sintered and unmelted particles of powder are also

visible, which are potential sources of noise in the system

Figure 7Calibration of RGB values with color temperature

(a) (b)

R G B Temperature (Celsius)

2

0

0

0

0

15

212

167

650

700

750

800

850

900

950

1,000

251

254

254

255

255

244

252

250

1

2

145

223

255

255

255

254

Notes:Calibration of RGB values vs. actual temperature of stainless steel substrate at 650to 850;color gradient change with increase in temperature (in color)





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Region of interest

Selecting a ROI, in this case the deposited track, helps to speed

up computer vision operations by allowing the code to process

only a small sub-region of the image (Bradski and Kaehler,

2010). In defect detection, it helps to correct for differences in

the positional relationship between the camera and the

substrate. Contour detection is used to obtain a bounding box

and these coordinates are used to set the ROI. Line scans of the

ROI are used toobtain RGB values ofeachpixel inthe linescan.

Due to the presence of sintered powder on the deposited track,

and rebounding powder particles, there is noise present in the

data in the form of extreme values, as seen in Figure 10(a).

A median of 3 line scans are performed to obtain the required

RGB values of the pixel. The RGB values are used in equation

(2) to calculate surface temperature of the track. Outlier values

atthe beginningandend ofthelinescanarefilteredoutto obtain

the temperature gradient. Data smoothing is performed using

moving average method to smooth out noise while preserving

useful data. Moving average method is essentially a low pass

filter in which the local average is computed for each

temperature value so that false noise can be minimized. The

final temperature gradient obtained is seen in Figure 10(b).

The temperature gradient obtained is approximately linear

and straight line curve fitting was performed to obtain the

least squares fitting. The residual values are calculated for a

batch of images during good deposition. The cases in

Figure 10(a) and (b) were conducted at different time. While

a linear pattern as shown in Figure 10(b) is a normalpattern, Figure 10(a) shows a drastic different pattern when a

defect is encountered. This shows the feasibility of

differentiating the two cases.

Post defect detection

During deposition, if a defect is detected, corrective action

can be taken on the defective track. Conventionally, laser

scanning is performed on the defective track to eliminate

the defect. If unsuccessful, the deposited track is machined off

and LMD is performed again, allowing for changes in part

geometry after the machining operation. In either case,

significant cost savings are achieved, emphasizing the

importance of on demand defect detection.

Results

The cooling curve for the deposited track with no defects is

shown in Figure 11. This can be compared to a bad deposit

with simulated porosities, as shown in Figure 12. The cooling

curve for the porous deposit is shown in Figure 13.

At the beginning and end of a deposited track, there are less

pixels available for image processing. Curve fitting techniques

cannot be reliably applied when the sampledata set is small. The

powder particlesreflecting offthe substrate alsopose a significant

amount of noise in the signal. Conventional techniques of

Figure 8 1/800 diameter holes drilled on substrate and filled withSS316L powder to simulate porosities

Figure 9Stainless steel substrates tack welded together to simulate 1/64 00 crack over which LMD is processed





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smoothing out noise using morphological operations such as

erosion and dilationresult in loss of signal dataas well. Currently,

the camera being used for image acquisition is a Canon EOS 7D

model. This is connected via USB to a computerwitha 2.2 GHz

dual core processor with 4 GB RAM. The software used for

acquiring images from the camera is GPhoto2. The entire time

taken for defect detection can be split up as:

. actual time taken by the camera sensor to process the

image;. transfer time to computer; and. computational time.

The camerahas the ability to take seven frames per second (fps) in

burst mode. The software used to acquire the images, GPhoto2

has a minimum exposure time of 1 s. Hence, there exists

Figure 12Image obtained during deposition over simulated 0.12500 diameter porosities showing the concentration of heat near the porosity

Figure 10Straight line fitting performed on temperature gradient

Temperature vs Pixels2,000

1,800

1,600

1,400

1,200

1,000

800

600

Temperaturein

Celcius

Temperaturein

Celcius

050100150200250300350400

pixel pixel

70 60 50 40 30 20 10 01,000

1,100

1,200

1,300

1,4001,500

1,600

1,700

1,800Temperature vs Pixels

(a) (b)

Notes: Initial temperature gradient obtained from line redundant scan; temperature gradient after chopping data

at beginning and end of curve

Figure 11Temperature measured across horizontal line of pixels in a good deposit demonstrating linear decrease in temperature

1,800

1,700

1,600

1,500

1,400

1,300

1,200

1,100

1,000

Temperatureincelcius

60 50 40 30 20 10 0

pixel

Temperature vs Pixels





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An automated defect detection program is implemented with

simulated defects and demonstrates proof of concept in the LMD

process. Although the temperature calibration is discussed in this

paper, the actual defect detection is based on pattern change

instead of the actual temperature change. Thus, accuratetemperature calibration is not needed.

References

Bradski, G. and Kaehler, A. (2010),Learning OpenCv-Computer

Vision with the OpenCv Library, available at: www/coursehero.

com/textbooks/43027-Learning-OpenCV-Computer-Vision/-

with-the-OpenCV-Library (accessed October 29, 2010)

Ciliberto, A., Cavaccini, G., Salvetti, O., Chimenti, M.,

Azzarelli, L., Bison, P.G., Marinetti, S., Freda, A. and

Grinzato, E. (2002), Porosity detection in composite

aeronautical structures, Infrared Physics & Technology, Vol. 43,

pp. 139-143.

