INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013
UK: Managing Editor International Journal of Innovative Technology and Creative Engineering 1a park lane, Cranford London TW59WA UK E-Mail: firstname.lastname@example.org 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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013
International Journal of Innovative Technology & Creative Engineering Vol.3 No.7 July 2013
From Editor's Desk Dear Researcher, Greetings! Research article in this issue discusses about Categorization of Human Errors: An Investigation into Restart, Development of Empirical Model for Prediction of Surface Roughness in Turning Operation. Let us review research around the world this month; Look to the past for the fuel of the future. The experiment was short-lived, but it proved the point that ammonia – plus a small amount of coal gas to help combustion – could be used as a transport fuel. Seventy years later, ammonia may be ready to ride to the rescue again. As a fuel it has a number of attractive attributes. It doesn't release carbon when burned, is relatively easy to store and transport, and could take advantage of an existing infrastructure of storage tanks, transport ships and pipelines. These attributes give ammonia an edge over hydrogen, long touted as the fuel of the future in a hypothetical "hydrogen economy". 17th-century gadget gives up secrets to 3D printer. Researchers from Birmingham City University in the UK have scanned items like this precious 17th-century watch in exquisite detail, and recreated them using a 3D printer. The watch is part of a trove of Elizabethan and Jacobean jewellery. The watch is so innovative, researchers are calling it "the iPod of its day". We fear some of these 400-year-old processes may now be lost to us." To reveal details of the watch's construction, the team removed the remaining enamel on the surface from their 3D model to show what the metal component looked like prior to being enameled. We have effectively used new technology to capture a moment in time during the watch's original making process. 3D-printed rocket engine gets its first fiery test. Thought current 3D printing was only good for creating cute plastic versions of teapot lids, key rings and other curios? Think again. Choreographed high-power lasers or electron beams can fuse and sculpt metal powders into high-performance machine parts. Now NASA has proved that even rocket motors can be made this way. Engineers led by Tyler Hickman in the Game Changing Technology Program at NASA's Glenn Research Center in Cleveland, Ohio, worked together with rocket-motor maker Aerojet Rocketdyne of Sacramento, California. They wondered if additive layer manufacturing – the engineer's name for 3D printing – could make a precision part called a rocket injector in less time than the year it takes using conventional methods. It has been an absolute pleasure to present you articles that you wish to read. We look forward to many more new technologies 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
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.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 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 Dr. V. P. Eswaramurthy M.C.A., M.Phil., Ph.D., Assistant Professor of Computer Science, Government Arts College(Autonomous), Salem-636 007, India.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 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 Josephâ€™s College of Engineering & Technology, Choondacherry Post, Kottayam Dt. Kerala -686579 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
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 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
Contents Human Errors: An investigation into restart by Ubaid Hussain Zahidani, Iram Zehra Mirza……………………………………………...........................................................................................................
Development of empirical model for prediction of surface roughness in turning operation by P.Shabarish, G. Ranga Janardhana, K. Vijaya Kumar Reddy.......................................................................................................
HUMAN ERRORS: AN INVESTIGATION INTO RESTART Ubaid Hussain Zahidani 1, Iram Zehra Mirza 2 1Computer Science Department, Queen Marry University of London, Mile End, UK 1 email@example.com 2 Library & Information Science Department, University of Kashmir, J&K, India 2 firstname.lastname@example.org
Abstract — Human error is an assumed cause and a I.
contradiction to ones intention at the psychological
level. In addition criticality implied by human error
Human intervention is often prone to mistakes, errors
is not directly measurable, rather inferred from the
and the like. Errors lead to backtracking, hence in a field
Such erroneous acts of
requiring expertise to handle the delicate situations,
violations are the processes of mental aberrations
errors play important roles. Consider, in aviation a minor
that lead to an unintended outcome. To overcome
error can lead to catastrophic consequences. On the
violations of principles, the design of a system
other hand, an error made while heating a meal using a
should come to the rescue. This motivates the
programmed chip can be neglected relative to an air
mishap. Moreover an error in a set of systematic
investigate the slips made after restarting the
procedure affects the performance of users in a
sequential actions to lessen the error commission
common way, thus resulting in low efficiency and often
because of the non-stochastic slips, the practical
failure to achieve the desired intention. Involvement of
issues of design are addressed. Further this paper
human behavior has attracted attention from cognitive
hypothesizes the effectiveness of a visual hint at
psychologists to study mental process during the
procedure. Previously such an error occurrence was
considers activation of intended goals which
considered to be stochastic; therefore, no attention was
otherwise have been in-active. The slips are blamed
paid to manifest the particular cause. However, with the
to occur because of working memory load incurred
recent developments in the theories of cognition and
by the system at the time. A structured micro-world
explanations for commission of an error.
