The use of intelligent systems and innovation to model and optimize fused deposition modeling process parameters for time manufacturing and material consumption | [Journal of Theoretical and Applied Information Technology • 2020]

Author(s):

1. H. ISKSIOUI: M2SM, Research Center STIS, Dep. of Mechanical Engineering, ENSET, Mohammed V University, Rabat, Morocco

2. S. ENNIMA: M2SM, Research Center STIS, Dep. of Mechanical Engineering, ENSET, Mohammed V University, Rabat, Morocco

3. S. BOUREKKADI: PCMT, Research Center STIS, Dep. of mechanical engineering, ENSET, Mohammed V University, Rabat, Morocco

4. M. OUBREK: National Engineering School for Mechanics and Aerotechnics (ENSMA), University of Poitiers France. L2IISR, ARSED Ibn Tofail University, Kenitra Morocco

5. A. ELGHARAD: M2SM, Research Center STIS, Dep. of Mechanical Engineering, ENSET, Mohammed V University, Rabat, Morocco

Abstract:

Additive manufacturing processes, especially the FDM process (Fused Deposition Modeling), are used for prototyping or manufacturing complex geometries. The ease of using 3D scanners and printers made FDM process a must have in every tech-house or laboratory. To optimize it, the prototyping cost, the manufacturing time and the material consumption must be reduced. Thus, the process parameters that intervene in the quantity of material and the manufacturing time (platform and extruder temperature, layer thickness, number of shells and solid layers, infill pattern and density, print speed) have been analyzed. An experimental study using a statistical analysis and an optimal experimental plan Design-optimal have been made. In addition, a mathematical model adapted to the experimental results has been designed. The RSM (response surface method) has been used to optimize the model response and find the most suitable set of process parameters. Those inputs have been validated with the developed mathematical model.

Page(s): 2776-2786

DOI: DOI not available

Published: Journal: Journal of Theoretical and Applied Information Technology, Volume: 98, Issue: 14, Year: 2020

Keywords:

Fused Deposition Modeling , Additive Manufacturing

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