MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02541nam a2200277 a 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
vtls000045888 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
KUKTEM |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20251114204429.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
100524t2009 my dao f m 000 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
THE0005782(Local) |
| 039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE] |
|---|
| Level of rules in bibliographic description |
201905101616 |
| Level of effort used to assign nonsubject heading access points |
aida |
| Level of effort used to assign subject headings |
201107132321 |
| Level of effort used to assign classification |
VLOAD |
| Level of effort used to assign subject headings |
201103301003 |
| Level of effort used to assign classification |
ida |
| -- |
201005241258 |
| -- |
ida |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
UMP |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
|---|
| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
TA418.7 .R59 2009 rs Bc. |
| 100 0# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Mohammad Rizal Abdul Lani |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Modeling of milling process to predict surface roughness using artificial intelligent method / |
| Statement of responsibility, etc. |
Mohammad Rizal Bin Abdul Lani |
| 246 3# - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
Modeling of milling process to predict surface roughness using artificial intelligent method |
| Medium |
[electronic resource] |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. |
|---|
| Place of publication, distribution, etc. |
Kuantan, Pahang : |
| Name of publisher, distributor, etc. |
UMP, |
| Date of publication, distribution, etc. |
2009 |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xiii, 64 p. : |
| Other physical details |
ill. (some col.) ; |
| Dimensions |
30 cm. + |
| Accompanying material |
1 computer disc |
| 502 ## - DISSERTATION NOTE |
|---|
| Dissertation note |
Project paper (Bachelor of Mechanical Engineering with Manufacturing Engineering) -- Universiti Malaysia Pahang - 2009 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc. note |
Bibliography : p. 60-61 |
| 520 3# - SUMMARY, ETC. |
|---|
| Summary, etc. |
This thesis presents the milling process modeling to predict surface roughness. Proper setting of cutting parameter is important to obtain better surface roughness. Unfortunately, conventional try and error method is time consuming as well as high cost. The purpose for this research is to develop mathematical model using multiple regression and artificial neural network model for artificial intelligent method. Spindle speed, feed rate, and depth of cut have been chosen as predictors in order to predict surface roughness. 27 samples were run by using FANUC CNC Milling α-T14E. The experiment is executed by using full-factorial design. Analysis of variances shows that the most significant parameter is feed rate followed by spindle speed and lastly depth of cut. After the predicted surface roughness has been obtained by using both methods, average percentage error is calculated. The mathematical model developed by using multiple regression method shows the accuracy of 86.7% which is reliable to be used in surface roughness prediction. On the other hand, artificial neural network technique shows the accuracy of 93.58% which is feasible and applicable in prediction of surface roughness. The result from this research is useful to be implemented in industry to reduce time and cost in surface roughness prediction. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Surface roughness |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Machining |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name entry element |
Artificial intelligence |