Jul13

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013

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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: 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.7 JULY 2013

IJITCE PUBLICATION

International Journal of Innovative Technology & Creative Engineering Vol.3 No.7 July 2013

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) 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

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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.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

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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.

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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.)

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Mrs. Smitha Ramachandran M.Sc(CS)., SAP Analyst, Akzonobel, Slough, United Kingdom.

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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 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

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MOHAMMAD YAZDANI-ASRAMI Electrical and Computer Engineering Department, Babol "Noshirvani" University of Technology, Iran.

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013

Contents Human Errors: An investigation into restart by Ubaid Hussain Zahidani, Iram Zehra Mirza……………………………………………...........................................................................................................[97]

Development of empirical model for prediction of surface roughness in turning operation by P.Shabarish, G. Ranga Janardhana, K. Vijaya Kumar Reddy.......................................................................................................[103]

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INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013

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 ec09302@dcs.qmul.ac.uk 2 Library & Information Science Department, University of Kashmir, J&K, India 2 mirzaims@gmail.com

Abstract — Human error is an assumed cause and a I.

contradiction to ones intention at the psychological

INTRODUCTION

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

performance scale.

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

research

to

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

restart

and

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

human

task

explanations for commission of an error.

to

mitigates

was

system,

conduct

the

constructed

with

certain

a

laboratory

error

using

study

occurrence,

Sudoku

variations,

to

gaming test

behavior,

science

has

put-forth

plausible

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,

Gaming Mirco-world.

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. [1] 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, [4] 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

effectiveness

to be consequence of high working memory load, and

and

efficiency

of

a

system.

But

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. [1] 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. [6] 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. [10]

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. [6] 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

[11] 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 [12], 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. [13] 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

[14]

increase the safety and performance of system and

framework as: skill-based slips and lapses, rule-based

user.

mistakes

and

studying

information

Introduced

influential

a

cognitive

error

knowledge-based processing,

classification

system

classification

mistakes.

[7] of

Whilst

drafted

an

information

processing in HCI. Latter identified the system of 98

information

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. [2] 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

based

processing

findings

category,

the

smoother

of

research

methodology

implied

user’s

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

event.

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

activity.

experimental research is noticeably in cue generation.

[4] Introduced the term ‘Post Completion Errors’ in

Whilst users will be interacting with goal oriented

research methodologies of human error. The report

microworld

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

design.

achieved, makes it prone to errors. Researchers credit

rather

than

feature

hub

application,

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

Variations

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.

in

design

will

provide

comprehensive

memory load. However to lessen the omission of last III. PROBLEM

step (slip) due to high working memory load, [5] 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

methodology

PCE? [9] 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

experimented

post

memory load needed the project to study a game-like

completion errors were committed when interrupted at

environment. Whilst working under high memory load,

methodology

reported

most

99

of

previous

research

has

provided

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 ‘think aloud’ 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

insert

the

number

within

the

box

by

pressing

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

hypothesis.

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

B. EVALUATION

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 [3] considering the interruption

System?”

duration

being

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 [9]. However conducting an experiment

and

with large number of participants will provide qualitative

reported

memory

load

duration

an

of

interrupt

interruption

delivers

are

will

be

tested

over

the

basis

of

methodology for future research. Moreover to establish

determining factors of performance.

firm qualitative basis for design implications, data Although [9] 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

results.

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

[6] 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. [8] Established a goal model based implication of omission errors. The

[7] Embrey, David, “Understanding Human Behaviour

study notes omission errors are low at self activation

and Error,” Lancashire: Human Reliability Associates

influence

Ltd, 2007.

and

might

fail

due

to

environmental

influences. This infers criticality in design issues of

[8] J. Gregory. Trafton ., Erik, M. Altmann ., and Raj,

omission errors The stereotyped development focuses on

increasing

complexity

of

the

system

M. Ratwani, “A Memory for Goals Model of Sequence

hence

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

[9] 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-

[10] Lee, Carrie A, “Human Error in Aviation,” Retrieved February 19, 2010, from http://www.carrielee.net/pdfs/HumanError.pdf.

behavior of interactive system design.

REFERENCES

[11] Mackenzie, Donald, “Knowing Machines: Essays [1] Back, J., Blandford, A., & Curzon, P, “Slip Errors and

on Technical Change,” Cambridge, MA: MIT Press,

Cue Salience,” ECCE, pp. 221- 224.

1996.

London: ACM,

New York, USA, 2007.

[2]

[12] 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

Avoid

Procedural Errors Influence their Systimaticity,” People

Design Centre, London, 2004.

and Computers XX - Engage Proceedings of HCI, Vol.1,

[13] Norman, D. A,” Design Rules Based on Analysis of

2006.

Human Error,” Association for Computing Machinery,

[3] Broadbent, D., and Gillie, T, “What makes

Vol. 26, pp. 254-258, 1983.

interruptions disruptive? A Study of Length, Similarity

[14] Reason, James, “Human Error,” New York:

and Performance,”Psychological Research, pp. 243-

Cambridge University Press, 1990.

