INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
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UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: editor@ijitce.co.uk Phone: +44-773-043-0249 USA: Editor International Journal of Innovative Technology and Creative Engineering Dr. Arumugam Department of Chemistry University of Georgia GA-30602, USA. Phone: 001-706-206-0812 Fax:001-706-542-2626 India: Editor International Journal of Innovative Technology & Creative Engineering Dr. Arthanariee. A. M Finance Tracking Center India 17/14 Ganapathy Nagar 2nd Street Ekkattuthangal Chennai -600032 Mobile: 91-7598208700
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY & CREATIVE ENGINEERING Vol.3 No.3 March 2013
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about Solar radiation, Taguchi technique for gas metal arc welding. Let us review research around the world this month; Automatic manufacturing has dropped the cost of LEDs dramatically throughout the decades. Depending on the color and brightness, LED lights are extraordinarily affordable and easy to replace. As televisions and computer displays rely increasingly on LED illumination, their construction becomes ever more thinner, robust, and efficient. With every year, the cost to produce semiconductors drops, allowing LEDs to be more accessible as a cheap light source. A forecast known as Haitz’s Law predicts that LEDs will become increasingly more powerful each decade. As people seek for eco-friendly alternatives, more attention is garnered by LEDs due to their small electrical demands and big punch. Professor Garrett Stanley detailed research progress toward “reading and writing the neural code.” This encompasses the ability to observe the spiking activity of neurons in response to outside stimuli and make clear predictions about what is being seen, heard, or felt, and the ability to artificially introduce activity within the brain that enables someone to see, hear, or feel something that is not experienced naturally through sensory organs. A student, Param Jaggi, invents a device to reduce auto emissions: Param Jaggi, an 18-year-old Vanderbilt sophomore, has made the Forbes 30 Under 30 list for two straight years due to his inventions, which include a device that cleans air coming out of a car exhaus. while watching the exhaust being expelled from an idling car, an idea was planted. He did some research and found that very little was being done to curtail carbon emissions at the source — a vehicle’s tailpipe. So he went to work, and at 15, for a high school science project, he built a device that fits onto the back of a car’s tailpipe and uses algae to transform carbon dioxide into oxygen. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technology related research articles from you and your friends. We are anxiously awaiting the rich and thorough research papers that have been prepared by our authors for the next issue.
Thanks, Editorial Team IJITCE
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Editorial Members Dr. Chee Kyun Ng Ph.D Department of Computer and Communication Systems, Faculty of Engineering, Universiti Putra Malaysia,UPM Serdang, 43400 Selangor,Malaysia. Dr. Simon SEE Ph.D Chief Technologist and Technical Director at Oracle Corporation, Associate Professor (Adjunct) at Nanyang Technological University Professor (Adjunct) at Shangai Jiaotong University, 27 West Coast Rise #08-12,Singapore 127470 Dr. sc.agr. Horst Juergen SCHWARTZ Ph.D, Humboldt-University of Berlin, Faculty of Agriculture and Horticulture, Asternplatz 2a, D-12203 Berlin, Germany Dr. Marco L. Bianchini Ph.D Italian National Research Council; IBAF-CNR, Via Salaria km 29.300, 00015 Monterotondo Scalo (RM), Italy Dr. Nijad Kabbara Ph.D Marine Research Centre / Remote Sensing Centre/ National Council for Scientific Research, P. O. Box: 189 Jounieh, Lebanon Dr. Aaron Solomon Ph.D Department of Computer Science, National Chi Nan University, No. 303, University Road, Puli Town, Nantou County 54561, Taiwan Dr. Arthanariee. A. M M.Sc.,M.Phil.,M.S.,Ph.D Director - Bharathidasan School of Computer Applications, Ellispettai, Erode, Tamil Nadu,India Dr. Takaharu KAMEOKA, Ph.D Professor, Laboratory of Food, Environmental & Cultural Informatics Division of Sustainable Resource Sciences, Graduate School of Bioresources, Mie University, 1577 Kurimamachiya-cho, Tsu, Mie, 514-8507, Japan Mr. M. Sivakumar M.C.A.,ITIL.,PRINCE2.,ISTQB.,OCP.,ICP Project Manager - Software, Applied Materials, 1a park lane, cranford, UK Dr. Bulent Acma Ph.D Anadolu University, Department of Economics, Unit of Southeastern Anatolia Project(GAP), 26470 Eskisehir, TURKEY Dr. Selvanathan Arumugam Ph.D Research Scientist, Department of Chemistry, University of Georgia, GA-30602, USA.
Review Board Members Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168, Australia Dr. Zhiming Yang MD., Ph. D. Department of Radiation Oncology and Molecular Radiation Science,1550 Orleans Street Rm 441, Baltimore MD, 21231,USA Dr. Jifeng Wang Department of Mechanical Science and Engineering, University of Illinois at Urbana-Champaign Urbana, Illinois, 61801, USA Dr. Giuseppe Baldacchini ENEA - Frascati Research Center, Via Enrico Fermi 45 - P.O. Box 65,00044 Frascati, Roma, ITALY. Dr. Mutamed Turki Nayef Khatib Assistant Professor of Telecommunication Engineering,Head of Telecommunication Engineering Department,Palestine Technical University (Kadoorie), Tul Karm, PALESTINE. Dr.P.Uma Maheswari Prof & Head,Depaartment of CSE/IT, INFO Institute of Engineering,Coimbatore.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Dr. T. Christopher, Ph.D., Assistant Professor & Head,Department of Computer Science,Government Arts College(Autonomous),Udumalpet, India. Dr. T. DEVI Ph.D. Engg. (Warwick, UK), Head,Department of Computer Applications,Bharathiar University,Coimbatore-641 046, India. Dr. Renato J. orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business School,Rua Itapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Visiting Scholar at INSEAD,INSEAD Social Innovation Centre,Boulevard de Constance,77305 Fontainebleau - France Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,São Paulo Business SchoolRua Itapeva, 474 (8° andar),01332-000, São Paulo (SP), Brazil Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Javad Robati Crop Production Departement,University of Maragheh,Golshahr,Maragheh,Iran Vinesh Sukumar (PhD, MBA) Product Engineering Segment Manager, Imaging Products, Aptina Imaging Inc. Dr. Binod Kumar PhD(CS), M.Phil.(CS), MIAENG,MIEEE HOD & Associate Professor, IT Dept, Medi-Caps Inst. of Science & Tech.(MIST),Indore, India Dr. S. B. Warkad Associate Professor, Department of Electrical Engineering, Priyadarshini College of Engineering, Nagpur, India Dr. doc. Ing. Rostislav Choteborský, Ph.D. Katedra materiálu a strojírenské technologie Technická fakulta,Ceská zemedelská univerzita v Praze,Kamýcká 129, Praha 6, 165 21 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials,CSIRO Process Science & Engineering Private Bag 33, Clayton South MDC 3169,Gate 5 Normanby Rd., Clayton Vic. 3168 DR.Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg.,Hampton University,Hampton, VA 23688 Mr. Abhishek Taneja B.sc(Electronics),M.B.E,M.C.A.,M.Phil., Assistant Professor in the Department of Computer Science & Applications, at Dronacharya Institute of Management and Technology, Kurukshetra. (India). Dr. Ing. Rostislav Chotěborský,ph.d, Katedra materiálu a strojírenské technologie, Technická fakulta,Česká zemědělská univerzita v Praze,Kamýcká 129, Praha 6, 165 21
