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Shin Journal Publications - Purdue

Archival Journal Publications


Zhang, B., Hong, K.M., and Shin, Y.C.,

�Deep-Learning-Based Porosity Monitoring of Laser Welding Process�, Manufacturing Letters, Volume 23, January 2020, Pages 62-66. 2.

Lei, S., Zhao, X., Yu, X., Hu, A., Vukelic, S., Jun, M.B.G., Joe, H.E., Yao, Y.L.

Y.C., �Ultrafast Laser Applications in Manufacturing Processes: A State of 3.

Wang, X., Chen, K., de Vasconcelos,

Communications, 11, 211 (2020). 4.

during Laser Welding of Al 6061 via 2D and 3D Phase Field Models�, Computational Material Science.

109291, 2020.� 5.

Manufacturing Processes, Volume 45, September 2019, Pages 579-587. 6.

synthesis of Zr-Based Bulk Metallic Glass

Ductility via Laser Direct Deposition�, Intermetallics, Volume 111,

August 2019, 106503. 7.

28, August 2019, pp. 8.

Weld Strength of Laser Welded Joints of AA6061-T6 and TZM Alloys via Novel

Dual-Laser Warm Laser Shock Peening", International Journal of Advanced Manufacturing Technology, September 2019, Volume 104, Issue1�4, pp 907�919. 9.

Ultra-Thin 316 Stainless Steel Plate Using Pulsed Laser Welding Process�, Optics and Laser Technology, Volume

119, November 2019, 105583. 10.

Volume 268, June 2019, Pages 201-212 11.

Volume 39, March 2019, Pages 146-159.

Processing, March 2019, Volume 6, Issue1, pp 41�58.�

10.1007/s40516-019-0079-5 13.

2019, 141(5), 051001 (Mar 04, 2019). 14.

and Shin, Y.C., �Additive Manufacturing of Ti6Al4V Alloy: A Review�, Materials and Design, Volume 164, 15

February 2019, 107552. 15.

Materials Processing Technology, Volume 266, April 2019, Pages

173-183.� 16.

Steels during Rolling Processes via Predictive Modeling�, International Journal of Mechanical

Sciences, Volume 150, January 2019, Pages 576-583.

10.1016/j.ijmecsci.2018.10.061 17.

Journal of Manufacturing Science and Engineering, Jan. 2019, Vol.

011001.� 18.

and Shin, Y.C., �Crack Formation within Ceramics via Coupled Multiscale

204, 2018, Pages 517-530. 19.

512, 15 December 2018, Pages 268-275. 20.

Processes, Volume 35, October 2018, Pages 547-558.� 21.

Volume 159, 5 December 2018, Pages 212-223.� 22.

Silicon Surface Structures Fabricated by Femtosecond Laser Texturing�, Applied Surface Science, Volume 459,

30 November 2018, Pages 86-91. 23.

Ti6Al4V/TiC Composite Interface�, Materials and Design, Volume 155, 5

October 2018, Pages 161-169.� 24.

2018, Vol.

140 / 091015.

� 25.

140(8), 081010 (Jun 04, 2018).�

10.1115/1.4040267 26.

October 2018, Pages 1356�1368. 27.

Mechanics, May 2018, Volume 61, Issue 5, pp 617�636.�� 28.

103, July 2018, Pages 99-108.�

10.1016/j.optlastec.2018.01.022 29.

Letters, Volume 16, April 2018, Pages 18-22.� 30.

140(4), 2018,

041014.� 10.1115/1.4038997 31.

Copper Under High Strain Rate Loading via In-situ Synchrotron Measurement and

Volume 143, 15 January 2018, Pages 43-54.� 32.

Materials Science, Volume 141, January 2018, Pages 10-18.� 33.

Force and Contour Error using Global Task Coordinate Frame�, Journal of Engineering Manufacture,

2018, Vol.

232(1) 40�50. 34.

and Design, Volume 136, 15 December 2017, Pages 185�195.� 35.

Process Parameters on Defect Formation in Friction Stir Welded Samples via

139(11), 111009, 2017. 36.

October 2017, Volume 93, Issue 1�4, pp 1079�1094.

AISI 304 Stainless Steel with An Interface Gap via A Three-Dimensional

Manufacturing Science and Engineering, 2017;139(8):081008-081008-10.

Volume 247, September 2017, Pages

223�233. 39.

Metal and Polymer Surfaces Created using a Femtosecond Laser�, Applied

Surface Science, 405 (2017) 465�475. 40.

Volume 245, July 2017, Pages 46�69.


Volume: 51, Issue: 28, 2017, Pages: 3941-3953.� 42.

Overlapping Fiber Laser Welding of AISI 304 Stainless Steel and AZ31

Journal of Advanced Manufacturing Technology, June 2017,

Volume 90, Issue9,

pp 3685�3696. 43.

of Advanced Manufacturing Technology, April 2017, Volume 90, Issue 1, pp


HTTPS://DOI.ORG/ 10.1007/s00170-016-9415-5 44.

Manufacturing Technology, 89(1), 1089-1102, 2017.


Conductivity of Silicon Carbide Ceramics�, Journal of American Ceramic Society, 99 (12), 4073-4082,

2016.� 46.

Technology, Volume 237, November 2016, Pages 420�429. 47.

Artificial Intelligence, Volume 53, August 2016, Pages 74�85 (2016).� 48.

Structures Created by a Picosecond Laser�, Applied Physics A, (2016),


:10.1007/s00339-016-0004-0 49.

Advanced Manufacturing Technology, October 2016, Volume 86, Issue 9, pp


10.1007/s00170-016-8400-3 50.

Amplification inside a Microhole and an Implied

Letter, Volume 7, Pages 1-5, January 2016.�� 51.

of the American Ceramic Society, 99 [3] 1006�1014 (2016).

HTTPS://DOI.ORG/ 10.1111/jace.14047 52.

the ASME, Journal of Manufacturing Science and Engineering, 138(1),

011004, 2016. 53.

Surface Science, 357 (2015), pp. 54.

Engineering�,� Computer-Aided Design, 2015, Volume 69, December 2015, Pages


10.1016/j.cad.2015.04.001 55.

2 (2015) 647�658.

81, Issue 1 (2015), Page 263-276.

