Abstract
One area that has received relatively little interest, yet could harness great potential, is reducing energy consumption by planning machine activities between manufacturing operations, while the machine is not in use. The intuitive option --which is currently exploited in manufacturing-- is to leave the machine in a normal operating state in anticipation of the next manufacturing job. However, this is far from optimal due to the thermal deformation phenomenon, which usually require an energy-intensive warm-up cycle in order to bring all the components (e.g. spindle motor) into a suitable (stable) state for actual machining. Evidently, the use of this strategy comes with the associated commercial and environmental repercussions.
In this paper, we investigate the exploitability of automated planning techniques for planning machine activities between manufacturing operations. We present a PDDL 2.2 formulation of the task that considers energy consumption, thermal deformation, and accuracy. We then demonstrate the effectiveness of the proposed approach using a case study which considers real-world data.
| Original language | English |
|---|---|
| Title of host publication | 27th International Conference on Automated Planning and Scheduling |
| Subtitle of host publication | (ICAPS 2017) |
| Editors | Laura Barbulescu, Jeremy Frank, Mausam , Stephen F. Smith |
| Publisher | Association for the Advancement of Artificial Intelligence |
| Number of pages | 9 |
| Publication status | Published - 2017 |
| Event | 27th International Conference on Automated Planning and Scheduling - Pittsburgh, United States Duration: 18 Jun 2017 → 23 Jun 2017 Conference number: 27 http://icaps17.icaps-conference.org/ (Link to Conference Website) |
Conference
| Conference | 27th International Conference on Automated Planning and Scheduling |
|---|---|
| Abbreviated title | ICAPS 2017 |
| Country | United States |
| City | Pittsburgh |
| Period | 18/06/17 → 23/06/17 |
| Internet address |
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On the Exploitation of Automated Planning for Reducing Machine Tools Energy Consumption Between Manufacturing Operations. / Parkinson, Simon; Longstaff, Andrew; Fletcher, Simon; Vallati, Mauro; Chrpa, Lukáš.
27th International Conference on Automated Planning and Scheduling: (ICAPS 2017). ed. / Laura Barbulescu; Jeremy Frank; Mausam; Stephen F. Smith. Association for the Advancement of Artificial Intelligence, 2017.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - On the Exploitation of Automated Planning for Reducing Machine Tools Energy Consumption Between Manufacturing Operations
AU - Parkinson, Simon
AU - Longstaff, Andrew
AU - Fletcher, Simon
AU - Vallati, Mauro
AU - Chrpa, Lukáš
N1 - Publisher will not allow accepted version to be put into a repository. Also cannot find ISBN or ISSN. HN 11/10/2017
PY - 2017
Y1 - 2017
N2 - There has recently been an increased emphasis on reducing energy consumption in manufacturing, driven by the fluctuations in energy costs and the growing importance given to environmental impact of manufactured goods. Lots of attention has been given to the reduction of machine tools energy consumption, as they require large amounts of energy to perform manufacturing tasks.One area that has received relatively little interest, yet could harness great potential, is reducing energy consumption by planning machine activities between manufacturing operations, while the machine is not in use. The intuitive option --which is currently exploited in manufacturing-- is to leave the machine in a normal operating state in anticipation of the next manufacturing job. However, this is far from optimal due to the thermal deformation phenomenon, which usually require an energy-intensive warm-up cycle in order to bring all the components (e.g. spindle motor) into a suitable (stable) state for actual machining. Evidently, the use of this strategy comes with the associated commercial and environmental repercussions.In this paper, we investigate the exploitability of automated planning techniques for planning machine activities between manufacturing operations. We present a PDDL 2.2 formulation of the task that considers energy consumption, thermal deformation, and accuracy. We then demonstrate the effectiveness of the proposed approach using a case study which considers real-world data.
AB - There has recently been an increased emphasis on reducing energy consumption in manufacturing, driven by the fluctuations in energy costs and the growing importance given to environmental impact of manufactured goods. Lots of attention has been given to the reduction of machine tools energy consumption, as they require large amounts of energy to perform manufacturing tasks.One area that has received relatively little interest, yet could harness great potential, is reducing energy consumption by planning machine activities between manufacturing operations, while the machine is not in use. The intuitive option --which is currently exploited in manufacturing-- is to leave the machine in a normal operating state in anticipation of the next manufacturing job. However, this is far from optimal due to the thermal deformation phenomenon, which usually require an energy-intensive warm-up cycle in order to bring all the components (e.g. spindle motor) into a suitable (stable) state for actual machining. Evidently, the use of this strategy comes with the associated commercial and environmental repercussions.In this paper, we investigate the exploitability of automated planning techniques for planning machine activities between manufacturing operations. We present a PDDL 2.2 formulation of the task that considers energy consumption, thermal deformation, and accuracy. We then demonstrate the effectiveness of the proposed approach using a case study which considers real-world data.
UR - https://www.aaai.org/Press/Proceedings/icaps17.php
M3 - Conference contribution
BT - 27th International Conference on Automated Planning and Scheduling
A2 - Barbulescu, Laura
A2 - Frank, Jeremy
A2 - , Mausam
A2 - Smith, Stephen F.
PB - Association for the Advancement of Artificial Intelligence
ER -