Dr. Amala VijayaSelvi Rajan, B.sc,Ph.d, Faculty – Information Technology Dubai Women’s College – Higher Colleges of Technology,P.O. Box – 16062, Dubai, UAE
Naik Nitin Ashokrao B.sc,M.Sc Lecturer in Yeshwant Mahavidyalaya Nanded University
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Dr.A.Kathirvell, B.E, M.E, Ph.D,MISTE, MIACSIT, MENGG Professor - Department of Computer Science and Engineering,Tagore Engineering College, Chennai Dr. H. S. Fadewar B.sc,M.sc,M.Phil.,ph.d,PGDBM,B.Ed. Associate Professor - Sinhgad Institute of Management & Computer Application, Mumbai-Banglore Westernly Express Way Narhe, Pune - 41 Dr. David Batten Leader, Algal Pre-Feasibility Study,Transport Technologies and Sustainable Fuels,CSIRO Energy Transformed Flagship Private Bag 1,Aspendale, Vic. 3195,AUSTRALIA Dr R C Panda (MTech & PhD(IITM);Ex-Faculty (Curtin Univ Tech, Perth, Australia))Scientist CLRI (CSIR), Adyar, Chennai - 600 020,India Miss Jing He PH.D. Candidate of Georgia State University,1450 Willow Lake Dr. NE,Atlanta, GA, 30329 Jeremiah Neubert Assistant Professor,Mechanical Engineering,University of North Dakota Hui Shen Mechanical Engineering Dept,Ohio Northern Univ. Dr. Xiangfa Wu, Ph.D. Assistant Professor / Mechanical Engineering,NORTH DAKOTA STATE UNIVERSITY Seraphin Chally Abou Professor,Mechanical & Industrial Engineering Depart,MEHS Program, 235 Voss-Kovach Hall,1305 Ordean Court,Duluth, Minnesota 55812-3042 Dr. Qiang Cheng, Ph.D. Assistant Professor,Computer Science Department Southern Illinois University CarbondaleFaner Hall, Room 2140-Mail Code 45111000 Faner Drive, Carbondale, IL 62901 Dr. Carlos Barrios, PhD Assistant Professor of Architecture,School of Architecture and Planning,The Catholic University of America Y. Benal Yurtlu Assist. Prof. Ondokuz Mayis University Dr. Lucy M. Brown, Ph.D. Texas State University,601 University Drive,School of Journalism and Mass Communication,OM330B,San Marcos, TX 78666 Dr. Paul Koltun Senior Research ScientistLCA and Industrial Ecology Group,Metallic & Ceramic Materials CSIRO Process Science & Engineering Dr.Sumeer Gul Assistant Professor,Department of Library and Information Science,University of Kashmir,India Dr. Chutima Boonthum-Denecke, Ph.D Department of Computer Science,Science & Technology Bldg., Rm 120,Hampton University,Hampton, VA 23688 Dr. Renato J. Orsato Professor at FGV-EAESP,Getulio Vargas Foundation,S찾o Paulo Business School,Rua Itapeva, 474 (8째 andar)01332-000, S찾o Paulo (SP), Brazil Dr. Wael M. G. Ibrahim Department Head-Electronics Engineering Technology Dept.School of Engineering Technology ECPI College of Technology 5501 Greenwich Road - Suite 100,Virginia Beach, VA 23462
Dr. Messaoud Jake Bahoura Associate Professor-Engineering Department and Center for Materials Research Norfolk State University,700 Park avenue,Norfolk, VA 23504
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. P. Kamakkannan,M.C.A., Ph.D ., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India. Dr. V. Karthikeyani Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 008, India. Dr. K. Thangadurai Ph.D., Assistant Professor, Department of Computer Science, Government Arts College ( Autonomous ), Karur - 639 005,India. Dr. N. Maheswari Ph.D., Assistant Professor, Department of MCA, Faculty of Engineering and Technology, SRM University, Kattangulathur, Kanchipiram Dt - 603 203, India. Mr. Md. Musfique Anwar B.Sc(Engg.) Lecturer, Computer Science & Engineering Department, Jahangirnagar University, Savar, Dhaka, Bangladesh. Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom. Dr. V. Vallimayil Ph.D., Director, Department of MCA, Vivekanandha Business School For Women, Elayampalayam, Tiruchengode - 637 205, India. Mr. M. Moorthi M.C.A., M.Phil., Assistant Professor, Department of computer Applications, Kongu Arts and Science College, India Prema Selvaraj Bsc,M.C.A,M.Phil Assistant Professor,Department of Computer Science,KSR College of Arts and Science, Tiruchengode Mr. G. Rajendran M.C.A., M.Phil., N.E.T., PGDBM., PGDBF., Assistant Professor, Department of Computer Science, Government Arts College, Salem, India. Dr. Pradeep H Pendse B.E.,M.M.S.,Ph.d Dean - IT,Welingkar Institute of Management Development and Research, Mumbai, India Muhammad Javed Centre for Next Generation Localisation, School of Computing, Dublin City University, Dublin 9, Ireland Dr. G. GOBI Assistant Professor-Department of Physics,Government Arts College,Salem - 636 007 Dr.S.Senthilkumar Post Doctoral Research Fellow, (Mathematics and Computer Science & Applications),Universiti Sains Malaysia,School of Mathematical Sciences, Pulau Pinang-11800,[PENANG],MALAYSIA. Manoj Sharma Associate Professor Deptt. of ECE, Prannath Parnami Institute of Management & Technology, Hissar, Haryana, India RAMKUMAR JAGANATHAN Asst-Professor,Dept of Computer Science, V.L.B Janakiammal college of Arts & Science, Coimbatore,Tamilnadu, India Dr. S. B. Warkad Assoc. Professor, Priyadarshini College of Engineering, Nagpur, Maharashtra State, India Dr. Saurabh Pal Associate Professor, UNS Institute of Engg. & Tech., VBS Purvanchal University, Jaunpur, India Manimala Assistant Professor, Department of Applied Electronics and Instrumentation, St Joseph’s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Dr. Qazi S. M. Zia-ul-Haque Control Engineer Synchrotron-light for Experimental Sciences and Applications in the Middle East (SESAME),P. O. Box 7, Allan 19252, Jordan Dr. A. Subramani, M.C.A.,M.Phil.,Ph.D. Professor,Department of Computer Applications, K.S.R. College of Engineering, Tiruchengode - 637215 Dr. Seraphin Chally Abou Professor, Mechanical & Industrial Engineering Depart. MEHS Program, 235 Voss-Kovach Hall, 1305 Ordean Court Duluth, Minnesota 558123042 Dr. K. Kousalya Professor, Department of CSE,Kongu Engineering College,Perundurai-638 052 Dr. (Mrs.) R. Uma Rani Asso.Prof., Department of Computer Science, Sri Sarada College For Women, Salem-16, Tamil Nadu, India. MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol "Noshirvani" University of Technology, Iran. Dr. Kulasekharan, N, Ph.D Technical Lead - CFD,GE Appliances and Lighting, GE India,John F Welch Technology Center, Plot # 122, EPIP, Phase 2,Whitefield Road,Bangalore – 560066, India. Dr. Manjeet Bansal Dean (Post Graduate),Department of Civil Engineering ,Punjab Technical University,Giani Zail Singh Campus, Bathinda -151001 (Punjab),INDIA Dr. Oliver Jukić Vice Dean for education, Virovitica College, Matije Gupca 78,33000 Virovitica, Croatia Dr. Lori A. Wolff, Ph.D., J.D. Professor of Leadership and Counselor Education, The University of Mississippi, Department of Leadership and Counselor Education, 139 Guyton University, MS 38677