10.1007/s00170-015-7079-1 57.

Conditions�, RSC Advances, 2015, 5,


HTTPS://DOI.ORG/ 10.1039/C5RA09718E� � 58.

Materials Processing, Vol.2, Issue 3, pp.

164-185, 2015.�

10.1007/s40516-015-0013-4 59.

Amorphous Composite by Laser Direct Deposition�, Metallurgical and Materials Transaction, A, September 2015,

Volume 46, Issue 9, pp 4316-4325.�

:10.1007/s11661-015-3047-5 60.

Metallic Glass and Its Effect on Plasticity: Experiment and Modeling�, Scientific Reports 5, Article number:

10789, 2015.� 61.

Physics B, July 2015, Volume 120, Issue 1, pp 81-87.�

10.1007/s00340-015-6102-4 62.

Manufacturing Technology, August 2015, Volume 79, Issue 9-12, pp


10.1007/s00170-015-6942-4 63.

of Artificial Intelligence, Volume 42, June 2015, Pages 1�15.


towards Applications in Fuel Cell Manufacturing�, Science and Technology of Welding and Joining, Vol.

2015, pp.

the ASME, Journal of Manufacturing Science and Engineering, 137, 041003, 2015.� 66.

Volume 98, 15 February 2015, Pages 446-458.�

Https:// 67.

Volume 98, Issue 3, pages 920�928, March 2015.�

Contour Error using Global Task Coordinate Frame�, Trans.

137, 014501 (2015).� 69.

Measurement during Keyhole Fiber Laser Welding�, Optics and Lasers in Engineering, Volume 64, January 2015, Pages


10.1016/j.optlaseng.2014.07.004 70.

Physics Letters, 105, 111907 (2014). 71.

Keyhole Dynamics with A Multi-Physics Numerical Model� Journal of Physics, Applied Physics D, 47 (2014) 345501.� 72.

80, 1 October 2014, Pages 170�178.�

10.1016/j.jclepro.2014.05.084 73.

2(3), 034501, Sept., 2014. 74.

of the ASME, Journal of Micro and Nano Manufacturing, 2(3), 031007,

Sept., 2014. 75.

of the ASME, Journal of Manufacturing Science and Engineering, 136 (4),

041003, 2014.


August 2014, Volume 116, Issue 2, pp 671-681.�

: 10.1007/s00339-014-8330-6 77.

and Coatings Technology, Volume 239, 25 January 2014, Pages 34�40.� 78.

Ergonomics in Manufacturing &

23, Issue 6,


483-516, 2013.�

Manufacturing Science and Engineering, 135(6), 061015, 2013.�

46(33), 2013, 335501. 81.

Physics, 113 (19), 2013, 193506-193506-8. 82.

25, Issue 3, 032002 (2013);

� /10.2351/1.4794032.

213, Issue 6,

June 2013, Pages 877�886.�


2013, pp. 85.

Representation and A Ray Casting Technique�, Journal of Manufacturing Processes, 15, 2013, 338�347.


268, 1 March 2013, Pages 6�10.�

� 87.

Volume 46, Number 5, 6 February 2013, 055501. 88.

213, Issue 2, February 2013, Pages 153�160.

/10.1016/j.jmatprotec.2012.09.010 89.

Volume 559, 1 January 2013,

Pages 836�843.� 90.

Journal of Advanced Manufacturing Technology, Volume 66, Issue 9 (2013), Page 1603-1610.�

10.1007/s00170-012-4443-2 91.

in Artificial Intelligence, Volume 26,

Issue 1, January 2013, Pages 446�455.

10.1016/j.engappai.2012.09.004 92.

66, Issue 9 (2013), Page 1641-1655.

Functionally Graded Inconel 690 Reinforced with TiC�,

207, 25 August 2012, Pages 517�522.� 94.

45, 355204, 2012. 95.

Coupled Analysis of Orthogonal Cutting of AISI 1045 Steel�, Trans.

134, October 2012, 051014.� 96.

via Laser-assisted Machining�, International

Journal of Advance Manufacturing Technology, Volume 64, Issue 1-4, January 2013, pp 475-486.

of Visual Experiments, (65), e4033, HTTPS://DOI.ORG/

10.3791/4033 (2012).

Growth in Laser Conduction Welding of 304 Stainless Steel�, Trans.

Manufacturing Science and Engineering, 134, 041010 (2012).� 99.

Bayesian Framework for Group Feature Selection�, International Journal Machine Learning and Cybernetics, in press.


Femtosecond Laser Pulse Interaction with Metals�, Journal of Physics D: Applied Physics, 45 105201, 2012.� 101.

8, pp.

3113-3123, Aug. 2012.�

Journal of Machine Tools and Manufacture, Volume 57, June 2012, Pages

102�121. 103.

Materials Processing Technology, Volume 212, Issue 5, May 2012, Pages


Volume 212, Issue 3, March 2012, Pages 601�613.� 105.

Journal of Advanced Manufacturing Technology, Volume 59, Issue 5 (2012),

Page 559-567.� 106.

Volume 43, Number 2, 2012,


HTTPS://DOI.ORG/ 10.1007/s11661-011-0890-x 107.

Refinement with Laser-Induced Shock Compression�, Computational Materials Science, Volume 53, Issue 1, February

2012, Pages 79-88.� 108.

Physics Letter, 99, 234104 (2011).

Ionization during Ultrashort Laser Ablation of Metal�, Physics of Plasmas, 18, 093302 (2011).


Journal of Heat and Mass Transfer, Volume 54, Issues 25-26, December

2011, Pages 5319-5326. 111.

Volume 133, Issue 4, August 2011, 041007.�

Science, Volume 50, Issue 10, August-September 2011, Pages 3016-3025.

Volume 42, Issue 6 (2011), Page 1306-1318.



Issue 9, July 2011, Pages 2573-2585 115.

of Multi-Track Direct Laser Deposition Processes�, Journal of Laser Applications, 23, 022003 (2011);

and Shin, Y.C., �Femtosecond Laser Drilling of High Aspect Ratio Microchannels in Glass�, Applied Physics A, Volume 104, Number 2, 713-719, 2011.��

10.1007/s00339-011-6326-z 117.

