Machining characteristics of hastelloy c-2000 in end milling using artificial intelligence approach / (Record no. 3706)

MARC details
000 -LEADER
fixed length control field 03771nam a2200277 a 4500
001 - CONTROL NUMBER
control field vtls000067401
003 - CONTROL NUMBER IDENTIFIER
control field KUKTEM
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20251114204532.0
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 121205t2012 my a f m 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number THE0006507(Local)
039 #9 - LEVEL OF BIBLIOGRAPHIC CONTROL AND CODING DETAIL [OBSOLETE]
Level of rules in bibliographic description 201905141305
Level of effort used to assign nonsubject heading access points amirul
Level of effort used to assign subject headings 201710121020
Level of effort used to assign classification aishah
-- 201212051202
-- 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) TJ1185 .H53 2012 rs Thesis
100 0# - MAIN ENTRY--PERSONAL NAME
Personal name Nurul Hidayah Razak
245 10 - TITLE STATEMENT
Title Machining characteristics of hastelloy c-2000 in end milling using artificial intelligence approach /
Statement of responsibility, etc. Nurul Hidayah Razak
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Place of publication, distribution, etc. Kuantan, Pahang :
Name of publisher, distributor, etc. UMP,
Date of publication, distribution, etc. 2012
300 ## - PHYSICAL DESCRIPTION
Extent xxiii, 162 p. :
Other physical details ill. ;
Dimensions 30 cm. +
Accompanying material 1 CD-ROM
502 ## - DISSERTATION NOTE
Dissertation note Thesis (Master of Engineering (Mechanical)) -- Universiti Malaysia Pahang - 2012
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Bibliography : p. [145]-165
520 3# - SUMMARY, ETC.
Summary, etc. This research work deals with the machining characteristics of Hastelloy C-2000 in the end milling operations. The mathematical model was developed through the response surface method (RSM) which basically focuses on machining characteristics such as surface roughness, tool life and cutting force using coated and uncoated carbide cutting inserts in wet conditions. The accuracy of this aforementioned technique and model was verified by ANOVA. The minimum and maximum of machining performance was presented followed by the confirmation test to validate the design variables. It is found that the models are able to predict the longitudinal component of the surface roughness, cutting force, and tool life close to those readings recorded experimentally with a 95% confident level. Artificial Neural network (ANN) prediction model was developed with back propagation algorithm with the use of multilayer perceptron and activation function of hyperbolic tangent. Feed rate is the most influential factor, followed by axial depth and cutting speed for surface roughness, tool life and cutting force. The mean absolute relative error for surface roughness of RSM models (first, second order) and ANN is 4.386 %, 2.324 % and 0.1790% for coated carbide inserts and 9.878 %, 6.681 % and 0.136 % for uncoated carbide inserts respectively. In addition, for tool life model, 8.3130 %, 4.8760 %, 0.2% for coated carbide inserts, 9.7880%, 7.6270 %, and 0.1580% for uncoated carbide inserts. Furthermore, for cutting force model 4.386 %, 2.324 % and 0.4181 % for coated carbide and 9.878 %, 6.681 % and 0.5% for uncoated carbide. The PVD coated-carbide cutting tools perform better than the uncoated-carbide in terms of the surface roughness, cutting force, and tool life. Surfaces finish and wear surfaces were characterized using an optical video measurement system, scanning electron microscope (SEM) and electron dispersive X-ray (EDX). The tool failures found in this research was flank wear, notching, and chipping. Adhesion and plastic lowering at cutting edge were the main tool wear mechanisms seen in the present work, which is clearly demonstrated by the adhered workpiece material and the formation of a built-up edge (BUE) on the tool flank. There have been a few chips found in this research and broadly they can be divided into two types. Type 1: unstable and type 2: critical. Due to the research done on the earlier models, RSM established prediction and optimization models. However, ANN serves more efficiency and accuracy because its error is very less compared to RSM. ANN has characteristics of predicting machining and they work far better when compares to mathematical modelling.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element High-speed machining
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 Milling (Metal-work)
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier <a href="http://ecollib.ump.edu.my/3788/">http://ecollib.ump.edu.my/3788/</a>
Public note Library access only
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Total checkouts Full call number Barcode Date last seen Copy number Price effective from Koha item type
  Not lost Library of Congress Classification   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   TJ1185 .H53 2012 rs Thesis 0000067924 04/09/2019 1 04/09/2019 Thesis
  Not lost Library of Congress Classification   Not for loan UMPLIB PEKAN UMPLIB PEKAN 04/09/2019   CD 6304 | TJ1185 .H53 2012 rs Thesis 0000067925 04/09/2019 1 04/09/2019 Thesis

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