Automated Planning for Multi-Objective Machine Tool Calibration: Optimising Makespan and Measurement Uncertainty

Automated Planning for Multi-Objective Machine Tool Calibration: Optimising Makespan and Measurement Uncertainty. Simon, P., Andrew, L., Andrew, C., & Peter, G. In Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling, of ICAPS 2014, pages 421--429. Association for the Advancement of Artificial Intelligence AAAI, Portsmouth, USA, June, 2014.

Paper abstract bibtex

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.

@incollection{hud20361,
           month = {June},
          author = {Parkinson Simon and Longstaff Andrew and Crampton Andrew and Gregory Peter},
          series = {ICAPS 2014},
       booktitle = {Proceedings of the Twenty-Fourth International Conference on Automated Planning and Scheduling},
         address = {Portsmouth, USA},
           title = {Automated Planning for Multi-Objective Machine Tool Calibration: Optimising Makespan and Measurement Uncertainty},
       publisher = {Association for the Advancement of Arti{fi}cial Intelligence AAAI},
            year = {2014},
           pages = {421--429},
             url = {http://eprints.hud.ac.uk/20361/},
        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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