Cohesive Zone Law for Describing Al-Sic Interface Mechanics�, Composites Part A, Volume 42, Issue 4,

April 2011, Pages 355-363.�


Science and Technology, 71, 2011, pp. 119.

Multi-Input Multi-Output Nonlinear Systems�, Engineering Applications of Artificial Intelligence, Volume 24,

Issue 2, March 2011, Pages 238-250.��

10.1016/j.engappai.2010.10.021 120.

133, Issue 3, 031007, March 2011.� 121.

211, Issue 2, February 2011,

Pages 294-304.


22, No.

1, pp.

129�136, 2011.�


Machining Conditions with Tool Life and Surface Roughness Uncertainties�, International Journal of Production Research, Volume 49, Issue 13, 2011,

Pages 3963 - 3978.

10.1080/00207543.2010.495207 124.

6, Dec. 2011.� 10.1109/TMECH.2010.2071417

Peening of 4140 Steel via Modeling and Experiments�, Trans.

Journal of Manufacturing Science and Engineering, Volume 132,

6, December 2010, 061010. 126.

Simulation�, Physical Review B, 82,

094111, 2010.

Applied Physics, 108, 044908, 2010.�

132, Issue 4, 041005, October 2010. 129.

Science and Engineering, Volume 132, Issue 6, 061004, 2010.


73, Issues 13-15, August 2010, Pages 2624-2631. 131.

20, 075012, 2010. 10.1088/0960-1317/20/7/075012 132.

Science and Engineering, Volume 132, Issue 2,

021008, April 2010. 133.

Engineering, Volume 132, Issue 1, 011009, February 2010.�

32 Issue: 5,


788 � 798, May 2010.

of Ti6Al4V Alloy via LAM and Hybrid Machining�, International Journal of

Machine Tools and Manufacture, Volume 50, Issue 2, pp.


Manufacture, Volume 50, Issue 1, Pages 106-114, January 2010.

for Improved Material Removal Rate�, Applied Physics, A, Volume 98,

Number 2, Pages 407-415, February, 2010.


Signal Processing, 24, 182�192, 2010.

618-619 (2009), pp 159-163.� 140.

Number 11(A), pp.

3933-3948, November 2009.

Abstract 141.

of Heat and Mass Transfer, 52, Issues 23-24, 5867�5877 (2009).

Metal Matrix Composites�, Composites, Part A, Volume 40, Issue 8,

Pages 1231-1239, August


37, 201-2006, 2009,

Difference Filtering for Simultaneous State and Parameter Estimation�, Automatica, 45, 1686-1693, 2009,

720 via Cryogenic Milling�, International Journal of Machining Science and

Technology, Volume 13, Issue 1, pages 1 � 19, January 2009.

HTTPS://DOI.ORG/ 10.1080/10910340902776010 146.

Residual Stresses in Laser Hardening of AISI 4140 Steel by a High Power Diode

Laser�, Surface and Coatings Technology, Volume 203, Issue 14, 15,

pages 2003-2012, April 2009.

Surface Science, Volume 255, Issue 9, 4996-5002, February 2009.�

of Advanced Manufacturing Technology, 40:648�661, 2009.�


5, pp.


Manufacturing Science and Engineering 130, 051016 (2008). 151.

130, 031014, June, 2008.� 152.

and Inconel 718�, Trans.

130, 031013, June, 2008. 153.

130, 031001-1-10, June,

2008. 10.1115/1.2823070


of 4140 Steel and Optimization of Overlapping Patterns� Materials Science

and Engineering, A, 480, pp.

209�217, 2008.� 155.

Journal of Machine Tools and Manufacture, 48, 61�72, 2008.� 10.1016/j.ijmachtools.2007.07.010


Vacuum�, Physics Letters A, 371, pp.

128�134, 2007.

Experimental Verification� Physical Review E, 76, 026405, 2007.

a FBFN model�, Fuzzy Sets and Systems, Vol.

158, 2013-2025,


Processes using a Multi-level Fuzzy Controller�, Trans.

4, pp.

480-492, 2007.


of Applied Physics, 101, 103514, 2007. 161.

3, pp.

539-550, 2007.


221(4), pp.

605-616, 2007. 163.

Optimization of the OD Plunge Grinding Process via Generalized Intelligent

35, pp.

489-496, 2007.


2, pp.

287-295, 2007.


Journal of Machine Tools and Manufacture, vol.

10, pp.


2, pp.


1, pp.

117-125, 2007.


8, pp.


10.1080/00207540600562025 169.

253, pp.

4079�4084, 2007.

of Applied Physics, 101, No.

2, 023510, 2007. 171.

47, No.

2, pp.

307-320, February 2007. 172.

Volume 47, Issue 1, Pages 14-22,

January 2007.


12, pp.

2006. 10.1243/09544054JEM562 174.

Volume 201, Issue 6, pp.

2256-2269, December 2006. 175.

Complex Geometric Features via In-process Control�, Journal of the American

89, issue 11, pp.

3397-3405, 2006.� 10.1111/j.1551-2916.2006.01265.x 176.

Ablation�, Applied Physics Letters, 89, 111902, 2006.� 177.

2, 2006.

Machining of Inconel 718 with an Economic Analysis�, International Journal

of Machine Tools and Manufacture, 46, pp.

1879�1891, Nov. 2006.� 179.

128, pp.

2006.�� 180.

Formation�, Journal of Applied Physics, 99, 084310, 2006.� 181.


393-403, May 2006.� 182.

machining of Compacted Graphite Iron with Microstructural Analysis�, International

46, 7-17, 2006.� 183.

Plasma in Laser Shock Peening�, Applied Physics Letters, 88, 041116, 2006. 184.

128, pp.

404-415, May 2006.� 185.


86-95, Feb. 2006.� 186.

97, Issue 11, pp.

113517-1-11, 2005.

6, pp.

761-778, Dec. 2005.


Mass Transfer, Volume 48, Issue 10, pp.

1999-2012, May 2005.



880-890, Dec. 2004.

32, pp.


44, pp.

347-364, 2004.


Spindles: Part 2- Solution Procedure and Validations", Trans.


159-168, Feb. 2004.


Spindles: Part 1- Model Development", Trans.

1, pp.



44, pp.

677-694, 2004.