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Contents RELATIONSHIP BETWEEN MODELED CLEAR- SKY SOLAR RADIATION and MEASURED SOLAR RADIATION DATA in UYO, NIGERIA by Onwude Daniel Iroemeha, Olosunde William Adebisi, Akindele Folarin Alonge......................................................................................................................................................[36] Parameters Optimization For Gas Metal Arc Welding of Mild Steel Using Taguchi’s Technique by Nishant, Ashok Kumar Mishra, Dr. B.K.Roy.....................................................................................................................[43]
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
RELATIONSHIP BETWEEN MODELED CLEAR- SKY SOLAR RADIATION and MEASURED SOLAR RADIATION DATA in UYO, NIGERIA Onwude, Daniel Iroemeha #1, Olosunde, William Adebisi #2, Akindele, Folarin Alonge#3 # Department of Agricultural and Food Engineering, University of Uyo, PMB 1017, Uyo, Nigeria 1
danafricgems@gmail.com williamolosunde@uniuyo.edu.ng 3 falonge6@yahoo.com Abstract— Solar radiation available to dry crop on any commodity on buildings or drying on the stalk by clear-day was mathematically simulated on a local standing in bundies. Solar drying differs from sun drying computer, using JAVA object oriented programming in that a structure is used to enhance the effect of the language with NETBEANS IDE platform, for predicting availability and adequacy of solar radiation according to solar radiation available in Uyo, Akwa Ibom State, Nigeria. Irtwange and Adebayo [19]. The deterministic model was developed using existing Hottel solar radiation model and set of equations, taking The amount of solar radiation available to any into consideration the factors of two components of solar given location depends on latitude, time of year, hour of radiation: The beam and diffused components. The output the day and the angle of inclination of the collector, of the model rapidly produced hourly, monthly and daily Duffie and Beckman [15]. data of insolation on horizontal surface and was verified and validated using existing solar radiation data, gotten The need to study the amount of solar radiation from the Nigeria Metrological Department, Department of Geography and Regional Planning, University of Uyo, available at a given location without using any Nigeria. The simulated and measured solar radiation measuring instrument becomes indispensible in the results were subjected to t-test and were correlated using drying of agricultural crops. SPSS statistical software. The results showed that there Alonge and Oje [2] developed a model using Basic were no significant difference between the simulated data language to predict the available solar radiation for crop and measured data throughout the months of the year. Hence, the program developed can be used to adequately drying at a tropical station llorin, Nigeria. predict the total global clear-sky radiation of any day of the month in Uyo, NigeriaKeywords: clear-sky radiation, A. EXTRATERRESTIAL RADIATION SPSS, Day of the year, latitude, longitude, measured data, This is the Instantaneous Insolation at any given time for simulated data, Total radiation, Uyo, Nigeria 2
hourly calculation for Insolation. Gsc has to be integrated over each hour. The hourly Insolation I0 is given by
I. INTRODUCTION
= Global solar radiation data is essential for the study and design of economic viability of system that uses solar energy.
]
δ
[1 + 0. ф+
ф
δ
(1)
Where Io can be written in term of average hourly daily extraterrestrial solar radiation as:
In most of the tropical countries the sun is the source of energy for drying. Natural sun drying is one of the most common ways to conserve agricultural products in Nigeria. This involves the spreading of the commodity in the sun on a suitable surface, hanging of
36
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 B. CLEAR SKY RADIATION (TOTAL) I0= (12*3600/∏) * Gsc [1 + 0. ] * The atmosphere reduces extraterrestrial radiation by Cosδcosϕsin(ω2-ω1) + {[2∏(ω2-ω1)]/360}sinϕsinδ scattering and absorbing some of the radiation. The (2) radiation that finally gets to earth on a clear day is the clear day sky radiation. Hottel [18] presented a This is the hourly daily extraterrestrial (outside convenient method for estimating beam radiation (Ib) the earth’s surface) solar radiation on the horizontal through clear skies as follows surface (Duffie and Beckman [15]). Ib = Io*Tb Where
Where Tb=ao+ a1 e
December 31= n = 365) Gsc = 1353wm-2 is the solar constant, ф is the latitude of Uyo, Akwa Ibom State, Nigeria.
=radians=180
(9)
The standard values of ao, a1 and k from table provided in Beckman and Duffie [15]. Where
δ is the declination angle given as (284+
θz
ao, a1, k are functions only of latitude and visibility.
Where ω1 and ω2 are solar hour angle at point 1 and point 2 respectively (ω1=7:00am, ω2= 5:00 pm solar time).
23.45
-k/
Where θz can be gotten from equation (5)
ߨ= 3.142;
δ=
(8)
is the Julian day number (i.e Janurary
o=
a/a* r1 =a1/a1* and rk = k/k*
Also Hottel gave equations for (ao*, a1* and k*) n/365)
where
ߨ
ao *
=
0.4237 – 0.00821 (6-A)2
*
=
0.5055+ 0.00595 (6.5-A)
=
0.2711 + 0.01858 (2.5-A)2
o
a1
δ= 23.45sin [360 (284+ n/365)
(3)
-1
ω= cos (-tanϕtanδ)
k*
2
(4) Where A = altitude of observers location (Uyo is 0.2km) In Kilometers
To calculate the solar hour angle ω1 and ω2, the solar time for the location must be obtained.
ro = 0.95 r1 = 0.98 rk = 1.02 at 0.2km altitude. Solar time = standard time + 4(Lst-Lloc) + E
(5) With the above formulas and values, equation (9) can be written as.
Where; 0
Lst = Standard meridian for the local time zero, 15 for Nigeria. Lloc
=
Longitude
of
the
location
due
*
*
-
*
k
Tb = ao (ro) + a1 (r1) exp [k (r )] / cosθz
(10)
Now, Tb can be calculated for a clear sky.
East
Diffused radiation, Id on horizontal surface is given by: E= correlation due to perturbations on the earth’s rate of rotation and is given by: 9.87 sin2B- 7.53CosB- 1.5 sinB
(6)
Β =360
(7)
Id = Io Td
(11)
Where Td = 0.2739 Tb * k
(12)
Total solar radiation, It on the horizontal surface is It =Ib + Id
37
(13)
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Hence, from the value of It the total hourly, daily III. RESULTS AND DISCUSSION and monthly solar radiation on an horizontal surface can be calculated and simulated on a computer A. RESULTS A two-sample t-test assuming equal variances using a pooled estimate of the variance was performed to test the hypothesis that the resulting mean Global solar radiation of Uyo, Nigeria for measured solar data and the simulated solar radiation data were equal.
Thus, the objective of this study was create a simulation for clear-sky solar radiation prediction; to correlate mathematically the relationship between simulated available clear-sky solar radiation on any given day in Uyo, Nigeria and the measured data and to determine if there was any significant difference between the data generated by the simulated program and the measured data II.
