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Optimizing index positions on CNC tool magazines considering cutting tool life and duplicates
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0001-7534-0382
University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment. Division of Industrial Engineering and Management, Uppsala University, Sweden. (Produktion och automatiseringsteknik, Production and Automation Engineering)ORCID iD: 0000-0003-0111-1776
Volvo Car Corporation, ME PS Research and Technology, Skövde, Sweden.
2020 (English)In: Procedia CIRP, ISSN 2212-8271, E-ISSN 2212-8271, Vol. 93, p. 1508-1513Article in journal (Refereed) Published
Abstract [en]

Minimizing the non-machining time of CNC machines requires optimal positioning of cutting tools on indexes (stations) of CNC machine turret magazine. This work presents a genetic algorithm with a novel solution representation and genetic operators to find the best possible index positions while tool duplicates and tools life are taken in to account during the process. The tool allocation in a machining process of a crankshaft with 10 cutting operations, on a 45-index magazine, is optimized for the entire life of the tools on the magazine. The tool-indexing time is considerably reduced compared to the current index positions being used in an automotive factory. 

Place, publisher, year, edition, pages
Elsevier, 2020. Vol. 93, p. 1508-1513
Keywords [en]
Cutting tools, Genetic algorithm, Non-machining time, Tool indexing, Tool life, Computer control systems, Genetic algorithms, Machinery, Machining, Manufacture, Cutting operations, Genetic operators, Machining Process, Machining time, Optimal positioning, Solution representation, Tool allocation, Tool magazine
National Category
Production Engineering, Human Work Science and Ergonomics Manufacturing, Surface and Joining Technology
Research subject
Production and Automation Engineering; VF-KDO
Identifiers
URN: urn:nbn:se:his:diva-19401DOI: 10.1016/j.procir.2020.03.044Scopus ID: 2-s2.0-85098719766OAI: oai:DiVA.org:his-19401DiVA, id: diva2:1517773
Conference
53rd CIRP Conference on Manufacturing Systems, CMS 2020, Northwestern University, Chicago, United States, 1 July 2020 through 3 July 2020, Code 163174
Note

CC BY-NC-ND 4.0

Edited by Robert X. Gao, Kornel Ehmann

This work was partially financed by the Knowledge Foundation (KKS), Sweden, through the Synergy project, Knowledge-Driven Decision Support via Optimization (KDDS).

Available from: 2021-01-14 Created: 2021-01-14 Last updated: 2023-02-24Bibliographically approved

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Amouzgar, KavehNg, Amos H. C.

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