1, pp.

42-51, Feb. 2004.


and Petrescu, G., �Hybrid Machining of Inconel 718,

43, Issue 13, pp.

1287-1396, October 2003.

137, pp.

297-323, 2003.

1, pp.


Surface Grinding Process for Heat-Treated 4140 Steel Alloys with Aluminum

125, pp.

65-76, February 2003.

125, pp.

21-28, February 2003.

and Shin, Y.C., �Wear of diamond dresser in laser assisted truing and

dressing of vitrified CBN wheels�, International Journal of Machine Tools

43, Issue 1, Pages 41-49, 2003.

408-412, pp.

1669-1674, Trans Tech Publications, Switzerland, 2002.


2, pp.

61-71, April 2002.


2, pp.

187-213, 2002.


875-885, Nov. 2002.


42, Issue 9, pp.

1035-1044, 2002.


42, Issue 7, pp.

825-835, 2002.


30, pp.

377-384, May 2002.


30, pp.

153-160, May 2002.


123, pp.



41/12, pp.

1763-1781, June 2001.


29, pp.

311-318, May 2001.

enhanced machining of Inconel 718: modeling of workpiece temperature with

plasma heating and experimental results", International Journal of

Machine Tools and Manufacture, 41/6, pp.

877-891, March 2001.

Y.C., Lee, C.W., Choi, T.J., Hu, S., "Intelligent Control via Open


26-35, 2000.


Potential and Future", Machining Technology, featured article,

3, pp.

1-7, Third quarter, 2000.



Magazine, April/May, pp.

16-30, 2000.


15, pp.

2213-2233, 2000.


12, pp.

2787-2813, 2000.


122, pp.

666-670, Nov. 2000.


122, pp.

95-101, March 2000.


the Laser Assisted Machining of a Silicon Nitride Ceramic: Part II -

Transfer, 43, pp.

1425-1437, 2000.


of Heat and Mass Transfer, 43, pp.

1409-1424, 2000.


Part C, pp.

1-13, 2000.


36/3, pp.

399-407, 2000.


4, pp.

November, 1999.

Operation: Part 2 - Experimental Validation and Influencing Factors�, Trans.

4, pp.

606-614, November, 1999.

Operation: Part 1 - Theory for Stability Lobe Prediction�, Trans.


600-605, November, 1999.

39, Issue 10, pp.

1631-1654, 1999.

3, pp.

363-372, July 1999.

27, pp.

111-116, May 1999.

Method", Journal of Intelligent and Fuzzy Systems, Volume 7,

Issue 1, pp.

13-25, March 1999.

39, Issue 5, pp.

731-750, February 1999.

Optimization of Grinding Processes using Fuzzy Logic", Proceedings of

212, pp.

647-660, 1998.

4, pp.

November 1998.

4, pp.

November 1998.

9, No.3, pp.

225-234, June 1998.

2, pp.

387-394, May 1998.

1, pp.

197-200, February 1998.

10, pp.

291-313, 1997.

4, pp.

502-508, November 1997.

4, pp.

875-882, October 1997.

9, pp.

August 1997.

119, No.2, pp.

146-152, June 1997.


207-212, May 1997.

Inconel 718", Trans.

1, pp.

125-129, Feb. 1997.

1, pp.

59-72, 1996.

12, pp.

3291-3309, 1996.

3, pp.

411-422, March, 1996.

3, pp.

August, 1996.

Cutting Force for Endmilling Processes using a

3, pp.

339-347, August, 1996.

Monitoring using Radial Basis Function Neural Network", Trans.

117, pp.

459-467, December, 1995.

117, pp.

439-447, August 1995.

Institution, pp.

287-292, May, 1995.

4, pp.

401-420, 1995.

Roughness Evaluation via Ultrasonic Scanning", IEEE Trans.

41, No.

6, pp.

1-9, Nov. 1994.

22, pp.

157-163, May 1994.

Modeling and Dynamic Analysis of High Speed Spindles - Part I", Trans.

116, pp.

October, 1994.


594-603, July 1994.

115, No.1, pp.

160-163, 1993.

12, pp.

2907-2919, 1992.

1992, pp.

1992, pp.

114, No.1, 1992, pp.

205, Issue C3, 1991, pp.

5, 1991, pp.


19, 1991, pp.

4, 1991, pp.

2, June 1991, pp.

18, May 1990, pp.

June 1990, Vol.

112, pp.

Top Refereed

Conference Proceedings (Recent several years only) �

Friction Stir Welded Samples via Predictive Numerical Modeling and

Science and Engineering, MSEC2017-3092, June 4-9, 2017, Los

MSEC2017-2974, June 4-9, 2017, Los Angeles, California.

Initial Temperatures inside a Microhole by a Short

2016, pp.

and Shin, Y.C., �A parametric study on laser welding of magnesium

alloy AZ31 by a fiber laser�, ASME

June 9-13, 2014, Detroit, MI.

MSEC2014-4042, June 9-13, 2014, Detroit, MI.

and Engineering, MSEC2014-3956, June 9-13, 2014, Detroit, MI.

and Engineering, MSEC2013-1126, June 10-14, 2013, Madison, Wisconsin.

and Engineering, MSEC2013-1108, June 10-14, 2013, Madison, Wisconsin.

June 10-14, 2013, Madison, Wisconsin.

International Congress on Applications of Lasers &

24-27, 2012, Anaheim, CA.

1045 Steel�, MSEC2012-7300, ASME 2012 International

Manufacturing Science and Engineering Conference, South Bend, June 4-8,


Technique�, NAMRC40-7780, 40th North

American Manufacturing Research Conference, South Bend, June 4-8, 2012.

Impact Analysis� Paper (1406), Proc.

(ICALEO), Oct. 23-27, 2011, Orlando, FL.

Paper (1707), Proc.

Congress on Applications of Lasers &

23-27, 2011, Orlando, FL.

Proceedings of the 11st International

Conference on Shot Peening, September 12-15, 2011, in South Bend,

Remanufacturing Gas Turbine Blades�, Proceedings of the ASME 2011 International Design Engineering Technical Conferences


2011, August 29 - 31, 2011, Washington, DC, USA.