the This pervasive behavior of computers delivering
association of visual hints with slips manifested
high performance in everyday life of a common man
after an interruption. The results proved the
makes computer scientists more responsible towards
effectiveness of visual hints to mitigate the error
the system design and reliability. The computer
commission under high working memory load.
scientists should understand the human psychology and Keywords: Human errors, Restart errors, Initialization
believe the mental error can be made by the user of a
errors and Post completion errors, Interruptions, Cues,
system, and turn them pale. Paul Curzon in ‘The Dog,
Hen and Corn’ argues that the software developer should come out of stereotype natured development and take an extra step to understand psychological issues of design. Moreover cognitive knowledge of 97
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 II. RELATED WORK system still is losing edge over the slips made.  Introduced the slip errors within the procedural set even
Interactive system design study is about the design of
when expert knowledge is involved behind the task. To
human computer interaction systems developed by
support the slip error commission,  demonstrated
none other than a human. Finding a design flaw in
experimentally the post completion error occurrence as
controlled conditions is a step towards improving
a slip within the procedure. The work blamed slip errors
to be consequence of high working memory load, and
commission of an error in uncontrolled situation, makes
declared that often under high working memory load
design flaw open to users of a system. To increase the
person doesn’t remember the sequence and omit the
system’s robustness and reliability, it is concern of the
finalizing step.  Studied the effect of interruption
designers to look for the needs of the users.
position and the duration of interruption. The study
Engineering a system to be design proof, needs a deep
reported duration should be enough to incur a
insight into the possible human errors.  Identifies
substantial decay in memory so that a participant can
some of the design flaws where computers showed
be prone to slip. The literature further investigated
their dominance over usability needs of a user. 
interruption positions while studying post completion
Describes the same design flaws prone to Human Error,
errors. Study reported the interruption occurring before
reporting it in terms of unsuitable behavior that can
the task completion step resulted in maximum errors
affect system efficiency and safety.  Identifies an
within the procedure. Moreover, the authors also
insight in design procedures in terms of the user goals.
studied the implications of cues for future actions. The
Literature argues if an application hubs thousands of
implications reported cues to be strong to open a
features but not satisfying the basic goal of a user, that
window of opportunities for a user to leave sensory
application is void. Designing for features makes an
notes for future action and commit errors less likely.
application error prone.
Procedural errors have gained much attention of
 Investigated the statistics of 34 incidents, and
researchers, and deep insight has been gained in
reporting 92% of the deaths in those incidents were due
research of post completion errors, initialization errors,
to the human computer interaction and other 3% were
interruptions, and cues. But most of the research has
credited to software error in application. Such reports
overlooked the restart errors being committed. Restart
show the criticalities of design issues within a highly
error is omission of a step while continuing the
reliable system. Thus human errors have been concern
procedure after interruption of a certain time interval at
of research over last few decades , where models of
a particular step. The interval turns the psychological
cognition and experimentation of human error are being
state of mind to other side, and incurs a substantial
performed.  Gave categorization of error on the
decay of memory corresponding to procedural task. The
basis of the intention as “If the intention is not
decay disturbs systematicity of the procedure and it is
appropriate, this is a mistake. If the action is not what
more likely for a user to commit an error at this stage.
was intended, this is a slip.”
