Recent advances in modelling of metal machining processes

被引:564
作者
Arrazola, P. J. [1 ]
Oezel, T. [2 ]
Umbrello, D. [3 ,5 ]
Davies, M. [4 ]
Jawahir, I. S. [5 ]
机构
[1] Mondragon Univ, Fac Engn, Arrasate Mondragon 20500, Spain
[2] Rutgers State Univ, Mfg & Automat Res Lab, Sch Engn Ind & Syst Engn, Piscataway, NJ 08854 USA
[3] Univ Calabria, Dept Mech Energy & Management Engn, I-87036 Arcavacata Di Rende, CS, Italy
[4] Univ N Carolina, Charlotte, NC 28223 USA
[5] Univ Kentucky, ISM, Lexington, KY 40506 USA
关键词
Modelling; Machining; Chip formation; TOOL-CHIP INTERFACE; FINITE-ELEMENT SIMULATION; INDUCED RESIDUAL-STRESSES; TEMPERATURE RISE DISTRIBUTION; HEAT PARTITION-COEFFICIENT; BURR FORMATION PROCESSES; PREDICTIVE FORCE MODEL; MATERIAL FLOW-STRESS; PLAIN CARBON-STEELS; TIN-COATED CARBIDE;
D O I
10.1016/j.cirp.2013.05.006
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
During the last few decades, there has been significant progress in developing industry-driven predictive models for machining operations. This paper presents the state-of-the-art in predictive performance models for machining, and identifies the strengths and weaknesses of current models. This includes a critical assessment of the relevant modelling techniques and their applicability and/or limitations for the prediction of the complex machining operations performed in industry. This paper includes contributions from academia and industry, and is expected to serve as a comprehensive report of recent progress, as well as a roadmap for future directions. Process models often target the prediction of fundamental variables such as stresses, strains, strain-rates, temperatures etc. However, to be useful to industry, these variables must be correlated to performance measures: product quality (accuracy, dimensional tolerances, finish, etc.), surface and subsurface integrity, tool-wear, chip-form/breakability, burr formation, machine stability, etc. The adoption of machining models by industry critically depends on the capability of a model to make this link and predict machining performance. Therefore, this paper would identify and discuss several key research topics closely associated with predictive model development for machining operations, primarily targeting industry applications. (C) 2013 CIRP.
引用
收藏
页码:695 / 718
页数:24
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