Iravani-Tabrizipour, M. and Toyserkani, E. (2007), Animage-based feature tracking algorithm for real-time

measurement of clad height, Machine Vision and

Applications, Vol. 18 No. 6, pp. 343-354.

Kinsman, G. and Duley, W.W. (1993), Fuzzy logic control of

CO2 laser welding, Proceeding of the International Congress

on Application of Lasers and Electro-Optics, pp. 160-167.

Kizaki, Y., Azumi, H., Yamazaki,S., Sugimoto,H. and Takagi, S.

(1993), Phenomenological studies in laser cladding part 2:

thermometrical experiments on the meltpool, Japanese

Journal of Applied Physics, Vol. 32, pp. 213-220.

Kral, J. and Matthews, E.K. (1996), Pyrolaser & pyrofiber

infrared temperature measurement with automatic

emissivity correction, paper presented at Metal 96

International Fair of Metallurgy Ostrava, Republic of

Czechoslovakia, May, available at: www.pyrometer.com/

paper0596.htm (accessed September 1, 2012)

Meriaudeau, F., Truchetet, F., Dumont, C., Renier, E. and

Bolland, P. (1996), Acquisition and image processing

system able to optimize laser cladding process, Proceedings

of ICSP96, pp. 1628-1631.

Ng, G., Jarfors, A., Bi, G. and Zheng, H. (2009), Porosity

formation and gas bubble retention in laser metal

deposition, A pp li ed P hy si cs A : Mater ia ls Sci ence

& Processing, Vol. 97, pp. 641-649.

Panditrao, A.M. and Rege, P.P. (2009), Temperature

estimation of visible heat sources by digital photography

and image processing, I EE E Tr ansa ctio ns o n

Instrumentation and Measurement, Vol. 59, pp. 1167-1174.

Pinkerton,A.J. and Li, L. (2004), An analytical model of energy

distribution in laser direct metal deposition, Proceedings of the

Institution of Mechanical Engineers. Part B: Journal ofEngineering Manufacture, Vol. 218 No. 4, pp. 363-374.

Roge, B., Fahr, A., Gigure, J. and McRae, K. (2003),

Nondestructive measurement of porosity in thermal

barrier coatings, Journal of Thermal Spray Technology,

Vol. 12, pp. 530-535.

Shahi, S., Khorvash, M., Harun, S.W., Ahmad, H. and

Golnabi, H. (2010), The temperature distribution eximer

laser source (193nm) within a human eye and review on

some side-effects, Australian Journal of Basic and Applied

Sciences, Vol. 4 No. 5, pp. 724-730.

Singh, A.K. and Prakash, R.S. (2010), DOE based three-

dimensional finite element analysis for predicting density of

a laser-sintered part, Rapid Prototyping Journal, Vol. 16

No. 6, pp. 460-467.Sparks, T.E., Tang, L. and Liou, F. (2009), Development of

a melt pool tracking vision system for laser deposition,

Proceedings of the Twentieth Solid Freeform Fabrication

Symposium, Austin, Texas, August3-5.

Voelkel, D.D. and Mazumder, J. (1990), Visualization of a laser

melt pool,Applied Optics, Vol. 29 No. 12, pp. 1718-1720.

Wang, L., Pratt, P., Felicelli, S.D., El Kadiri, H., Berry, J.T.,

Wang, P.T. and Horstemeyer, M.F. (2009),Pore formationin

laser-assisted powder deposition process, Journal of

Manufacturing Science and Engineering, Vol. 131, pp. 51-58.

Yang, S., Tian, G.Y., Abidin, I.Z. and Wilson, J. (2011),

Simulation of edge cracks using pulsed eddy current

stimulated thermography, J. Dyn. Sys., Meas., Control,

Vol. 133, pp. 9-11.

Further reading

Bi, G., Gasser, A.,Wissenbach, K.,Drenker, A. and Poprawe, R.

(2006), Investigation on the direct laser metallic powder

deposition process via temperature measurement, Applied

Surface Science, Vol. 253, pp. 1411-1416.

Boddu, M.R., Musti, S., Landers, R.G., Agarwal, S. and

Liou, F.W. et al. (2001), Empirical modeling and vision

based control for laser aided metal deposition process,

Solid Freeform Fabrication Proceedings, pp. 452-459.

Figure 15Image obtained during change in travel speed during deposition

Note:The deposit is not uniform and temperature concentrations

near the 1/64'' crack defects are visible





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Jackson, A.W. and Gossard, A.C. (2007), Thermal imaging

of wafer temperature in MBE using a digital camera,

Journal of Crystal Growth, Vol. 301, pp. 105-108.

Paul, C.P., Ganesh, P., Mishra, S.K., Bhargava, P., Negi, J.

and Nath, A.K. (2007), Investigating laser rapid

manufacturing for Inconel-625 components, Optics

& Laser Technology, Vol. 39, pp. 800-805.

Susan, D.F., Puskar, J.D., Brooks, J.A. and Robino, C.V.

(2006), Quantitative characterization of porosity in

stainless steel LENS powders and deposits, J. Materials

Characterization, Vol. 57, pp. 36-43.

Corresponding author

Frank Liou can be contacted at: [email protected]

To purchase reprints of this article please e-mail: [email protected]

Or visit our web site for further details: www.emeraldinsight.com/reprints





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