Welding of 304 Stainless Steel�, ASME

June 13-17, 2011, Corvallis, Oregon.

density-based grain refinement modeling of orthogonal cutting of commercially

pure titanium�, ASME International

2011, Corvallis, Oregon.

�Experimental and numerical modeling analysis of micro-milling of hardened

H13 tool steel�, ASME International

2011, Corvallis, Oregon.

Jan. 3-7, 2011, Atlanta, GA.

Conference, Jan. 3-7, 2011, Atlanta, GA.

International Congress on Applications of Lasers &

(ICALEO), September 26-30, 2010, Los Angeles, CA.

International Congress on Applications of Lasers &

(ICALEO), September 26-30, 2010, Los Angeles, CA.

Electro-Optics (ICALEO), September 26-30, 2010, Los Angeles, CA.

of the 5th International Conference on MicroManufacturing, April 5-8, 2010, Madison,

of the 5th International Conference on MicroManufacturing, April 5-8, 2010, Madison,

Top Books

and Book Chapters �

of Metal Matrix Composites, edited by J.

Chapter 1, Mechanics and Modeling of

Chip Formation in Machining of MMC, Springer, 2011, ISBN


Innovations, edited by W.

1 edition (July 6, 2010), ISBN-10: 1420071017.

Press, Taylor and Francis, 456 pages, Dec. 2008.

(editor: Cornelius T.

Flexible Manufacturing Systems", pp.

345-389, CRC Press Inc., 1998.

Shin, Y.C., The Engineering Handbook, Chapter 166, "Machine Tools and


Manufacturing and Automation, chapter 14 "Machine Tools", pp.

243-258, John Wiley &

Shin, Y.C., Artificial Neural Networks for Intelligent Manufacturing, chapter

14 "Adaptive Control in Manufacturing", pp.

Hall, ed.

57, editor, co-editor with

Press, co-editor with C.H.


Press, co-editor with C.H.

1991 Back to Top


Artificial intelligence

In computer science, artificial intelligence (AI), sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans.

Leading AI textbooks define the field as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1]

Colloquially, the term 'artificial intelligence' is often used to describe machines (or computers) that mimic 'cognitive' functions that humans associate with the human mind, such as 'learning' and 'problem solving'.[2]

The traditional problems (or goals) of AI research include reasoning, knowledge representation, planning, learning, natural language processing, perception and the ability to move and manipulate objects.[14]

Many tools are used in AI, including versions of search and mathematical optimization, artificial neural networks, and methods based on statistics, probability and economics.

In the twenty-first century, AI techniques have experienced a resurgence following concurrent advances in computer power, large amounts of data, and theoretical understanding;

and AI techniques have become an essential part of the technology industry, helping to solve many challenging problems in computer science, software engineering and operations research.[24][12]

The study of mathematical logic led directly to Alan Turing's theory of computation, which suggested that a machine, by shuffling symbols as simple as '0' and '1', could simulate any conceivable act of mathematical deduction.

The development of metal–oxide–semiconductor (MOS) very-large-scale integration (VLSI), in the form of complementary MOS (CMOS) transistor technology, enabled the development of practical artificial neural network (ANN) technology in the 1980s.

The success was due to increasing computational power (see Moore's law and transistor count), greater emphasis on solving specific problems, new ties between AI and other fields (such as statistics, economics and mathematics), and a commitment by researchers to mathematical methods and scientific standards.[42]

According to Bloomberg's Jack Clark, 2015 was a landmark year for artificial intelligence, with the number of software projects that use AI Google increased from a 'sporadic usage' in 2012 to more than 2,700 projects.

He attributes this to an increase in affordable neural networks, due to a rise in cloud computing infrastructure and to an increase in research tools and datasets.[12]

Computer science defines AI research as the study of 'intelligent agents': any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[1]

A more elaborate definition characterizes AI as “a system’s ability to correctly interpret external data, to learn from such data, and to use those learnings to achieve specific goals and tasks through flexible adaptation.”[60]

An AI's intended utility function (or goal) can be simple ('1 if the AI wins a game of Go, 0 otherwise') or complex ('Do mathematically similar actions to the ones succeeded in the past').

Alternatively, an evolutionary system can induce goals by using a 'fitness function' to mutate and preferentially replicate high-scoring AI systems, similar to how animals evolved to innately desire certain goals such as finding food.[61]

Some AI systems, such as nearest-neighbor, instead of reason by analogy, these systems are not generally given goals, except to the degree that goals are implicit in their training data.[62]

Some of the 'learners' described below, including Bayesian networks, decision trees, and nearest-neighbor, could theoretically, (given infinite data, time, and memory) learn to approximate any function, including which combination of mathematical functions would best describe the world[citation needed].

In practice, it is almost never possible to consider every possibility, because of the phenomenon of 'combinatorial explosion', where the amount of time needed to solve a problem grows exponentially.

The third major approach, extremely popular in routine business AI applications, are analogizers such as SVM and nearest-neighbor: 'After examining the records of known past patients whose temperature, symptoms, age, and other factors mostly match the current patient, X% of those patients turned out to have influenza'.

A fourth approach is harder to intuitively understand, but is inspired by how the brain's machinery works: the artificial neural network approach uses artificial 'neurons' that can learn by comparing itself to the desired output and altering the strengths of the connections between its internal neurons to 'reinforce' connections that seemed to be useful.

Therefore, according to Occam's razor principle, a learner must be designed such that it prefers simpler theories to complex theories, except in cases where the complex theory is proven substantially better.

Many systems attempt to reduce overfitting by rewarding a theory in accordance with how well it fits the data, but penalizing the theory in accordance with how complex the theory is.[70]

A toy example is that an image classifier trained only on pictures of brown horses and black cats might conclude that all brown patches are likely to be horses.[71]

instead, they learn abstract patterns of pixels that humans are oblivious to, but that linearly correlate with images of certain types of real objects.

Humans also have a powerful mechanism of 'folk psychology' that helps them to interpret natural-language sentences such as 'The city councilmen refused the demonstrators a permit because they advocated violence'.

