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Research Papers

A Novel Approach of Tool Wear Evaluation

[+] Author and Article Information
Wei Ji

School of Mechanical and Power Engineering,
Harbin University of Science and Technology,
Harbin 150080, China;
Department of Production Engineering,
KTH Royal Institute of Technology,
Brinellvägen 68,
Stockholm 100 44, Sweden
e-mails: weiji@hrbust.edu.cn; weiji@kth.se

Jinkui Shi

School of Mechanical and Power Engineering,
Harbin University of Science and Technology,
Xuefu Road 52,
Harbin 150080, China

Xianli Liu

School of Mechanical and Power Engineering,
Harbin University of Science and Technology,
Xuefu Road 52,
Harbin 150080, China
e-mail: xianli.liu@hrbust.edu.cn

Lihui Wang

Fellow ASME
Department of Production Engineering,
KTH Royal Institute of Technology,
Brinellvägen 68,
Stockholm 100 44, Sweden
e-mail: lihui.wang@iip.kth.se

Steven Y. Liang

Fellow ASME
Mechanical Engineering for Advanced
Manufacturing Systems,
MARC,
Georgia Institute of Technology,
Atlanta, GA 30332-0405
e-mail: steven.liang@me.gatech.edu

1Corresponding author.

Manuscript received January 26, 2017; final manuscript received June 23, 2017; published online July 24, 2017. Assoc. Editor: Laine Mears.

J. Manuf. Sci. Eng 139(9), 091015 (Jul 24, 2017) (8 pages) Paper No: MANU-17-1044; doi: 10.1115/1.4037231 History: Received January 26, 2017; Revised June 23, 2017

The high-efficiency utilization of cutting tool resource is closely related to the flexible decision of tool life criterion, which plays a key role in manufacturing systems. Targeting a flexible method to evaluate tool life, this paper presents a data-driven approach considering all the machining quality requirements, e.g., surface integrity, machining accuracy, machining stability, chip control, and machining efficiency. Within the context, to connect tool life with machining requirements, all patterns of tool wear including flank face wear and rake face wear are fully concerned. In this approach, tool life is evaluated systematically and comprehensively. There is no generalized system architecture currently, and a four-level architecture is therefore proposed. Workpiece, cutting condition, cutting parameter, and cutting tool are the input parameters, which constrain parts of the independent variables of the evaluation objective including first-level and second-level indexes. As a result, tool wears are the remaining independent variables, and they are calculated consequently. Finally, the performed processes of the method are experimentally validated by a case study of turning superalloys with a polycrystalline cubic boron nitride (PCBN) cutting tool.

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Copyright © 2017 by ASME
Topics: Wear , Machining , Cutting
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Figures

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Fig. 1

Tool wear patterns

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Fig. 2

Tool flank wear curve

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Fig. 4

System articheture of tool life evaluation

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Fig. 5

Tool rake face wear

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Fig. 6

Tool wear curves at different cutting speeds

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Fig. 7

Influence of tool wear on surface roughness. Cutting parameters: v = 120 m/min, f = 0.15 mm/r, and ap = 0.2 mm.

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Fig. 8

Influence of tool wear on MD layer depth. Cutting parameters: v = 120 m/min, f = 0.15 mm/r, and ap = 0.2 mm.

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Fig. 9

Calculation processes of GA operation

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Fig. 10

Calculated results based on GA: (a) Ra < 1.6 μm, Dh < 56 μm and (b) Ra < 1.35 μm, Dh < 50 μm

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