Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.
A modified genetic algorithm for time and cost optimization of an additive manufacturing single-machine scheduling
FRUGGIERO, FABIO;Lambiase, A.
;
2018-01-01
Abstract
Additive Manufacturing (AM) is a process of joining materials to make objects from 3D model data, usually layer by layer, as opposed to subtractive manufacturing methodologies. Selective Laser Melting, commercially known as Direct Metal Laser Sintering (DMLS®), is the most diffused additive process in today’s manufacturing industry. Introduction of a DMLS® machine in a production department has remarkable effects not only on industrial design but also on production planning, for example, on machine scheduling. Scheduling for a traditional single machine can employ consolidated models. Scheduling of an AM machine presents new issues because it must consider the capability of producing different geometries, simultaneously. The aim of this paper is to provide a mathematical model for an AM/SLM machine scheduling. The complexity of the model is NP-HARD, so possible solutions must be found by metaheuristic algorithms, e.g., Genetic Algorithms. Genetic Algorithms solve sequential optimization problems by handling vectors; in the present paper, we must modify them to handle a matrix. The effectiveness of the proposed algorithms will be tested on a test case formed by a 30 Part Number production plan with a high variability in complexity, distinct due dates and low production volumes.File | Dimensione | Formato | |
---|---|---|---|
Lambiase_Alfredo_3_422_AMOdified.pdf
non disponibili
Tipologia:
Versione editoriale (versione pubblicata con il layout dell'editore)
Licenza:
NON PUBBLICO - Accesso privato/ristretto
Dimensione
521.53 kB
Formato
Adobe PDF
|
521.53 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
422 Lambiase Pre-print.pdf
accesso aperto
Descrizione: 2018 Growing Science Ltd. doi: 10.5267/j.ijiec.2018.1.001. Link editore: http://growingscience.com/beta/ijiec/2802-a-modified-genetic-algorithm-for-time-and-cost-optimization-of-an-additive-manufacturing-single-machine-scheduling.html
Tipologia:
Documento in Pre-print (manoscritto inviato all'editore, precedente alla peer review)
Licenza:
Creative commons
Dimensione
658.04 kB
Formato
Adobe PDF
|
658.04 kB | Adobe PDF | Visualizza/Apri |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.