For example, existing self-driving cars cannot reason about the location nor the intentions of pedestrians in the exact way that humans do, and instead must use non-human modes of reasoning to avoid accidents.[80][81][82]

By the late 1980s and 1990s, AI research had developed methods for dealing with uncertain or incomplete information, employing concepts from probability and economics.[85]

These algorithms proved to be insufficient for solving large reasoning problems, because they experienced a 'combinatorial explosion': they became exponentially slower as the problems grew larger.[65]

In addition, some projects attempt to gather the 'commonsense knowledge' known to the average person into a database containing extensive knowledge about the world.

by acting as mediators between domain ontologies that cover specific knowledge about a particular knowledge domain (field of interest or area of concern).

They need a way to visualize the future—a representation of the state of the world and be able to make predictions about how their actions will change it—and be able to make choices that maximize the utility (or 'value') of available choices.[107]

A sufficiently powerful natural language processing system would enable natural-language user interfaces and the acquisition of knowledge directly from human-written sources, such as newswire texts.

Modern statistical NLP approaches can combine all these strategies as well as others, and often achieve acceptable accuracy at the page or paragraph level, but continue to lack the semantic understanding required to classify isolated sentences well.

Besides the usual difficulties with encoding semantic commonsense knowledge, existing semantic NLP sometimes scales too poorly to be viable in business applications.

is the ability to use input from sensors (such as cameras (visible spectrum or infrared), microphones, wireless signals, and active lidar, sonar, radar, and tactile sensors) to deduce aspects of the world.

a giant, fifty-meter-tall pedestrian far away may produce exactly the same pixels as a nearby normal-sized pedestrian, requiring the AI to judge the relative likelihood and reasonableness of different interpretations, for example by using its 'object model' to assess that fifty-meter pedestrians do not exist.[123]

Advanced robotic arms and other industrial robots, widely used in modern factories, can learn from experience how to move efficiently despite the presence of friction and gear slippage.[125]

the paradox is named after Hans Moravec, who stated in 1988 that 'it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility'.[129][130]

Moderate successes related to affective computing include textual sentiment analysis and, more recently, multimodal affect analysis (see multimodal sentiment analysis), wherein AI classifies the affects displayed by a videotaped subject.[139]

Some computer systems mimic human emotion and expressions to appear more sensitive to the emotional dynamics of human interaction, or to otherwise facilitate human–computer interaction.[140]

These early projects failed to escape the limitations of non-quantitative symbolic logic models and, in retrospect, greatly underestimated the difficulty of cross-domain AI.

Many researchers predict that such 'narrow AI' work in different individual domains will eventually be incorporated into a machine with artificial general intelligence (AGI), combining most of the narrow skills mentioned in this article and at some point even exceeding human ability in most or all these areas.[18][143]

One high-profile example is that DeepMind in the 2010s developed a 'generalized artificial intelligence' that could learn many diverse Atari games on its own, and later developed a variant of the system which succeeds at sequential learning.[144][145][146]

hypothetical AGI breakthroughs could include the development of reflective architectures that can engage in decision-theoretic metareasoning, and figuring out how to 'slurp up' a comprehensive knowledge base from the entire unstructured Web.[6]

Finally, a few 'emergent' approaches look to simulating human intelligence extremely closely, and believe that anthropomorphic features like an artificial brain or simulated child development may someday reach a critical point where general intelligence emerges.[149][150]

For example, even specific straightforward tasks, like machine translation, require that a machine read and write in both languages (NLP), follow the author's argument (reason), know what is being talked about (knowledge), and faithfully reproduce the author's original intent (social intelligence).

A problem like machine translation is considered 'AI-complete', because all of these problems need to be solved simultaneously in order to reach human-level machine performance.

When access to digital computers became possible in the mid 1950s, AI research began to explore the possibility that human intelligence could be reduced to symbol manipulation.

in the 1960s and the 1970s were convinced that symbolic approaches would eventually succeed in creating a machine with artificial general intelligence and considered this the goal of their field.

Economist Herbert Simon and Allen Newell studied human problem-solving skills and attempted to formalize them, and their work laid the foundations of the field of artificial intelligence, as well as cognitive science, operations research and management science.

Their research team used the results of psychological experiments to develop programs that simulated the techniques that people used to solve problems.

Unlike Simon and Newell, John McCarthy felt that machines did not need to simulate human thought, but should instead try to find the essence of abstract reasoning and problem-solving, regardless whether people used the same algorithms.[15]

His laboratory at Stanford (SAIL) focused on using formal logic to solve a wide variety of problems, including knowledge representation, planning and learning.[157]

found that solving difficult problems in vision and natural language processing required ad-hoc solutions—they argued that there was no simple and general principle (like logic) that would capture all the aspects of intelligent behavior.

When computers with large memories became available around 1970, researchers from all three traditions began to build knowledge into AI applications.[161]

By the 1980s, progress in symbolic AI seemed to stall and many believed that symbolic systems would never be able to imitate all the processes of human cognition, especially perception, robotics, learning and pattern recognition.

This coincided with the development of the embodied mind thesis in the related field of cognitive science: the idea that aspects of the body (such as movement, perception and visualization) are required for higher intelligence.

Within developmental robotics, developmental learning approaches are elaborated upon to allow robots to accumulate repertoires of novel skills through autonomous self-exploration, social interaction with human teachers, and the use of guidance mechanisms (active learning, maturation, motor synergies, etc.).[164][165][166][167]

Artificial neural networks are an example of soft computing—they are solutions to problems which cannot be solved with complete logical certainty, and where an approximate solution is often sufficient.

Much of traditional GOFAI got bogged down on ad hoc patches to symbolic computation that worked on their own toy models but failed to generalize to real-world results.

However, around the 1990s, AI researchers adopted sophisticated mathematical tools, such as hidden Markov models (HMM), information theory, and normative Bayesian decision theory to compare or to unify competing architectures.

Compared with GOFAI, new 'statistical learning' techniques such as HMM and neural networks were gaining higher levels of accuracy in many practical domains such as data mining, without necessarily acquiring a semantic understanding of the datasets.

The increased successes with real-world data led to increasing emphasis on comparing different approaches against shared test data to see which approach performed best in a broader context than that provided by idiosyncratic toy models;

In AGI research, some scholars caution against over-reliance on statistical learning, and argue that continuing research into GOFAI will still be necessary to attain general intelligence.[172][173]

Planning algorithms search through trees of goals and subgoals, attempting to find a path to a target goal, a process called means-ends analysis.[182]

In some search methodologies heuristics can also serve to entirely eliminate some choices that are unlikely to lead to a goal (called 'pruning the search tree').