The hypotheses for this test are H0: µ1 = µ2 versus Ha: µ1 ≠ µ2 or, in words, the following:
MATERIALS AND METHOD
A. Materials Used
Null hypothesis (H0): The mean clear sky solar radiation of any given day of the year using both the measured and simulated solar data is the same.
Climate data for Uyo and its environs is based on weather conditions at Nigerian Meteorological Department Uniuyo station. The data collected covered a period of 14 years from 1995-2008, which was used to verify, validate and compare with the simulated data.
Alternative hypothesis (Ha): The mean clear sky solar radiation of any given day of the year using both the measured and simulated solar data are different. TABLE 1
B. Method
MAXIMUM OBTAINABLE MONTHLY SOLAR RADIATION H, (MJ/M2) FOR EACH MONTH
The program was written in java (Object-Oriented Programming language) using NETBEANS IDE platform which enabled the easy writing, verification, validation and testing of the model on any operating system. It was developed and tested on a local computer with windows operating system.
Month
In the deterministic model the input parameters were kept dynamic and change with time (hour, day and month). From the mathematical model developed, equations(2),(3),(4),(5),(6),(7),(8),(9),(10),(11),(12) and (13) were used to compute the total clear sky radiation on the horizontal earth surface(It) for the average day of every month in Uyo, Nigeria. Each stage of the model development was tested. The simulated and measure solar radiation result were subjected to t-test and was correlated using SPSS statistic software
38
January
Radiation 2 MJ/m 11.04785
February
11.60038
March
11.62785
April
12.04607
May
11.62785
June
10.41859
July
10.89985
August
10.51037
September
11.61859
October
11.62685
November
11.61859
December
10.89888
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 TABLE 2 MEASURED MONTHLY MEAN SOLAR RADIATION (MJ/M2) YEARS
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
NOV
DEC
1995
9.8
10.5
11.4
13.4
10.8
11.3
8.0
7.7
9.4
10.7
13.6
10.7
1996
11.9
12.1
10.7
12.7
10.2
11.2
8.8
6.8
8.9
11.8
12.3
11.8
1997
9.9
17.0
10.9
11.6
11.5
9.4
8.9
8
10.2
11.3
9.5
10.7
1998
9.0
11.7
7.7
11.9
12.8
11.3
9.4
8.4
7.6
10.6
9.6
10.3
1999
10.4
12.6
9.5
12.4
10.0
9.7
5.9
8.3
8.5
8.2
9.0
12.2
2000
9.0
11.3
10.5
9.7
9.2
9.9
7.8
7.5
8.0
9.5
10.0
11.5
2001
11.8
10.9
10.7
12.4
10.8
10
7.4
6.1
8.2
10.3
10.7
12.0
2002
11.6
12.0
10.5
10.5
11.2
10.8
9.3
7.5
10.3
9.3
12
11.5
2003
9.6
9.6
10.0
11.3
10.6
8.3
10.2
7.9
10.4
11.4
10.5
9.4
2004
9.2
11.3
11.6
10.2
10.5
9.9
8.8
7.7
10.3
10.9
10.4
10.4
2005
10.8
11.8
10.4
11.7
9.0
10.7
8.0
8.0
11.0
12.0
13.4
12.3
2006
10.8
10.9
12.1
12.8
10.6
10.8
7.9
9.7
9.0
11.5
12.1
13.3
2007
12.4
13.0
12.9
15.6
12.1
9.3
9.7
7.5
10.6
11.2
10.04
11.7
2008
12.0
13.5
11.0
13.6
10.8
10.0
9.2
7.6
9.8
10.2
9.6
11.3
TOTAL
148.2
168.2
149.9
169.8
150.1
142.6
119.3
108.7
132.2
148.9
152.74
159.1
AVERAGE
10.58
12.01
10.7
12.12
10.72
11.88
8.52
7.76
9.44
10.63
10.91
11.36
Source: Department of Geography and Regional Planning, Uniuyo, Nigeria. TABLE 3 GROUP STATISTICS
Radiati on
Typ e
N
Mean
Std. Deviation
Std. Error Mean
1
12
10.56
1.371
.396
2
12
11.24
.516
.149
TABLE 4 INDEPENDENT SAMPLES TEST
Levene's Test for Equality of Variances
t-test for Equality of Means 95% Confidence Interval of the Difference
Radiation
Equal variances assumed Equal variances not assumed
F
Sig.
t
Df
Sig. (2tailed)
Mean Difference
Std. Error Difference
Lower
Upper
4.219
.052
1.604
22
.123
-.679
.423
-1.556
.199
1.604
14.057
.131
-.679
.423
-1.585
.228
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
Model Flowcharts (Simulation Algorithm)
Figure 1.0 Flow chart for simulation algorithm
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
B. DISCUSSION Figure 2.0 shows the maximum obtainable simulated monthly radiation from 7am to 5pm, for the average of each month of the year. The months that has the maximum and minimum total monthly radiation in Uyo, Akwa ibom State are shown. As expected, April is the hottest month with a total monthly solar radiation of 2 12.04607MJ/m . While the lowest total monthly solar radiation are gotten in the month of June and August with a value of 10.4MJ/m2 and 10.51037MJ/m2 respectively, which are the months that has the highest amount of rainfall in Uyo, Nigeria. Figure 3.0 shows the mean measured solar radiation of uyo, Nigeria with August having the lowest solar radiation with a value of 8.00MJ/m2
Figure. 2.0 Total monthly Solar Radiation (Simulated)
Table 2.0 shows the total global solar radiation data in Uyo, Nigeria for 10 years. Fig 3.0 shows a relationship between simulated and 14 years mean measured data behavior. From the graph it can be seen that both simulated and measured are similar although slight difference can be noticed in the month of August and September due to inconsistency in the weather condition of Uyo, during these period. Table 3.0 shows the group sample statistics. From the table it can be seen that there is significant relationship between Radiation 1 (Measured) and Radiation 2 (Simulated) in the standard deviation, with a standard error of 0.396 and 0.149 respectively.
Figure 3.0 Total monthly Solar Radiation (Measured)
The Results from Table 4.0 shows that the p-value for the equal variances t-test is p = 0.123. Since this pvalue is greater than 0.05, the decision therefore is that there is no significant difference between the two groups (Measured and simulated solar radiation data). Hence, the null hypothesis is accepted. i.e H0: µ1 = µ2. Hence, the mean Solar radiation using Measured Data Brand 1 (M = 10.56, SD = 1.371, N = 12) was not significantly different from that using Simulated Brand 2 (M = 11.24, SD = 0.516, N = 12), t (22) = –1.604, p = 0.131. Thus, there is not enough evidence to conclude that the mean of both the simulated and measured solar radiation are different.
Figure. 4.0 Annual variation of global solar radiation
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 IV. CONCLUSION
From the results obtained, The program was able to predict accurately the best day of any month and the best time for drying crops, which corresponded to the measured data gotten from the Nigerian Meteorological Department Uniuyo station. The model was able to deduce that the amount of insolation obtainable on any clear day in Uyo, Nigeria is sufficient for normal crop drying. There was no significant difference between measured solar radiation data and simulated solar radiation as the p value was 0.131
[10]
Chiemeka IU (2007). Determination of aerosol composition and concentration during the 2004/2005 harmattan season at Uturu, Nigeria. Advances in Science and Technology, 1(2): 128.
