Moddeling of tool wear in hot forming of metals

Code

J2-0226 (C) - included in ARIS records

Head

PhD Radomir Turk

Period

7/1/1998 - 6/30/2000

Science

Engineering sciences and technologies (4)

Reseacher status

Researcher (4)
Junior expert or technical associate (0)

Education

Doctor's degree (4)

Sex

Man (4)

Status

Employed at RO and RRD (3)
Retired (1)

No. of publications

0 (4)

Projects / Programmes source: ARIS

source: ARIS

Moddeling of tool wear in hot forming of metals

Research activity

Code	Science	Field	Subfield
2.04.02	Engineering sciences and technologies	Materials science and technology	Metallic materials

Code	Science	Field
T450	Technological sciences	Metal technology, metallurgy, metal products
T155	Technological sciences	Coatings and surface treatment

Keywords

metallurgy, forming, forging, tool wear, physical modelling, neuronal network

Evaluation (rules)

source: COBISS

Evaluation of bibliographic research performance indicators according to ARIS methodology

Citations Citations for bibliographic records in COBIB.SI that are linked to records in citation databases

source: WoS

source: Scopus

source: COBISS

Researchers (4)

no.	Code	Name and surname	Research area	Role	Period	No. of publicationsNo. of publications
1.	05204	PhD Peter Fajfar	Materials science and technology	Researcher	1998 - 2000	0
2.	08302	PhD Tomaž Rodič	Materials science and technology	Researcher	1999 - 2000	0
3.	05206	PhD Milan Terčelj	Materials science and technology	Researcher	2000	0
4.	00739	PhD Radomir Turk	Materials science and technology	Head	2000	0

Organisations (1)

no.	Code	Research organisation	City	Registration number	No. of publicationsNo. of publications
1.	1555	University of Ljubljana, Faculty of Natural Sciences and Engeneering	Ljubljana	1627074	7

Abstract

Tool wear increases the production costs and has influence on the quality of products, which are made by the forming technology. For each tool higher lifetime is desired, which means that the dimension and tool surface have to remain in side allowed tolerances. Prediction ofTool lifetime is based on the knowledge of tool wear influence parameters. With the laboratory equipment experiments, developed by our research group and with numerical analyses (FEM) of contact conditions on the surface between the tool and the workpiece the influence parameters on wear will be locally determined. Database of influence parameters on tool wear will be the input for the prediction of wear, using neuronal network. Predicted the wear on the experimental and industrial samples at equal forging conditions will be compared.