These algorithms can be visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by jumps or steps, we keep moving our guess uphill, until we reach the top.

For example, they may begin with a population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the fittest to survive each generation (refining the guesses).

Two popular swarm algorithms used in search are particle swarm optimization (inspired by bird flocking) and ant colony optimization (inspired by ant trails).[187][188]

Fuzzy set theory assigns a 'degree of truth' (between 0 and 1) to vague statements such as 'Alice is old' (or rich, or tall, or hungry) that are too linguistically imprecise to be completely true or false.

Fuzzy logic is successfully used in control systems to allow experts to contribute vague rules such as 'if you are close to the destination station and moving fast, increase the train's brake pressure';

Probabilistic algorithms can also be used for filtering, prediction, smoothing and finding explanations for streams of data, helping perception systems to analyze processes that occur over time (e.g., hidden Markov models or Kalman filters).[204]

Complicated graphs with diamonds or other 'loops' (undirected cycles) can require a sophisticated method such as Markov chain Monte Carlo, which spreads an ensemble of random walkers throughout the Bayesian network and attempts to converge to an assessment of the conditional probabilities.

Classifier performance depends greatly on the characteristics of the data to be classified, such as the dataset size, distribution of samples across classes, the dimensionality, and the level of noise.

Otherwise, if no matching model is available, and if accuracy (rather than speed or scalability) is the sole concern, conventional wisdom is that discriminative classifiers (especially SVM) tend to be more accurate than model-based classifiers such as 'naive Bayes' on most practical data sets.[220][221]

A simple 'neuron' N accepts input from other neurons, each of which, when activated (or 'fired'), cast a weighted 'vote' for or against whether neuron N should itself activate.

one simple algorithm (dubbed 'fire together, wire together') is to increase the weight between two connected neurons when the activation of one triggers the successful activation of another.

In the 2010s, advances in neural networks using deep learning thrust AI into widespread public consciousness and contributed to an enormous upshift in corporate AI spending;

The main categories of networks are acyclic or feedforward neural networks (where the signal passes in only one direction) and recurrent neural networks (which allow feedback and short-term memories of previous input events).

Neural networks can be applied to the problem of intelligent control (for robotics) or learning, using such techniques as Hebbian learning ('fire together, wire together'), GMDH or competitive learning.[226]

However, some research groups, such as Uber, argue that simple neuroevolution to mutate new neural network topologies and weights may be competitive with sophisticated gradient descent approaches[citation needed].

For example, a feedforward network with six hidden layers can learn a seven-link causal chain (six hidden layers + output layer) and has a 'credit assignment path' (CAP) depth of seven[citation needed].

Deep learning has transformed many important subfields of artificial intelligence[why?], including computer vision, speech recognition, natural language processing and others.[235][236][234]

In 2006, a publication by Geoffrey Hinton and Ruslan Salakhutdinov introduced another way of pre-training many-layered feedforward neural networks (FNNs) one layer at a time, treating each layer in turn as an unsupervised restricted Boltzmann machine, then using supervised backpropagation for fine-tuning.[242]

Over the last few years, advances in both machine learning algorithms and computer hardware have led to more efficient methods for training deep neural networks that contain many layers of non-linear hidden units and a very large output layer.[243]

In 1992, it was shown that unsupervised pre-training of a stack of recurrent neural networks can speed up subsequent supervised learning of deep sequential problems.[253]

The most common areas of competition include general machine intelligence, conversational behavior, data-mining, robotic cars, and robot soccer as well as conventional games.[272]

The 'imitation game' (an interpretation of the 1950 Turing test that assesses whether a computer can imitate a human) is nowadays considered too exploitable to be a meaningful benchmark.[273]

High-profile examples of AI include autonomous vehicles (such as drones and self-driving cars), medical diagnosis, creating art (such as poetry), proving mathematical theorems, playing games (such as Chess or Go), search engines (such as Google search), online assistants (such as Siri), image recognition in photographs, spam filtering, predicting flight delays,[279]

With social media sites overtaking TV as a source for news for young people and news organizations increasingly reliant on social media platforms for generating distribution,[284]

In 2016, a ground breaking study in California found that a mathematical formula developed with the help of AI correctly determined the accurate dose of immunosuppressant drugs to give to organ patients.[287]

Another study is using artificial intelligence to try to monitor multiple high-risk patients, and this is done by asking each patient numerous questions based on data acquired from live doctor to patient interactions.[290]

One study was done with transfer learning, the machine performed a diagnosis similarly to a well-trained ophthalmologist, and could generate a decision within 30 seconds on whether or not the patient should be referred for treatment, with more than 95% accuracy.[291]

The team supervised the robot while it performed soft-tissue surgery, stitching together a pig's bowel during open surgery, and doing so better than a human surgeon, the team claimed.[292]

However, Google has been working on an algorithm with the purpose of eliminating the need for pre-programmed maps and instead, creating a device that would be able to adjust to a variety of new surroundings.[298]

Some self-driving cars are not equipped with steering wheels or brake pedals, so there has also been research focused on creating an algorithm that is capable of maintaining a safe environment for the passengers in the vehicle through awareness of speed and driving conditions.[299]

Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.

For example, AI based buying and selling platforms have changed the law of supply and demand in that it is now possible to easily estimate individualized demand and supply curves and thus individualized pricing.

Other theories where AI has had impact include in rational choice, rational expectations, game theory, Lewis turning point, portfolio optimization and counterfactual thinking[citation needed]..

The cybersecurity arena faces significant challenges in the form of larges scale hacking attacks of different types which harm organizations of all kinds and create billions of dollars in business damage.

