Automated planning for multi-objective machine tool calibration

Optimising makespan and measurement uncertainty

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine's capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer's time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domain-independent automated planning reduce machine downtime by 10.6% and the uncertainty of measurement by 59%, it is also possible to optimise both metrics reaching a compromise that is on average 9% worse that the best-known solution for each individual metric.

Original languageEnglish
Pages (from-to)421-429
Number of pages9
JournalProceedings International Conference on Automated Planning and Scheduling, ICAPS
Volume2014-January
Issue numberJanuary
Publication statusPublished - 2014

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Machine tools
Calibration
Planning
Lead
Uncertainty
Makespan
Machine tool
Temperature
Manufacturing

Cite this

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title = "Automated planning for multi-objective machine tool calibration: Optimising makespan and measurement uncertainty",
abstract = "The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine's capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer's time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domain-independent automated planning reduce machine downtime by 10.6{\%} and the uncertainty of measurement by 59{\%}, it is also possible to optimise both metrics reaching a compromise that is on average 9{\%} worse that the best-known solution for each individual metric.",
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AU - Parkinson, Simon

AU - Longstaff, Andrew P.

AU - Crampton, Andrew

AU - Gregory, Peter

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AB - The evolution in precision manufacturing has resulted in the requirement to produce and maintain more accurate machine tools. This new requirement coupled with desire to reduce machine tool downtime places emphasis on the calibration procedure during which the machine's capabilities are assessed. Machine tool downtime is significant for manufacturers because the machine will be unavailable for manufacturing use, therefore wasting the manufacturer's time and potentially increasing lead-times for clients. In addition to machine tool downtime, the uncertainty of measurement, due to the schedule of the calibration plan, has significant implications on tolerance conformance, resulting in an increased possibility of false acceptance and rejection of machined parts. The work presented in this paper is focussed on expanding a developed temporal optimisation model to reduce the uncertainty of measurement. Encoding the knowledge in regular PDDL requires the discretization of non-linear, continuous temperature change and implementing the square root function. The implementation shows that not only can domain-independent automated planning reduce machine downtime by 10.6% and the uncertainty of measurement by 59%, it is also possible to optimise both metrics reaching a compromise that is on average 9% worse that the best-known solution for each individual metric.

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