[11]
Chiemeka I.U. Estimation of solar radiation at Uturu, Nigeria.International Journal of Physics Sciences vol.3 (5), (2008), pp. 126-130.
[12]
Csc, Solar Dryers their role in post-harvest processing. Commonwealth Science Council. The Commonwealth Secretariat, London 1985.
[13]
Davies JA (1966). “Radiation and Evaporation Approximate for Nigeria” Ph.D. Thesis, University of London.
[14]
Donald CA, Meteorology Today: An Introduction to Weather, Climate and the Environment. West Publishing Co. 1982.
[15]
Duffie, J.A. and Beckman, W.A. Solar Engineering of Thermal Processes.John Wiley, New York, U.S.A, 1980.
[16]
Ezekwe, C.I. and C.C.O. Ezeilo.. Measured Solar Radiation in Nigeria Environment Compared with predicted Data. Solar energy 28: (1981), pp 181-186
[17]
Ezekwe CI, The Solar Radiation Climate of Nigeria, Solar and Wind Technology. 5: (1988), pp 563 – 571.
[18]
Hottel H.C, A simple Model for Estimating the transmittance of Direct Solar Radiation through clear Atmosphere. Solar Energy 18: (1976), 129-140.
[19]
Irtwange S. V. and S. Adebayo, Development and Performance of a Laboratory-Scale Passive Solar Grain Dryer in a Tropical Environment. Journal of Agricultural Extension and Rural Development Vol 1(2) (2009), pp 042-045. Iqbal, An Introduction to Solar Radiation, Academic Press, Toronto, 390 pp, 1983.
ACKNOWLEGMENT The Authors acknowledge God almighty for his grace during the research work and also the Nigeria Metrological substation, Department of Geography and Regional Planning University of Uyo, Nigeria for the easy access to measured solar radiation data. REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[7]
[8]
[9]
Akpabio E.L and Etuk E.S. Relationship between Global Radiation and Sunshine Duration for Onne, Nigeria. Turk J Physics 27 (2003), 161-167. Alonge, A.F. and K. Oje. Computer Simulation of available Solar Radiation for Crop drying at a Tropical Station llorin, Nigeria. FUTAJEET vol 3: (2003), pp 41-46. Arinze, E. A.. Instantaneous, hourly and Daily prediction of Incident and Transmitted Solar Radiation in Layened Plastic Covered titled surface. Nigerian Journal of Solar Energy 2. (1982), pp 15-25.
[20]
Augustine C and Nnabuchi, M.N ‘ Analysis of Some Meteorological Data for Some Selected Cities in The Eastern and Southern Zone of Nigeria’, African Journal of Environmental Science and Technology, Vol.4 (2), (2010), pp 92–99 Bamiro, O.A. Empirical Relations for the determination of solar Radiation in Ibadan, Nigeria, Solar Energy. 31 (1): 1982, 8594 B. Brenndorfer, L. Kennedy, C.O. Oswin Bateman and D.S. Trim. Solar Dryers-Their Role in Post-Harvest Processing. Tropical Development and Research Institute, 127 Clerkenwell Road, London ECIR 50B, U.K, 1985. Chineke TC, Jagtap SS, A Comparison of three Empirical Models for Estimating Solar Radiation in Nigeria. Afr. J. Sci. Technol., Series A, 11: 45 – 48. Chineke TC, A Robust Method of Evaluating the Solar Energy Potential of a Data Sparse Site. The Physical Scientist (c) 2002, (1): 59 – 69.
[21]
Klein, S. A. Calculation of monthly average insolation on tilted surface, Solar Energy 19:325, 1977.
[22]
Kreider, J.F and K. Frank, Principles of Solar Engineering. McGraw Hill Book Company, New York, USA 1978.
[23]
Orgil, J.F and Hollands, K.G.T, Correlation Equation for Hourly Diffuse Radiation on a Horizontal Surface, Solar Energy 19: (1977) pp 257.
[24]
Roger McHaney, Understanding Computer Simulation, Ventus Publishing Aps, UK, 2009.
[25]
Radosavijevic JA, Definition of Intensity of Solar Radiation on Horizontal and Oblique Surfaces on Earth. Working and Living Environmental Protection. 2 (1): (2001), pp 77-86.
[26]
Swartman RK, Ogunlade O, Solar Radiation Estimates from Common Parameters, Solar Energy, 56: (1963), pp 145 – 158.
Chineke TC Renewable Energy: Equations for Estimating Global Solar Radiation Data Sparse Regions. Science Direct, (2007), pp.1
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013
Parameters Optimization For Gas Metal Arc Welding of Mild Steel Using Taguchi’s Technique 1
2
3
Nishant , Ashok Kumar Mishra , Dr. B.K.Roy . 1.Assistant Prof. Department of Mechanical Engineering, Om Institute of Technology and Management, Hisar, Haryana 2.Assistant Prof. Department of Mechanical Engineering, BRCMCET, BAHAL, BHIWANI 3.Director, Om Institute of Technology and Management, Hisar, Haryana
E-mail: nishantverma111@gmail.com1 Ashokaero04@yahoo.com2 drbkroy@hotmail.com3 Abstract: Welding is a basic manufacturing process for making components or assemblies. Recent welding economics research has focused on developing the reliable machinery database to ensure optimum production. In this paper, the optimization of welding input process parameters for obtaining greater weld strength in the Gas Metal Arc Welding (GMAW) of Mild Steel is presented. The Taguchi method is adopted to analyze the effect of each welding process parameter on the weld strength, and the optimal process parameters are obtained to achieve greater weld strength. A L9 Orthogonal array was selected for analysis of data. Investigation to find out the influence of Arc Current, Arc Voltage & Gas Flow Rate on Tensile Strength, Hardness of Parent Metal, Weld Zone & Heat Affected Zone & Microstructure during welding process was carried out using ANOVA and Regression equations for each response were developed. Experimental results are provided to illustrate the proposed approach.
Welding processes play an important role in metal fabrication industries. There are various welding techniques. The two most commonly used types are tungsten inert gas (TIG) and metal inert gas (MIG/MAG) welding process. The two processes differ in that the TIG process uses a non consumable electrode, while the MIG/MAG process utilizes a consumable electrode for joining. A metal inert gas (MIG) welding process consists of heating, melting and solidification of parent metals and a filler material in localized fusion zone by a transient heat source to form a joint between the parent metals. MIG welding parameters are the most important factors affecting the quality, productivity and cost of welded joint. Factors such as arc current, arc voltage and welding speed and their interactions play a significant role in the welding process.[5] All commercially important metals such as carbon steels, high strength low alloy steel, stainless steel, aluminium, copper, titanium, and nickel alloys can be welded in all positions with GMAW process by choosing appropriate shielding gas, electrode, and welding variable. The process is illustrated in Figure 1.[8]
Keywords: GMAW welding, Taguchi method, Tensile strength and Hardness of the weld, Microstructure.
