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Predictive modeling of tool wear rat...

Huang, Yong.

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Predictive modeling of tool wear rate with application to CBN hard turning.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Predictive modeling of tool wear rate with application to CBN hard turning./
Author:	Huang, Yong.
Description:	213 p.
Notes:	Director: Steven Y. Liang.
Contained By:	Dissertation Abstracts International63-11B.
Subject:	Engineering, Industrial. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3072318
ISBN:	0493923411

Predictive modeling of tool wear rate with application to CBN hard turning.
Huang, Yong.

Predictive modeling of tool wear rate with application to CBN hard turning. - 213 p.

Director: Steven Y. Liang.

Thesis (Ph.D.)--Georgia Institute of Technology, 2002.

Finish machining of hardened steels is receiving increasing attention as an alternative to the grinding process. One of the road blocks for the hard turning implementation is the severe tool wear. The high cost of hard turning cutters and the tool change down-time can impact the economic viability of precision hard turning. The recent development of tool materials and geometries for hard turning applications has always been characterized by an increase in wear resistance. The ability to predict tool wear rate for various tool materials and tool geometries under various cutting conditions is important to the overall optimization of finish hard turning process.

ISBN: 0493923411Subjects--Topical Terms:

626639
Engineering, Industrial.

Predictive modeling of tool wear rate with application to CBN hard turning.
LDR:02518nam 2200289 a 45 001 933429
005 20110505
008 110505s2002 eng d
020 $a 0493923411
035 $a (UnM)AAI3072318
035 $a AAI3072318
040 $a UnM $c UnM
100 1 $a Huang, Yong. $3 1257160
245 1 0 $a Predictive modeling of tool wear rate with application to CBN hard turning.
300 $a 213 p.
500 $a Director: Steven Y. Liang.
500 $a Source: Dissertation Abstracts International, Volume: 63-11, Section: B, page: 5476.
502 $a Thesis (Ph.D.)--Georgia Institute of Technology, 2002.
520 $a Finish machining of hardened steels is receiving increasing attention as an alternative to the grinding process. One of the road blocks for the hard turning implementation is the severe tool wear. The high cost of hard turning cutters and the tool change down-time can impact the economic viability of precision hard turning. The recent development of tool materials and geometries for hard turning applications has always been characterized by an increase in wear resistance. The ability to predict tool wear rate for various tool materials and tool geometries under various cutting conditions is important to the overall optimization of finish hard turning process.
520 $a Polycrystalline cubic boron nitride (CBN) tools are commonly used in the finish hard turning currently. In order to guide the design of CBN tool geometry and to optimize cutting parameters, this dissertation develops the methodology to model the CBN tool flank/crater wear rate. First, process modeling, including thermal modeling and force modeling, is presented and validated respectively. Then, by utilizing the information from process modeling and the cutting configuration, the tool flank/crater wear rates are modeled as a function of tool geometry, cutting condition, and material properties of tool/work through abrasion, adhesion, and diffusion mechanisms in the steady state wear period. Finally, the proposed approach is verified in turning hardened 52100 bearing steel using a low CBN content tool. The result of this dissertation will help improve the state of art in designing the CBN tool geometry and optimizing cutting parameters.
590 $a School code: 0078.
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Engineering, Mechanical. $3 783786
690 $a 0546
690 $a 0548
710 2 0 $a Georgia Institute of Technology. $3 696730
773 0 $t Dissertation Abstracts International $g 63-11B.
790 $a 0078
790 1 0 $a Liang, Steven Y., $e advisor
791 $a Ph.D.
792 $a 2002
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3072318