However the design should allow user to put future action visual/sensory cues, thus lessen error rate and
increase the safety and performance of system and
framework as: skill-based slips and lapses, rule-based
mistakes.  of
processing in HCI. Latter identified the system of 98
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 processing related to degree of penultimate step of the task completion, compared to
consciousness and hence derivable of an error. Based
when interrupted at any other step.  Introduced an
on his classification of degree of consciousness, insight into systematicity concept with the hypothesis
processing systems were diversified into Skill, Rule and
questioning” Does being motivated to avoid procedural
Knowledge based. Brief definitions of above mentioned
errors influence their systematicity?” The corresponding
processing systems is noted as: Skill
performance is prone to PC type of errors.
execution of a highly expertise task, in response to an Nevertheless evident from the literature, there has been
a little lapse in concentrating on other kind of error Rule based processing category, a user performs a task
namely: Restart error. This project is concerning the
with transitional conscious control and executes the
research methodology to study restart errors. Method
usage of rules learnt in training.
requires the user working on a game like environment. While under high memory load user has to be paused
Knowledge based processing category, a user performs
for some time interval and asked to restart performing
a task with high consciousness scaling as if he is new to
the same task. Control of the ‘in between’ group
experimental research is noticeably in cue generation.
 Introduced the term ‘Post Completion Errors’ in
Whilst users will be interacting with goal oriented
research methodologies of human error. The report
argues human are capable of doing certain things in a
environment will be studied to calculate the restart
right way and proper manner. However introduction of
errors occurred within the microworld variations of
one extra step in a procedure, after main goal is
achieved, makes it prone to errors. Researchers credit
Research observations will imply the error rate to be
this kind of error to working memory load at that
calculated. Project goals are set to reduce the errors by
particular instance. The findings of their research reported the participants doing the same task in a
studying slips, cues and working memory load.
procedural way without committing errors but when
working memory load is low. This created a relationship
knowledge for the interactive system design issues and
between Post Completion Errors (PCE) and working
factors of human error.
memory load. However to lessen the omission of last III. PROBLEM
step (slip) due to high working memory load,  Studied the introduction of visual hints (cues). The study
Paper concerns research study of Restart type of
reported to have eliminated slip error when a specific
Procedural errors. The research is based on firm
visual cue was drawn out just in time. This illustrates the
literature of procedural error research in the past. Thus
role visual cues play in controlling the procedural errors.
the independent variables e.g. position of interruption,
Furthermore to investigate deeply into effect of
duration of interruption are not taken in concern. The
interruptions, which is more often logical cause for
PCE?  Analyzed the effect of interruption position and
infrastructural basis for future research in these areas.
duration on the rate of PCE. The results of the
Nonetheless the problem of incurring a high working
memory load needed the project to study a game-like
completion errors were committed when interrupted at
environment. Whilst working under high memory load,
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 the user evaluation was not selected to be â€˜think aloudâ€™ same screen a number panel appears providing the options of clicks that will fill up that particular box
evaluation, because of the reason that speaking while
corresponding to number clicked in panel. performing would affect the performance of users. Therefore discarding the reason to increase the error rate, application was designed to generate the reports of errors committed rather incurring extra memory load on participant. Further the problem of selecting the control between the two designs needed to be simple enough to notice the errors. For the reason to make errors noticeable, control was chose to be within the number panel as cue. Moreover the microworld state cannot be made goal motivated/driven for each participant because of the consideration of ethical issues. IV. METHODOLOGY Project majorly considers the restart errors using
Fig.1. Micro-world Description
Sudoku gaming application as the microworld. The
In matrix B user can cross out the options those are not
recruited participants will be trained to gain exposure
valid to be filled in that particular box within matrix A. i.e.
towards particular microworld. In the training session no
cross the numbers in matrix B which are declined for
cue usage will be implied, hence not making users to
choice to be filled in particular box.
get used to cues. Moreover participants will be asked to 3) Step 3: Lock the choice of number to be filled in box
speed up timely. During interruption a similar secondary
within Matrix A: arithmetic task will be performed which deviates their Crossing out numbers in Matrix B leaves user with one
attention from high memory load game to another
choice of number to be filled within the box of Matrix A.
game. The participants will have to remember the color
Thus, finalize the content of a box within 3*3 matrixes
of balls appearing in sequence with numbers appearing
(Matrix A). Same pattern be repeated for each box
on them. Users will have to re-write numbered
within the 3*3 matrix.
sequence of colored balls.