This system will involve use of cameras to ascertain traffic density and accordingly calculate the time needed to clear the traffic volume which will determine the signal duration for vehicular traffic across streets.[314]

Intelligence technologies enables coordination of sensors and effectors, threat detection and identification, marking of enemy positions, target acquisition, coordination and deconfliction of distributed Join Fires between networked combat vehicles and tanks also inside Manned and Unmanned Teams (MUM-T).[318]

It is possible to use AI to predict or generalize the behavior of customers from their digital footprints in order to target them with personalized promotions or build customer personas automatically.[327]

Moreover, the application of Personality computing AI models can help reducing the cost of advertising campaigns by adding psychological targeting to more traditional sociodemographic or behavioral targeting.[329]

Irakli Beridze, Head of the Centre for Artificial Intelligence and Robotics at UNICRI, United Nations, has expressed that 'I think the dangerous applications for AI, from my point of view, would be criminals or large terrorist organizations using it to disrupt large processes or simply do pure harm.

He argues that sufficiently intelligent AI, if it chooses actions based on achieving some goal, will exhibit convergent behavior such as acquiring resources or protecting itself from being shut down.

If this AI's goals do not reflect humanity's—one example is an AI told to compute as many digits of pi as possible—it might harm humanity in order to acquire more resources or prevent itself from being shut down, ultimately to better achieve its goal.

For this danger to be realized, the hypothetical AI would have to overpower or out-think all of humanity, which a minority of experts argue is a possibility far enough in the future to not be worth researching.[362][363]

Algorithms have a host of applications in today's legal system already, assisting officials ranging from judges to parole officers and public defenders in gauging the predicted likelihood of recidivism of defendants.[368]

It has been suggested that COMPAS assigns an exceptionally elevated risk of recidivism to black defendants while, conversely, ascribing low risk estimate to white defendants significantly more often than statistically expected.[368]

Jobs at extreme risk range from paralegals to fast food cooks, while job demand is likely to increase for care-related professions ranging from personal healthcare to the clergy.[374]

Research in this area includes machine ethics, artificial moral agents, friendly AI and discussion towards building a human rights framework is also in talks.[376]

The field of machine ethics is concerned with giving machines ethical principles, or a procedure for discovering a way to resolve the ethical dilemmas they might encounter, enabling them to function in an ethically responsible manner through their own ethical decision making.[381]

The field was delineated in the AAAI Fall 2005 Symposium on Machine Ethics: 'Past research concerning the relationship between technology and ethics has largely focused on responsible and irresponsible use of technology by human beings, with a few people being interested in how human beings ought to treat machines.

In contrast to computer hacking, software property issues, privacy issues and other topics normally ascribed to computer ethics, machine ethics is concerned with the behavior of machines towards human users and other machines.

Research in machine ethics is key to alleviating concerns with autonomous systems—it could be argued that the notion of autonomous machines without such a dimension is at the root of all fear concerning machine intelligence.

Humans should not assume machines or robots would treat us favorably because there is no a priori reason to believe that they would be sympathetic to our system of morality, which has evolved along with our particular biology (which AIs would not share).

I think the worry stems from a fundamental error in not distinguishing the difference between the very real recent advances in a particular aspect of AI, and the enormity and complexity of building sentient volitional intelligence.'[384]

The philosophical position that John Searle has named 'strong AI' states: 'The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds.'[387]

Technological singularity is when accelerating progress in technologies will cause a runaway effect wherein artificial intelligence will exceed human intellectual capacity and control, thus radically changing or even ending civilization.

Ray Kurzweil has used Moore's law (which describes the relentless exponential improvement in digital technology) to calculate that desktop computers will have the same processing power as human brains by the year 2029, and predicts that the singularity will occur in 2045.[393]

A survey of economists showed disagreement about whether the increasing use of robots and AI will cause a substantial increase in long-term unemployment, but they generally agree that it could be a net benefit, if productivity gains are redistributed.[396]

In the 1980s, artist Hajime Sorayama's Sexy Robots series were painted and published in Japan depicting the actual organic human form with lifelike muscular metallic skins and later 'the Gynoids' book followed that was used by or influenced movie makers including George Lucas and other creatives.

Sorayama never considered these organic robots to be real part of nature but always unnatural product of the human mind, a fantasy existing in the mind even when realized in actual form.

Moving to a New Level of Intelligent Manufacturing

Semiconductor manufacturing over the past few decades has moved through several levels of technology, with each transition leading to lower costs and higher fab productivity.

Within the walls of the factory, a new level of intelligence is being enabled by technologies such as 5G wireless, artificial intelligence (AI), more powerful processors, cloud-based data analysis, virtual and augmented reality, and integrated knowledge networks.

Together with new software applications, these technologies are driving the semiconductor manufacturing industry toward what is variously referred to as Industry 4.0, smart manufacturing, or the industrial internet of things (IIoT).

The evolving technologies pushing us toward intelligent manufacturing are truly astonishing: 5G networks so fast we can download a movie in four or five seconds;

and the potential for strategies such as cloud-based computing with maturing security capabilities poised to foster cost-effective collaborative problem solving and data analysis.

Gradually, fabs moved to a new level, with the ability to aggregate data from the equipment, automate material handling systems, and implement advanced process control (APC) capabilities such as run-to-run control and fault detection.

Many companies continued to use existing technologies that lacked enough compute power, bandwidth, data aggregation, and filtering to move to these higher levels of automation.

The tough questions for chipmakers are how to (1) improve productivity, (2) create a roadmap so production engineers can go off and create something useful, (3) develop methods for making people more productive, and (4) help production engineers work as a team (as opposed to standalone entities).

After all, autonomous vehicles involve software and sensors providing data from surrounding cars with real-time responses, as well as safety goals and objectives to reduce the cost of getting from point A to point B (figure 1).

(Source: The Society of Automotive Engineers) The SAE’s definition starts with no automation at Level 0 and then moves to Level 1, where the automobile itself makes some real-time decisions, such as cruise control regulating the acceleration and deceleration of the car, with the driver as the backup plan.

Level 3, where many manufacturers are moving today, attempts to achieve the first phases of full automation, including figuring out how to bring in real-time scheduling and predictive techniques.

As these companies look down the road to their end goal, realizing that the processes and systems of yesterday aren’t necessarily going to work tomorrow, they will face a steeper learning curve than ever before in building user trust in the technology.

Just as autonomous vehicles must make decisions in real time for safety reasons, Applied Materials is working with early adopters to enable real-time decisions that optimize business practices.

Meanwhile, without compromising security, Applied Materials is working with customers to develop useful techniques, such as dynamic scheduling, full automation scenarios, next-generation run-to-run control, and quality scenarios.

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