Figure 1: MIG welding process 1. INTRODUCTION Welding is a process of joining two materials. It is more economical and faster process compared to both casting and riveting. Welding find applications in the manufacture of many products around us name few ships, rail road equipments, building construction, boilers, launch vehicles, pipelines, nuclear power plants, aircrafts, automobiles, pipelines. Various welding methods available are: Shielded Metal Arc Welding (SMAW), Submerged Arc Welding (SAW), Tungsten Inert Gas (TIG) Welding, metal inert gas (MIG) welding, Plasma Arc Welding (PAW), Gas Metal Arc Welding (GMAW), Flux Cored Arc Welding (FCAW), Electro Slag Welding (ESW), and Oxyacetylene (OA) Welding.[2]
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 4. METHODOLOGY 2. DESIGN OF EXPERIMENTS (DOE) The following are the steps which were followed to achieve the objective:
Design of experiment is one of the important and powerful statistical technique to study the effect of multiple variables simultaneously and involves a series of steps which must follow a certain sequence for the experiment to yield and improved understanding of process performance. All designed experiments require a certain number of combinations of factors and levels be tested in order to observe the results of those test conditions. Taguchi approach relies on the assignment of factors in specific orthogonal arrays to determine test combinations. The DOE process is made up of three main phases: the planning phase, the conducting phase and the analysis phase. A major step in the DOE process is the determination of the combination of factors and levels which will provide the desired information.[1,5]
1) Selection of process parameters & their levels 2) Selection of quality characteristics 3) Selection of Orthogonal Array 4) Selection of Parent Metal & Filler Material 5) Preparation of steel plate specimen 6) After performing GMAW operation, the specimens were cut from the welded plate to carry out various tests. The following are the tests carried out to achieve the objective:
Analysis of the experimental results uses a signal to noise ratio to aid in the determination of the best process designs. In the present work, a plan order for performing the experiments was generated by Taguchi method using orthogonal array and analysis of parameters was done using ANOVA technique. This method yields the rank of various parameters with the levels of significance or influence of a factor on a particular output response.[14]
Table 1: Chemical Composition of Base Metal(Mild Steel 1018) P
S
Fe
0.18
0.6-0.9
0.04 max
0.05 max
98.81-99.26
Vicker Hardness Test
Microstructure Study
5.1 Selection of process parameters & their levels In the present study, three 3-level process parameters i.e. Arc current, Arc voltage and Gas Flow Rate are considered. The values of the welding process parameters are listed in Table 3.The ranges and levels are fixed based on the screening experiments. The interaction effect between the parameters is not considered. The total degrees of freedom of all process parameters are 8. The degrees of freedom of the orthogonal array should be greater than or at least equal to the degrees of freedom of all the process parameters. Hence, L9 (33) Orthogonal array was chosen which has 8 degrees of freedom.
Mild Steel is selected as a base metal for performing the experimental work.The following tables shows the composition of base metal and welding electrode
Mn
Tensile Test on UTM
5. EXPERIMENTATION
3. MATERIAL SELECTION
C
Table 3 Selected Process Parameters and their Levels Parameters
Fe
Mb 0.15 max
Welding Current (Amp) Arc Voltage (Volt) Gas Flow Rate (kg/hr)
Balance
Vanadium 0.03
Cr 0.15 max
Cu 0.05 max
S 0.35
P 0.025 max
Mn 1.40-1.85
Si 0.8-0.15
0.06-0.15
C
Table 2: Chemical Composition of Electrode- ER 70 S6
Code
Level 1
Level 2
Level 3
A
120
140
160
B
35
38
40
C
25
28
32
Nine Experiments are conducted based on the orthogonal array, instead of 27 possibilities.
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were developed based on an orthogonal array, with the aim of relating the influence of Welding Current, Arc Voltage and Gas Flow Rate. These design parameters are distinct and intrinsic feature of the process that influence and determine the composite performance.
S/N RATIO
TENSILE STRENGT H (MPa)
GFR (CFH)
VOLTAGE (Volt)
RUN
CURRENT (Amp)
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Table 4 Orthogonal array after assignment of 6.2 Taguchi Analysis for Tensile Strength parameters Table 5: Results of L9 Orthogonal Array for Tensile CURRENT VOLTAGE RUN CFH (kg/hr) (amp) (volt) Strength 1 120 35 25 2 120 38 28 3 120 40 32 4 140 35 28 5 140 38 32 6 140 40 25 7 160 35 32 1 120 35 25 361 50.7513 8 160 38 25 2 120 38 28 341.8 51.5176 9 160 40 28 3 120 42 32 552.7 54.8498 4 140 35 28 521.7 54.3484 6. RESULT & DISCUSSION 5 140 38 32 478.2 53.5922 6 140 42 25 491.8 53.8385 The aim of the experimental plan is to find the optimize 7 160 35 32 476.5 53.5613 parameters those are influencing the Tensile Strength, 8 160 38 25 397.7 51.9911 Hardness of Parent metal, Weld Zone & Heat Affected 9 160 42 28 478.2 53.5922 Zone and microstructure of weldment. The experiments 6.2.1 Response Table for Signal to Noise Ratios (Tensile Strength) :- Larger is better Table 6 Response Table for S/N Ratio (Tensile Strength) Arc Arc Gas Flow Level Current Voltage Rate
6.1 Results of Statistical Analysis of Experiments The results for various combinations of parameters were obtained by conducting the experiment as per the orthogonal array. The measured results were analyzed using the commercial software MINITAB 14 specially used for the design of experiment applications. To measure the quality characteristics, the experimental values are transformed into signal to noise ratio. The influence of control parameters such as Arc Current, Arc Voltage & Gas Flow Rate on Tensile Strength & Hardness of parent metal, weld zone & heat affected zone has been analyzed using Response table for signal to noise ratio.
1
52.37
52.89
52.19
2
53.93
52.37
53.15
3
53.05
54.09
54.00
Delta
1.55
1.73
1.81
Rank
3
2
1
Figure 2: Main effects plot for S/N ratio- Tensile Strength MainEffects Plot (data means) for SN ratios Arc Current
Arc Voltage
54.0
The response tables show the average of each response characteristic (S/N ratios) for each level of each factor. The tables include ranks based on Delta statistics, which compare the relative magnitude of effects.
53.5
Mean of SN ratios
53.0 52.5 52.0 120
140 Gas Flow Rate
160
25
28
32
35
38
40
54.0 53.5
The Delta statistic is the highest minus the lowest average for each factor. Minitab assigns ranks based on Delta values; rank 1 to the highest Delta value, rank 2 to the second highest, and so on. Use the level averages in the response tables to determine which level of each factor provides the best result.
53.0 52.5 52.0
Signal-to-noise: Larger is better
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Figure 3: Pie Chart for %age Contribution of Table 6 shows the experimental analysis for Tensile Different Parameters for Tensile Strength Strength. In our experimental analysis, the ranks indicate that gas flow rate has the greatest influence on both the S/N ratio and the mean. For S/N ratio, arc Pie Chart for % age Contribution voltage has the next greatest influence, followed by arc of Different Parameters for current. For means, arc voltage has the next greatest Tensile Strength influence, followed by arc current. Here, because our goal is to increase the tensile strength, we want factor Residual Arc levels that produce the highest mean. In Taguchi Error Current 12% experiments, we always want to maximize the S/N ratio. 24% The level averages in the response tables show that the S/N ratios and the means were maximized when the gas Gas Flow flow rate was 32, the arc voltage was 40 and the arc Rate current was 140. Arc 33% Voltage 31%
6.2.2 Analysis of variance for S/N ratios (Tensile Strength) Table 7. Analysis of Variance for Signal to Noise Ratio Source
D F
Seq SS
Adj SS
Adj MS
F
P
% Cont ributi on
Arc Current
2
3.636
3.636
1.818
1.96
0.03
24
Arc Voltage
2
4.702
4.702
2.3508
2.53
0.03
31
Gas Flow Rate
2
4.911
4.911
2.4557
2.64
0.03
33
Residual Error
2
1.858
1.858
0.9288
Total
8
15.107
The purpose of ANOVA is to investigate which welding process parameters significantly affect the quality characteristics. This is accomplished by separating the total variability of the S/N Ratios, which is measured by the sum of squared deviations from the total mean of the S/N ratio, into contributions by each welding process parameter and the error. The percentage contribution by each of the welding process parameters in the total sum of the squared deviations can be used to evaluate the importance of the process parameter change on the quality characteristic. From fig 3, we can conclude that Gas Flow rate is significantly affects the Tensile strength with contribution of 33% followed by Arc voltage with contribution of 31% and Arc current with contribution of 24%.