4) Step 4: Confirm the number to be filled:
A. MICROWORLD PROCEDURES:
In-order to confirm the number which emerged after 1) Step 1: Click on 3*3 inner Matrix A of 9*9
crossing out numerals from Matrix B, user can now
Matrixes: Select a particular 3*3 matrix to fill up the
corresponding numeral (1-9) from number panel. Thus, confirming the box contents.
numbers within it. 2) Step 2: Click on a box within 3*3 Matrix B: Choose one box in a 3*3 matrix. On the
5) Step 5: Commit the step: User has to press the commit button to finalize the insert operation. This commit can help user to be sure about whether the number is committed to be in the 100
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 right place. Whenever user rolls mouse over the click on the Matrix A, a pop up message will appear uncommitted box, cloudy message appears that tells
reporting error. The Visual cues, like blinking number
user the particular button is committed or not. That’s
panel, are used to help the users of locked number
user can be sure about the choice of number and
panel gaming interface, as a hint. Whereas, the users
corresponding box, while he is committing every box
who don’t get a locked number panel in the application
after full consideration.
will carry on without receiving a cue over the locked panel.
6) Step 6: Rollback: Two groups of 30 participants each will be studied for 5 If user somehow makes a mistake and needs to un-
gaming session each. The errors reported in two
commit some number which is wrongly filled in a box,
different environments (with cues and without cues) will
he can press rollback button over that box, resulting in
be statistically analyzed to deliver results and test the
an empty box which is now ready to go to stepwise
procedure again. However roll back of a step can result in increase in steps taken to complete the game. Control lies in the number panel. Users will be
interrupted by the end of Step 3, when they would have decided which number to insert within the box. Group1
Evaluation of the microworld will be conducted between
users will have to unlock the number panel when they
the groups, based on the stepwise procedure drafted
return to play. Duration of interruption will be 30
above. Report of errors will be generated for each
seconds so as to enable a substantial decay of working
participant, and the data will be stored as per ethical
memory from step 3. That is, after a certain time interval
norms. Experimental hypothesis “Does Restart in a
number panel will be locked to clicks. Moreover the
Procedural Step Imply more Issues in the Design of a
research conducted by  considering the interruption
experimentation results. Position of interruption and
independent of global task performance of user. Though
time of interruption is not studied as already considered
the study accounted similarity of interruption, complexity
in literature by . However conducting an experiment
with large number of participants will provide qualitative
methodology for future research. Moreover to establish
determining factors of performance.
firm qualitative basis for design implications, data Although  states that the interruption duration shall
collected will be statistically analyzed. Analysis of
last long enough to incur a decay of goal to be resumed
variance (ANOVA) tests will be selected based on the
and has to be prevented from being rehearsed. In this
common procedure for future verification of research
experiment 30 seconds interval will incur enough decay
in memory so that the effects of interruption can be noted. The effects of interruption mean the occurrence of slip when game is resumed. Control between the groups decides whether the slip occurred or not. Control is decided to be the click on number panel. For one group experiment, the number panel is locked and for the other it’s not. If the users directly start trying to 101
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 V. CONCLUSION Routine Procedural Task. International Journal of Human-Computer Studies, pp. 217- 232. Conclusively previous literature has drawn major
 Cooper, A, “Inmates are Running the Asylum”.
conclusions considering just-in-time cues and low
Indiana Polis: SAMS. Embrey, 1999.
working memory load on participants.  Established a goal model based implication of omission errors. The
 Embrey, David, “Understanding Human Behaviour
study notes omission errors are low at self activation
and Error,” Lancashire: Human Reliability Associates
influences. This infers criticality in design issues of
 J. Gregory. Trafton ., Erik, M. Altmann ., and Raj,
omission errors The stereotyped development focuses on
M. Ratwani, “A Memory for Goals Model of Sequence
Errors, “Conference of Cognitive Modeling , pp. 39-83.
increasing the rate of omission. However the study
Manchester, UK, 2002.
confirms the issues and implications of design. Further the results solidify the significance of high working
 Li, S.Y.W ., Cox, A.L ., Blandford, A ., Cairns, P .,
memory load, personalized cue creation and its
and Abeles, A,
effectiveness. Beyond the above implications the study
Completion error: The Effects of Interruption,” Cognitive
investigates their influential behavior on restart errors
Science Conference. London, 2006.
and overall capability of lowering the error prone
“Further Investigations into Post-
 Lee, Carrie A, “Human Error in Aviation,” Retrieved February 19, 2010, from http://www.carrielee.net/pdfs/HumanError.pdf.
behavior of interactive system design.
