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A Novel Approach of Tool Wear Evaluation
KTH, School of Industrial Engineering and Management (ITM), Production Engineering. Harbin University of Science and Technology, China.ORCID iD: 0000-0002-9642-6983
KTH, School of Industrial Engineering and Management (ITM), Production Engineering, Production Systems.
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2017 (English)In: Journal of manufacturing science and engineering, ISSN 1087-1357, E-ISSN 1528-8935, Vol. 139, no 9, 091015Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
American Society of Mechanical Engineers (ASME) , 2017. Vol. 139, no 9, 091015
Keyword [en]
genetic algorithm, machining efficiency, machining quality, tool wear evaluation
National Category
Production Engineering, Human Work Science and Ergonomics
Identifiers
URN: urn:nbn:se:kth:diva-212251DOI: 10.1115/1.4037231ISI: 000414596100016Scopus ID: 2-s2.0-85025809045OAI: oai:DiVA.org:kth-212251DiVA: diva2:1134140
Note

QC 20170818

Available from: 2017-08-18 Created: 2017-08-18 Last updated: 2017-11-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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  • de-DE
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