Planning and Scheduling in Additive Manufacturing
Recent advances in additive manufacturing (AM) and 3D printing technologies have led to significant growth in the use of additive manufacturing in industry, which allows for the physical realization of previously difficult to manufacture designs. However, in certain cases AM can also involve higher...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Asociación Española para la Inteligencia Artificial
2018-09-01
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| Series: | Inteligencia Artificial |
| Online Access: | https://journal.iberamia.org/index.php/intartif/article/view/218 |
| _version_ | 1831601904766943232 |
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| author | Filip Dvorak Maxwell Micali Mathias Mathieug |
| author_facet | Filip Dvorak Maxwell Micali Mathias Mathieug |
| author_sort | Filip Dvorak |
| collection | DOAJ |
| description | Recent advances in additive manufacturing (AM) and 3D printing technologies have led to significant growth in the use of additive manufacturing in industry, which allows for the physical realization of previously difficult to manufacture designs. However, in certain cases AM can also involve higher production costs and unique in-process physical complications, motivating the need to solve new optimization challenges. Optimization for additive manufacturing is relevant for and involves multiple fields including mechanical engineering, materials science, operations research, and production engineering, and interdisciplinary interactions must be accounted for in the optimization framework.
In this paper we investigate a problem in which a set of parts with unique configurations and deadlines must be printed by a set of machines while minimizing time and satisfying deadlines, bringing together bin packing, nesting (two-dimensional bin packing), job shop scheduling, and constraints satisfaction. We first describe the real-world industrial motivation for solving the problem. Subsequently, we encapsulate the problem within constraints and graph theory, create a formal model of the problem, discuss nesting as a subproblem, and describe the search algorithm. Finally, we present the datasets, the experimental approach, and the preliminary results. |
| first_indexed | 2024-12-18T15:10:31Z |
| format | Article |
| id | doaj.art-d6af0b88b85c4deeb61b5c6b8de731b2 |
| institution | Directory Open Access Journal |
| issn | 1137-3601 1988-3064 |
| language | English |
| last_indexed | 2024-12-18T15:10:31Z |
| publishDate | 2018-09-01 |
| publisher | Asociación Española para la Inteligencia Artificial |
| record_format | Article |
| series | Inteligencia Artificial |
| spelling | doaj.art-d6af0b88b85c4deeb61b5c6b8de731b22022-12-21T21:03:40ZengAsociación Española para la Inteligencia ArtificialInteligencia Artificial1137-36011988-30642018-09-01216210.4114/intartif.vol21iss62pp40-52218Planning and Scheduling in Additive ManufacturingFilip Dvorak0Maxwell MicaliMathias MathieugOqtonRecent advances in additive manufacturing (AM) and 3D printing technologies have led to significant growth in the use of additive manufacturing in industry, which allows for the physical realization of previously difficult to manufacture designs. However, in certain cases AM can also involve higher production costs and unique in-process physical complications, motivating the need to solve new optimization challenges. Optimization for additive manufacturing is relevant for and involves multiple fields including mechanical engineering, materials science, operations research, and production engineering, and interdisciplinary interactions must be accounted for in the optimization framework. In this paper we investigate a problem in which a set of parts with unique configurations and deadlines must be printed by a set of machines while minimizing time and satisfying deadlines, bringing together bin packing, nesting (two-dimensional bin packing), job shop scheduling, and constraints satisfaction. We first describe the real-world industrial motivation for solving the problem. Subsequently, we encapsulate the problem within constraints and graph theory, create a formal model of the problem, discuss nesting as a subproblem, and describe the search algorithm. Finally, we present the datasets, the experimental approach, and the preliminary results.https://journal.iberamia.org/index.php/intartif/article/view/218 |
| spellingShingle | Filip Dvorak Maxwell Micali Mathias Mathieug Planning and Scheduling in Additive Manufacturing Inteligencia Artificial |
| title | Planning and Scheduling in Additive Manufacturing |
| title_full | Planning and Scheduling in Additive Manufacturing |
| title_fullStr | Planning and Scheduling in Additive Manufacturing |
| title_full_unstemmed | Planning and Scheduling in Additive Manufacturing |
| title_short | Planning and Scheduling in Additive Manufacturing |
| title_sort | planning and scheduling in additive manufacturing |
| url | https://journal.iberamia.org/index.php/intartif/article/view/218 |
| work_keys_str_mv | AT filipdvorak planningandschedulinginadditivemanufacturing AT maxwellmicali planningandschedulinginadditivemanufacturing AT mathiasmathieug planningandschedulinginadditivemanufacturing |