 Mackenzie, Donald, “Knowing Machines: Essays  Back, J., Blandford, A., & Curzon, P, “Slip Errors and
on Technical Change,” Cambridge, MA: MIT Press,
Cue Salience,” ECCE, pp. 221- 224.
New York, USA, 2007. 
 Mortenson, I. C, “An Investigation of Working Memory Areas: Post Completion errors and the
Back, J ., Cheng, W.L ., Dann, R ., Curzon, P .,
and Blandford, A, “Does being Motivated to
Implication for HCI,” Middlesex University, Interaction
Procedural Errors Influence their Systimaticity,” People
Design Centre, London, 2004.
and Computers XX - Engage Proceedings of HCI, Vol.1,
 Norman, D. A,” Design Rules Based on Analysis of
Human Error,” Association for Computing Machinery,
 Broadbent, D., and Gillie, T, “What makes
Vol. 26, pp. 254-258, 1983.
interruptions disruptive? A Study of Length, Similarity
 Reason, James, “Human Error,” New York:
and Performance,”Psychological Research, pp. 243-
Cambridge University Press, 1990.
250. Oxford,1989.  Byrne, Micheal. D., and Bovair, Susan, “A Working Memory Model of a Common Procedural Error,” Cognitive Science Session, Vol.2, pp. 31-69. Atlanta, 1997 
Chung, Philip. H., and Byrne, Micheal. D, “Cue
Effectiveness in Mitigating Post Completion Errors in a 102
DEVELOPMENT OF EMPIRICAL MODEL FOR PREDICTION OF SURFACE ROUGHNESS IN TURNING OPERATION P.Shabarish#1, G. Ranga Janardhana*2, K. Vijaya Kumar Reddy#3 #
Department of Mechanical Engineering, JNTUH College of Engineering, Hyderabad, A.P, India 1 email@example.com 3
Department of Mechanical Engineering, JNTUK College of Engineering, Kakinada, A.P, India 2 firstname.lastname@example.org
Abstract In present days, the important goal in the modern industries to manufacture high quality and low cost products in just in time. The quality of the product depends upon the surface roughness and hence the surface roughness placed an important role in product manufacturing. Hence, an Empirical model is proposed for prediction of surface roughness in machining processes at given cutting conditions (speed, feed, depth of cut).For a given work-tool combination, the range of cutting conditions are selected from different cutting condition variables. These cutting conditions are applied for Factorial design of experiments (DOE) method. After conducting experiments, surface roughness values are measured. Then these experimental results are used to develop an Empirical model for prediction of surface roughness by using Multiple Regression method. Keywords: Surface Roughness, Factorial Design of Experiments (DOE), Prediction Models, Multiple Regression method.
I. INTRODUCTION Surface Roughness is one of the important attributes of job quality in machining process. The controlled surface roughness of machined component is necessary to improve its tribological properties, fatigue strength, corrosion resistance and aesthetic appearance. In addition to tolerances, surface roughness imposes one of the most critical constraints for the selection of machines and cutting parameters in process planning. A good-quality machined surface significantly improves the fatigue strength, corrosion resistance, and creep life of the component. Therefore, the desired finish surface is usually specified and the appropriate processes are selected to reach the desired quality. Turning is the most common metal removal operation and is widely used in a variety of manufacturing industries, including aerospace and automotive sectors, where quality is an important factor in the production of cylindrical, cone shaped and taper surfaces etc. Several factors influence the final surface roughness in a Turning operation. This surface roughness might be considered as the sum of two independent effects. K. Taraman et.al., developed  a mathematical
model for the surface roughness in a turning operation was developed in terms of the cutting speed, feed and depth of cut. Utilizing PL1 language and an IBM 360/50 computer, the model was used to generate contours of surface roughness in planes containing the cutting speed and feed at different levels of depth of cut. The surface roughness contours were used to select the machining conditions at which an increase in the rate of metal removal was achieved without sacrifice in surface finish.