250. Oxford,1989.

[4] 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 [5]

Chung, Philip. H., and Byrne, Micheal. D, “Cue

Effectiveness in Mitigating Post Completion Errors in a 102

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013

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 shabarish.p@gmail.com 3

*

kvijayakumarreddy@gmail.com

Department of Mechanical Engineering, JNTUK College of Engineering, Kakinada, A.P, India 2 ranga.janardhana@gmail.com

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 [1] 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.

II.

LITERATURE SURVEY

R. M. Sunderam et. al., [2] 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 [5] 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.

103

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.

III.

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

VARIABLES DESIGNATION

DESCRIPTION

VALUES OF DIFFERENT LEVELS

s f

Spindle Speed(rpm) Feed rate (mm/min)

680, 395, 225 90, 78, 72, 60

d

Depth of cut(mm)

1.0, 0.75, 0.5, 0.25

Table 2: Experimental Results (Train Data)

104

TEST No.

SPINDLE SPEED,V (rpm)

FEED, F(mm/rev)

DEPTH OF CUT, D (mm)

SURFACE ROUGHNESS, Ra (Îźm)

1

680

90

1

5.56

2

680

90

0.75

6.286

3

680

90

0.5

6.99

4

680

90

0.25

7.542

5

680

78

1

5.52

6

680

78

0.75

5.964

7

680

78

0.5

6.224

8

680

78

0.25

6.322

9

680

72

1

5.862

10

680

72

0.75

5.128

11

680

72

0.5

5.96

12

680

72

0.25

5.168

13

680

60

1

5.428

14

680

60

0.75

5.423

15

680

60

0.5

4.914

16

680

60

0.25

4.857

17

395

90

1

4.68

18

395

90

0.75

5.462

19

395

90

0.5

5.784

20

395

90

0.25

6.992

21

395

78

1

5.176

22

395

78

0.75

5.186

23

395

78

0.5

6.384

24

395

78

0.25

6.678

25

395

72

1

5.868

INTERNATIONAL JOURNAL OF INNOVATIVE TECHNOLOGY AND CREATIVE ENGINEERING (ISSN:2045-8711) VOL.3 NO.7 JULY 2013 I. Multiple Regression Model:

26

395

72

0.75

6.184

27

395

72

0.5

6.65

28

395

72

0.25

6.562

29

395

60

1

6.678

30

395

60

0.75

6.549

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

31

395

60

0.5

5.674

Ra = b0+b1s+b2f+b3d ………… (1)

32

395

60

0.25

6.342

33

225

90

1

4.557

34

225

90

0.75

5.743

35

225

90

0.5

6.642

36

225

90

0.25

7.682

37

225

78

1

5.576

Ra=b0+b1s+b2f+b3d+b4sf+b5ds+b6fd+b7s +b8f +b9d ..… (2)

38

225

78

0.75

6.528

39

225

78

0.5

6.243

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.

40

225

78

0.25

7.868

41

225

72

1

6.436

42

225

72

0.75

6.84

43

225

72

0.5

7.264

44

225

72

0.25

7.501

45

225

60

1

7.2

46

225

60

0.75

7.54

47

225

60

0.5

7.642

48

225

60

0.25

7.523

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

2

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,

105

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

COEF

SE COEF

T

P

Constant

12.399

2.388

5.19

0.000 0.000

S

-0.027252

0.002362

11.54

F

-0.03691

0.05943

-0.62

0.538

D

6.378

1.382

4.61

0.000

0.00021172

0.0000205 6

10.3

0.000

-0.12486

0.01381

-9.04

0.000

0.0033786

0.0007958

4.25

0.000

0.00000786

0.0000018 5

4.25

0.000

0.0001154

0.0003866

0.3

0.767

0.1235

0.6681

0.18

0.854

SF FD

Table 3: Regression Analysis: In Ra vs s, f, d.

SD PREDICTOR Constant

COEF 8.3918

SE COEF 0.7853

T 10.6 9

2

P

S

0.0 0

F2 2

s 0.0020 105 f 0.0058 84 d 1.3676 R-Sq=39 %

D 0.0005428

-3.7

0.0 1

0.009419

0.62

0.5 35

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

3.75 R-Sq(adj)=34.9%

0.3645

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

F 9.4

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.

Source

DF

SS

MS

F

REGRESSIO N

9

32.7888

3.643

43.53

RESIDUAL ERROR

38

3.1801

0.0832

TOTAL

47

35.9689

P

0.00

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.

106

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)

S.No

Experimental Ra

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

23

6.384

6.4504

6.71684

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

33

4.557

6.0374

6.86505

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)

Exp No.

V (rpm)

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

%

107

F (mm/mi n)

D (mm)

Ra (mea)

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 [1] 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.

[2] 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.

[3] 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.

[4] 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.

IV.

[5] 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

CONCLUSION

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.

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