Knowledge Capture in CMM Inspection Planning: Barriers and Challenges

Dimitrios Anagnostakis, James Millar Ritchie, Theodore Lim, Aparajithan Sivanathan, Rick Dewar, Raymond Sung, Frederic Nicolas Bosche, Ludovico Carozza

Research output: Chapter in Book/Report/Conference proceedingConference contribution

19 Citations (Scopus)
253 Downloads (Pure)


Coordinate Measuring Machines (CMM) have been widely used as a means of evaluating product quality and controlling quality manufacturing processes. Many techniques have been developed to facilitate the generation of CMM measurement plans. However, there are major gaps in the understanding of planning such strategies. This significant lack of explicitly available knowledge on how experts prepare plans and carry out measurements slows down the planning process, leading to the repetitive reinvention of new plans while preventing the automation or even semi-automation of the process. The objectives of this paper are twofold: (i) to provide a review of the existing inspection planning systems and discuss the barriers and challenges, especially from the aspect of knowledge capture and formalization; and (ii) to propose and demonstrate a novel digital engineering mixed reality paradigm which has the potential to facilitate the rapid capture of implicit inspection knowledge and explicitly represent this in a formalized way. An outline and the results of the development of an early stage prototype - which will form the foundation of a more complex system to address the aforementioned technological challenges identified in the literature survey - will be given.
Original languageEnglish
Title of host publicationProcedia CIRP
Subtitle of host publicationThe Sixth International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV2016)
EditorsAydin Nassehi , Stephen Newman
Publication statusPublished - 2016

Publication series

NameProcedia CIRP
ISSN (Print)2212-8271


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