R. M. Sunderam et. al.,  has presented the experimental development of mathematical models for predicting the surface finish of AISI 4140steel in fine turning operation using TiC coated tungsten carbide throw away tools. presented a novel experimental design called the rotatable design was used for the experimental procedures. Variables included in the model are: cutting speed, feed, depth of cut and time of cut of the tool. Statistical coding was used for the experimental variables. First order (log transformed) models were developed. For tools that exhibited lack of fit for the first-order models, a secondorder model was developed. Multiple regression analysis was used in developing these prediction models. Mike S.Lou and co-workers [3,4] developed a new technology for surface prediction, literature reviews of the surface texture, surface finish parameters, and multiple regression analysis have been carried out. M.S. Chua  et. al., developed a process planning or NC part programming, optimal cutting conditions are to be determined using reliable mathematical models representing the machining conditions of a particular work-tool combination. The development of such mathematical models requires detailed planning and proper analysis of experiments. In this paper, the mathematical models for TiN-coated carbide tools and Rรถchling T4 medium carbon steel were developed based on the design and analysis of machining experiments. The models developed were then used in the formulation of objective and constraint functions for the optimization of a multipass turning operation with such work-tool combinations. This is the base for my project work by considering three parameters spindle speed, feed, depth of cut for achieving good surface values with less percentage deviation from actual.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 From the above literature survey it is evident that, there is a need to develop a technique to predict the surface roughness of the final product, without carrying out the turning operation, for a given set of values for the process parameters. This would be very handy in determining the requirement of machining parameters such as feed rate and spindle speed for obtaining a desired surface roughness and increasing product quality.
RESULTS AND DISCUSSION
In order to establish the correlation between the cutting parameters and the surface roughness in the mathematical model form, machining issues were incorporated with different cutting conditions, aiming at simulating them for the surface roughness.
A. Design of Experiments: The experiments program was planned using a multiple variable factorial design [3*4*4]. The factors considered were Spindle Speed, Feed Rate, Depth of Cut. The range of values of each factor was set at the mixed levels, as shown in Table1. Based on this setting a total of 48 experiments, each having a combination of different levels of factors were carried out. The experiments are conducted on Lathe and selected work piece material is Mild Steel (C-0.18 to 0.25, P-0.035, Si-0.04, Cu-0.2, Mn-0.6 to 1.25). The cutting tool with High Speed Steel (W18%, Cr-55%, C-0.7)is used to machine the work piece material. The response of surface roughness was measured by using Taylor Hobson Talysurf instrument and tabulated (Table 1 & 2).
Table 1: Values of Test Variables
VALUES OF DIFFERENT LEVELS
Spindle Speed(rpm) Feed rate (mm/min)
680, 395, 225 90, 78, 72, 60
Depth of cut(mm)
1.0, 0.75, 0.5, 0.25
Table 2: Experimental Results (Train Data)
SPINDLE SPEED,V (rpm)
DEPTH OF CUT, D (mm)
SURFACE ROUGHNESS, Ra (Îźm)
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 I. Multiple Regression Model:
The multiple regression models were developed by using the independent variables (v, f, d) and the dependent variable (Ra). The experimental results were modeled using multiple regression methodology and respective models excluding and including interaction terms were developed. The equation excluding interaction terms using independent variables. For simplicity, equation is re-written as algebraic representation of regression line can be represented by
Ra = b0+b1s+b2f+b3d ………… (1)
Ra=b0+b1s+b2f+b3d+b4sf+b5ds+b6fd+b7s +b8f +b9d ..… (2)
Where Ra is surface roughness; s,f,d are predictors and b0,b1,b2,b3,b4,b5,b6,b7,b8,b9 are the multiple regression coefficients.
Where, Ra is surface roughness; s,f,d are predictors and b0,b1,b2,b3 are the regression coefficients. Using the experimental data, the analysis consisted of estimating these three variables first for first order model. If the first order model demonstrates any statistical evidence of lack of fit, a second order model can then be developed using additional data, this model is an algebraic model with interaction terms are considered. The Multiple regression equation of second order model with interaction terms can be represented by the fallowing equation 2
C. Development of Surface Roughness Prediction Model: The experimental results as shown in the Table 2 are used to develop the surface roughness prediction model. The criterion to judge the efficiency and the ability of the model to predict surface roughness values is taken as percentage deviation(∆) which is defined in equation(3). With this criterion it would be much easier to see how the proposed model fit and how the predicted values are close to the actual ones. Percentage Deviation = ((Predicted Ra – Experimental Ra)/Experimental Ra)*100 ……… (3)
I. Multiple Regression Model: Regression analysis is conducted with MINITAB using above experimental data to establish the surface roughness prediction model.