12
100
6.2.3 Regression Equation
Linear
Mathematical
Model
A linear regression model is developed using statistical software “MINITAB 14”. This model gives the relationship between an independent / predicted variable & a response variable by fitting a linear equation to observe data. Regression equation thus generated establishes correlation between the significant terms obtained from ANOVA analysis namely welding current (C1), arc voltage (C2) and gas flow rate (C3). In this approach, there is a single equation relating one response to the process parameters for the whole domain of investigation. The response factor involving all linear and interaction terms of the input parameters may be written as follows: C4 = - 385 + 0.65 C1 + 10.2 C2 + 12.9 C3
Table 7 shows the result of the analysis of variance (ANOVA) for the Tensile Strength. The analysis of variance was carried out at 95% confidence level. The main purpose of analysis of variance is to investigate the influence of the design parameters on Tensile strength by indicating that which parameters is significantly affected the quality characteristics. In our experimentation work, we have generated results for S/N ratios of Tensile Strength. For S/N ratios, all the factors and the interaction terms are significant at an αlevel of 0.05. For S/N ratio, Selected parameters Arc Current (p=0.0338), Arc Voltage (p=0.0283) & Gas Flow Rate (p=0.0274) are significant because their p-values are less than 0.05.
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 6.3 Taguchi analysis for hardness (Parent Metal,Weld Zone & Heat Affected Zone) Table 8: Results of L9 orthogonal array for Hardness RUN
CURRENT (Amp)
VOLTAGE (Volt)
GFR (CFH)
HARDNESS WZ (HV 1)
HARDNESS PM (HV 1)
HARDNESS HAZ (HV 1)
S/N RATIO
1
120
35
25
168.5
188
196.5
22.17
2
120
38
28
198.5
210.5
204.5
30.65
3
120
40
32
220
197.5
224.5
23.4
4
140
35
28
210
204.5
212
34.61
5
140
38
32
190
217
194
22.76
6
140
40
25
219
217.5
209.5
32.5
7
160
35
32
219
217.5
209.5
32.5
8
160
38
25
182.5
163.5
172
25.17
9
160
40
28
192
172.5
184
25.41
In our experimental analysis for hardness of weld zone, parent metal and heat affected zone, the ranks indicate that Arc Current has the greatest influence on the S/N ratio and Gas Flow Rate has the greatest influence on the mean having highest rank 1 respectively. For S/N ratio, gas flow rate has the next greatest influence, followed by arc voltage. For means, arc current has the next greatest influence, followed by arc voltage. Here, because our goal is to increase the weldability by keeping the hardness at nominal value, we want factor levels that produce the highest mean. In Taguchi experiments, we always want to maximize the S/N ratio. The level averages in the response tables show that the S/N ratios and Mean is maximized when the value of Arc Current was 140 A, the Arc Voltage was 35 V and the gas flow rate was 32 kg/hr.
6.3.1 Response Table for Signal to Noise Ratio (Hardness) Response Table for Signal to Noise Ratios Nominal is best (L 10*Log (Y bar**L 2/s**L 2)) Table 9: Response Table for S/N Ratio (Hardness)
LEVEL
CURRENT
VOLTAGE
GFR
1
25.41
29.76
26.61
2
29.96
26.2
30.23
3
27.7
27.1
26.22
6.3.2 Analysis of variance for S/N ratios (hardness)
DELTA
4.55
3.56
4
RANK
1
3
2
Table 10: Analysis of Variance Table for Signal to Noise Ratio of Hardness
Figure 4: Main effect plot for S/N Ratio (Hardness) MainEffects Plot (data means) for SNratios Arc Current
Arc Voltage
30 29
Mean of SN ratios
28 27 26 120
140 Gas Flow Rate
160
35
38
40
30 29 28 27
Sourc e
DF
Seq SS
Adj SS
Adj MS
F
P
% Contri bution
Arc Curre nt
2
31.06
31.06
15.53
0.29
0.0474
32
Arc Voltag e
2
20.58
20.58
10.29
0.19
0.0438
21
Gas Flow Rate
2
29.22
29.22
14.61
0.27
0.0485
30
Resid ual Error
2
16.66
16.66
13.33
Total
8
97.52
17
26 25
28
32
100
Signal-to-noise: Nominal is best (10*Log(Ybar**2/s**2))
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 Table 10 shows the result of the analysis of variance 6.4 Microstructure study (ANOVA) for the Hardness (PM, WZ & HAZ). The analysis of variance was carried out at 95% confidence level. The main purpose of analysis of variance is to investigate the influence of the design parameters on Hardness by indicating that which parameters is significantly affected the quality characteristics. In our experimentation work, we have generated results for S/N ratios of Hardness (PM, WZ & HAZ). For S/N ratios, all the factors and the interaction terms are significant at an Îą-level of 0.05. For S/N ratio, Selected parameters Arc Current (p=0.0474), Arc Voltage (p=0.0438) & Gas Flow Rate (p=0.0485) are significant because their pFigure 6: Microstructure of Weld Zone values are less than 0.05. Figure 5: Pie Chart for %age Contribution of Different Parameters for Hardness
Figure 7: Microstructure of Parent Metal
Figure 8: Microstructure of Heat Affected Zone
The purpose of ANOVA is to investigate which welding process parameters significantly affect the quality characteristics. This is accomplished by separating the total variability of the S/N Ratios, which is measured by the sum of squared deviations from the total mean of the S/N ratio, into contributions by each welding process parameter and the error. The percentage contribution by each of the welding process parameters in the total sum of the squared deviations can be used to evaluate the importance of the process parameter change on the quality characteristic. From fig 5 we can conclude that Arc Current is significantly affects the Hardness of Weld Zone, Parent Metal & Heat Affected Zone with contribution of 32% followed by Gas Flow Rate with contribution of 30% and Arc Voltage with contribution of 21%.
