Tacit Knowledge Based Acquisition of Verified Machining Data

Johannes Schmid, Stefan Trabesinger, Markus Brillinger, Rudolf Pichler, Johannes Wurzinger, Radu Ciumasu

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

In the era of digitalization, the efficiency of machine tools can be improved due to optimized machining simulations. However, actual simulations only consider the kinematics of the machine tool and not the influence of the machining data, which are changed by experienced machine operators rather frequently. Moreover, these changes of machining data are not always properly communicated. For various reasons, this important knowledge of the machine operators remains tacit. Transferring tacit knowledge of the machine tool operators at the shop floor automatically to a database has not been possible so far. This paper describes a workflow for the acquisition of verified machining data and how to transfer them to a database.
Original languageEnglish
Title of host publicationICITM 2020 - 2020 9th International Conference on Industrial Technology and Management
PublisherInstitute of Electrical and Electronics Engineers
Pages117-121
Number of pages5
ISBN (Electronic)978-1-7281-4306-4
DOIs
Publication statusPublished - 5 Feb 2020
Event9th International Conference on Industrial Technology and Management - Oxford, United Kingdom
Duration: 11 Feb 202013 Feb 2020

Publication series

NameICITM 2020 - 2020 9th International Conference on Industrial Technology and Management

Conference

Conference9th International Conference on Industrial Technology and Management
Abbreviated titleICITM 2020
Country/TerritoryUnited Kingdom
CityOxford
Period11/02/2013/02/20

Keywords

  • human factor
  • machining data acquisition
  • postprocessor
  • tacit knowledge
  • verified machining data

ASJC Scopus subject areas

  • Information Systems and Management
  • Decision Sciences (miscellaneous)
  • Management of Technology and Innovation
  • Industrial and Manufacturing Engineering
  • Strategy and Management
  • Management Science and Operations Research

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