B Surface Roughness Model:
First Order Multiple Regression Model:
The purpose of developing the mathematical models relating the machining responses and their machining factors is to
The First Order Multiple Regression Model for the prediction of surface roughness is postulated by the equation(1) and the fallowing equation is found
facilitate a functional relationship between surface roughness and the independent variables (v, f, d). The following models are considered in this section.
Ra = 8.39 - 0.00201 s – 0.00588 f – 1.37 d ……… (4) Referring to the regression analysis results in Table 3, for 3degrees of freedom for regression and 44 degrees of freedom for residual error, F-ratio from the regression analysis is 4.54,
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 which is greater than F-ratio (2.41) from the statistical tables. Its P-value corresponding to F-ratio is 0.002, which is significant for 95% confidence interval. All the independent variables are not significant as their p-value are less than 0.05. The R2 value is 39%, which indicates 35.1 variability in predicting Ra with independent variables. Hence, the first order multiple regression model cannot be considered. In order to improve the prediction accuracy and for further comparison, another model called second order multiple regression model is considered.
Table 4: Regression Analysis: In Ra Vs s, f ,nf ,vnf ,vol ,fol, nfol. PREDICT OR
Table 3: Regression Analysis: In Ra vs s, f, d.
SD PREDICTOR Constant
SE COEF 0.7853
T 10.6 9
s 0.0020 105 f 0.0058 84 d 1.3676 R-Sq=39 %
R-Sq=91.2 % R-Sq(adj)=89.1% Modified Regression Analysis with interaction terms Ra = 12.4 – 0.0273 s -0.0369 f + 6.38 d +0.000212 sf – 0.125 fd +0.00338 ds +0.000008 s2 +0.000115 f2 +0.123 d2
Analysis of Variance:
Regression Analysis without interaction terms Ra = 8.39 - 0.00201 s – 0.00588 f – 1.37 d.
Source REGRESSI ON RESIDUAL ERROR
Analysis of Variance: DF SS MS 14.04 4.681 3 48 6 21.91 0.498 44 47 3
P 0.0 0
Second Order Multiple Regression Model: The Second Order Multiple regression model for the prediction of surface roughness is postulated by equation (2) and the following equation is found. In Ra = 12.4 – 0.0273 s -0.0369 f + 6.38 d +0.000212 sf – 2 2 2 0.125 fd +0.00338 ds +0.000008 s +0.000115 f +0.123 d ………… (5) If the purpose is to determine the factors and factor interaction are statistically significant in predicting Ra based on 95% confidence interval, the p-value of all the independent variables must be below 0.05. The regression analysis results are shown in Table 4.
In Table 4, for 9 degree of freedom of regression and 38 degree of freedom for residual error, the F-ratio from the regression analysis is 43.53, which is greater than F-ratio from the statistical tables (2.02) and the corresponding p-value is less than 0.05 i.e. 0.000. Hence the model is significant. All the independent variables are significant since their p-value are less than 0.05 for 95% confidence interval. The R2 value is 91.2, which indicates 91.2% variability in predicting Ra with independent variables. The values predicted by first order and second order multiple regression models are tabulated in Table 5. The percentage deviation is computed between the experimental values and predicted values for the train data and results are tabulated in Table 6.