The study of welding metallurgy is very important because the overall mechanical properties of a weldment are determined by the characteristics properties of the individual microstructure present in the weld deposit and the weld heat affected zone. Both chemical inhomogeneity and changes in metallurgical structures are known to result during welding operation because most fusion welding processes generate high rates of heating & cooling in the weld metal and parent metal adjacent to the weld. Temperature change and change in microstructure introduce volume changes in the area surrounding the weld and hence cause straining, plastic flow, residual stresses or even cracking. Acc. to fig 6,7 & 8, microstructure of Weld Zone, Heat Affected Zone & Parent Metal consists of
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 fine grains of Ferrite and Pearlite. No formation of cost and maximum profit for the organization and to Martensite takes place. So according to our results we minimize the weld defects as well as welding problems can conclude that our weldments have lower hardness for further future work. because both ferrite and pearlite are soft constituents & there is no sign of formation of Martensite. Ferrite Ferrite is B.C.C iron phase with very limited 0 solubility for carbon (max 0.025% at 723 C) Ferrite is soft & ductile Pearlite The pearlite consists of alternate lamellae of Ferrite & Cementite. It has properties somewhere between Ferrite & Cementite 7. CONCLUSION The present study can be concluded in the following steps: 1. Taguchi design of experiment technique can be very efficiently used in the optimization of welding parameters in manufacturing operations 2. Gas Flow rate has the highest influence on the Tensile strength with contribution of 33% followed by Arc voltage with contribution of 31% and Arc current with contribution of 24%. 3. Arc Current is significantly affects the Hardness of Weld Zone, Parent Metal & Heat Affected Zone with contribution of 32% followed by Gas Flow Rate with contribution of 30% and Arc Voltage with contribution of 21%. 4. Thus design of experiments by Taguchi method was successfully used to find the optimum welding parameters for Tensile Strength & Hardness of Mild Steel (1018) 8. SCOPE FOR FUTURE WORK This study presented an efficient method for determining the optimal Gas Metal Arc welding parameters for increasing weldability of Mild steel of grade SA 1018 under varying conditions through the use of the Taguchi parameter design process. This process was applied using a specific set of control and a response variable of Tensile Strength, Hardness of Weld zone, Parent Metal 3 & Heat Affected Zone. The use of the L9 (3 ) orthogonal array, with Three control parameters (Arc Current, Arc voltage & Gas Flow rate) used for this study to be conducted with a sample of 9 work pieces. It is also carried out for other stainless steel material with more control factors and compared with AISI stainless steels to recommend which material is suitable process for recommending the process at a minimum
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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.3 MARCH 2013 REFERENCES [1]
[2]
[3]
[4]
[5]
[6]
[12]
G. Haragopal, P V R Ravindra Reddy, G Chandra Mohan Reddy, J V Subrahmanyam, " Parametric design for MIG welding of Al- 65032 alloy using Tagauchi Method ", Journal of Scientific and Industrial Research, Vol. 70, pp.844 - 850, October 2011. M. Aagakhani, E. Mehardad, E. Hayati, “Parameteric optimization of GMAW process by Tagauchi Method on weld dilution. ”, International Journal of Modeling and Optimization, Vol. 1, No. 3, August’2011. Ashok Kr. Mishra, Rakesh Sheokand, Dr. R K Srivastava, “Tribological Behaviour of Al-6061 / SiC Metal Matrix Composite by Taguchi’s Techniques”, International Journal of Scientific and Research Publications, Volume 2, Issue 10, October 2012 1 ISSN 2250-3153 Sandip Bhattacharya, Kamal Pal, Surjya K. Pal, “Multi-sensor Based prediction of metal deposition in pulsed GMAW using various soft computing models ”, ELSEVIER , Applied Soft Computing 12 (2012) 498–505, August’ 2011. Abbasi.K, Alam.S, Khan.M.I, “An experimental study on effect of increased pressure on MIG welding arc ”, International Journal of Applied Engineering Research, DINDIGUL, Vol.2,No. 1, 2011. Mr. L Suresh Kumar, Dr. S M Verma, P kiran Kumar, Dr. T Shiva Shanker, “Experimental Investigation for welding aspects of AISI 304 & 316 by Tagauchi Technique for process of TIG & MIG welding ”, International Journal of Engineering Trends and Technology, Vol.2, Issue2, 2011.
[13]
[14]
[15]
[16]
[17]
steel using Tagauchi method ”, ARPN Journal of Engineering and Applied Science, VOL. 5, NO. 1, JANUARY 2010. Farhad Kohalan, Mehdi Heidari, “A New approach for predicting and optimizing weld bead geometry in GMAW ”, International Journal of Mechanical System Science and Engineering, 2,2010. Emilie Le Guen, Muriel Carin, Remi Fabbro, “3D heat transfer model of hybrid laser welding of S355 steel and experimental validation ”, International Journal of Heat and Mass transfer, 54 (2011) 1313–1322, Dec’2010. Her-Yueh Huang, “Effect of activating flux on welding characteristics in GMAW”, ELSEVIER, 31 (2010) 2488–2495, Nov’2009. Sukhomay Pal, Santosh K. Malviya, Surja K. Pal, “Optimization of quality characteristics parameter in pulsed GMAW process using Grey-Based Tagauchi Method ”, International Journal of Advanced Manufacturing Technology, (2009) 44:1250–1260, Jan’2009. Bappa Acherjee, Souren Mitra, Arunanshu S. Kuar, Dipten Mishra, “Use of Tagauchi Quality loss function to optimize quality characteristics using laser transmission welding process of thermoplastics”, ASME Early Career Technical Journal, ASME 2009. K.Y. Benyounis, A.G. Olabi, “Optimization of different welding process using statistical and numerical approaches-A reference guide”, ELSEVIER, Advances in Engineering Software 39 (2008) 483–496,Mar’2007.
[18] J.P.Ganjigatti, D.K.Pratihar,A.Roy Choudhury, “Modeling of MIG welding process using statistical approaches ”, International Journal of Advanced Manufacturing Technology, (2008) 35:1166–1190, Nov’2006. [19] J.P. Ganjigatti, Dilip Kumar Pratihar, A.Roy Choudhury, “Global verses Cluster Wise Regression Analysis for prediction of bead geometry in MIG welding process”, ELSEVIER, Dec’2006. [20] Modern Arc Welding Technology, Ador Welding Publication [21] O.P.Khanna(2000),Welding Technology,Dhanpat Rai Publications [22] Roy RK1990,”A primer on Taguchi Method” Van Nostrand Reinhold, New York. [23] Ross P. J.,(1996), “Taguchi Techniques for quality engineering”, McGraw Hill, New York. [24] Dr. R. S Parmar, Welding Processes & Technology, AdvaniOerlikon limited. 1988
[7]
Satoshi Yamane, Takuya Yamazaki, Tomoaki Kaneta, Toru Nakazima, “Experimental and numerical simulation in temperature distribution and weld distortion in GMA welding ”, Journal of Japan welding society , 2011, Vol. 29. [8] Sanjay Kumar, P. Srinivasa Rao, A Ramakrishna, “Effect of eccentricity and arc rotational speed on weld bead geometry in pulsed GMAW of 5083 Al Alloy ”, Journal of Mechanical Engineering Research, Vol. 3. (6), pp. 186-196, June 2011. [9] M.P.Garg, Sarbjit Singh, Jodh Singh, “Experimental investigation of mechanical properties of MIG weldments of Al Alloy plates ”, Journal of Engineering and Technology, Jul – Dec’2011, Vol. 1, Issue 2. [10] Lenin.N, Shiva kumar M.,Vignesh kumar D, “Process parameter optimization in arc welding of dissimiliar metals ”, Thammasat Int .J. Sc. Tech, Vol. 15,No. 3, Sept’2010. [11] K. Kishore, P.V.Gopal Krishna, K. Veladri, Sayed Qasim Ali, “Analysis of defects gas shielded arc welding of AISI 1040
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