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 Table 5: Experimental & Regression Model Values (Train Data)
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
5.56 6.286 6.99 7.542 5.52 5.964 6.224 6.322 5.862 5.128 5.96 5.168 5.428 5.423 4.914 4.857 4.68 5.462 5.784 6.992 5.176 5.186
First Order Multiple Regression Ra 5.1206 5.4631 5.8056 6.1481 5.1966 5.533 6.823 7.112 5.226 5.568 5.911 6.253 5.297 5.6395 5.982 6.3245 5.32 6.0373 6.3798 6.7223 5.176 6.1079
24 25 26 27
6.678 5.868 6.184 6.65
6.7929 5.242 6.232 6.95
5.68620 5.78137 7.13576 6.54200
28 29 30 31 32
6.562 6.678 6.549 5.674 6.342
6.8782 6.945 6.2132 6.324 6.8982
7.52406 7.64231 7.57550 5.65548 5.68207
34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 %
5.743 6.642 7.682 5.576 6.528 6.243 7.868 6.436 6.84 7.264 7.501 7.2 7.54 7.642 7.523 Deviation
6.24 6.7224 7.0049 6.242 6.4504 6.245 6.92 6.1432 6.4857 7.824 7.1707 6.124 6.556 6.898 7.241 15.236
6.22740 5.71806 7.55987 5.67144 7.48800 5.30598 6.93056 5.93885 6.42720 6.34093 6.22164 4.59990 6.09609 7.13726 5.55306 3.4265
Fig 1. Experimental and predicted Ra Values (Train Data) for model 1&2
Second Order Multiple Regression Ra 5.86663 5.65254 7.57907 6.15056 5.42981 6.31662 6.27510 5.00475 7.54631 4.68709 5.76089 6.71082 6.51980 6.29976 5.72403 6.31961 5.67150 6.26059 6.23118 5.33779 7.48489 7.01248
After the development of prediction models, the models are validated with new experimental values which are not used in training set. The test data contains 12 new experimental values. For all these input values, the response of surface roughness values are predicted and compared with experimental surface roughness values and are shown in Table 6. Further, the percentage deviation is also computed and displayed in Table 6. The Fig 3 shows the difference between experimental Ra values and the values predicted by both the models for test data.
Table 6: Experimental values and Predicted values (Test Data)
1 2 3 4 5 6 7 8 9 10 11 12
680 680 680 680 395 395 395 395 225 225 225 225 Deviat ion
F (mm/mi n)
84 81 73.2 69 64.8 85.2 81 76.2 63 71.2 88.8 80
0.55 0.95 0.65 0.35 0.20 90 0.8 0.4 0.3 0.3 0.6 0.6
6.326 6.147 6.042 5.642 5.725 4.742 4.984 5.974 6.625 6.542 6.415 6.971
Second Order Multiple Regressi on Ra 6.39181 5.71451 5.74826 5.51741 6.31076 5.14848 5.56406 6.40603 7.59006 7.49767 6.19695 6.59108 7.585
INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 Fig 2. Experimental values and Predicted values (Test Data) REFERENCES  K.Taraman, B.Lambert, “A Surface roughness model for a turning operation”, International Journal of production research, Vo.12, No.6, pp.691-703, 1974.  R.M.Sunderam, B.K.Lambert, “Mathematical models to predict surface finish in fine turning of steel”, part-1, International Journal of Production Research 19, pp.547-556, 1981.  Mike S.Lou, Joseph C.Chen, Caleb M.Li, “Surface Roughness Prediction for CNC End Milling” Journal of Industrial Technology Vol.15, pp.2-6, 1999.  Mike, S.L,C.Joseph C.Chen and M.Li, “Surface Roughness prediction for CNC End milling. Materials and process quality control manufacturing”.J.Ind.Technol.,15, pp.2-6, 1998.
 M.S.Chua, M.Rahman, Y.S.Wong and H.T.Loh, “Determination of optimal cutting conditions using Design of Experiments and optimization Techniques” Int.J.machine Tools Manufacture Vol.33 pp.297-305, 1993
The first order regression model is predicting the surface roughness with the independent variables of Speed(v),Feed(f),Depth of Cut(d) and the percentage deviation of the model is 15.236% in train data. It is observed that the first order regression model is insignificant as its F ratio from the regression analysis is less than the value from statistical tables and all the independent variables are found insignificant in the first order regression model. The reason of high percentage deviation this model cannot be used.The second order regression model is predicting the surface roughness with independent variables in s,f,d. The percentage deviation
of the model is 3.4265% in train data and 7.585% in test data. Hence, it is concluded that the Multiple Regression Model has good capabilities of predicting high accuracy surface roughness for given input conditions. Acknowledgement Authors are thankful to authorities of Jawaharlal Nehru Technological University Hyderabad (A.P) India and Jawaharlal Nehru Technological University Kakinada (A.P), India.
Published on Jul 31, 2013