OPTIMIZATION OF ANNEALING AND 3D PRINTING PROCESS PARAMETERS OF PLA PARTS

Fused Filament Fabrication (FFF) has gained significant popularity as the prevalent additive manufacturing method due to its ability to reduce production time and expenses. However, the constraints of limited dimensional precision, poor surface quality, and relatively low Ultimate Tensile Strength (UTS) hinder compliance with the stringent regulatory norms of conventional manufacturing, necessitating post-processing for enhancement. In this investigation, the response surface method was used to optimize annealing and specific printing parameters to enhance the quality of PLA parts produced by FFF. Tensile specimens were printed with varying production parameters and annealed at varying heat treatment parameters. The following parameters are specified: layer height (0.1, 0.2, and 0.3 mm), build orientation (0°, 22.5°, 45°, 67.5°, and 90°), annealing temperature (70, 90, 110, and 130 ºC), and annealing time (60, 120, 180, and 240 min). The optimization technique aimed to enhance the UTS and match the CAD dimensions while minimizing surface roughness. The RSM optimization analysis identified the optimal parameters as layer height of 0.1 mm, build orientation at 0 degrees, annealing temperature of 110 degrees, and annealing time of 180 min. The consistent achievement of high levels of agreement between estimated and experimental response values substantiates the proposed models. A composite desirability value of 0.80 was derived for the variables due to the optimization investigation.

Authors

Institutions

Publication Details

Journal
International Journal of 3D Printing Technologies and Digital Industry
Published
2024-08-26
DOI
https://doi.org/10.46519/ij3dptdi.1451666
Citations
7
Primary Topic
Additive Manufacturing and 3D Printing Technologies
Type
article
Field-Weighted Citation Impact
1.42

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

OPTIMIZATION OF ANNEALING AND 3D PRINTING PROCESS PARAMETERS OF PLA PARTS

Abdurrahim Temiz, Mhd Usama Alabd
7 citations
International Journal of 3D Printing Technologies and Digital Industry
Additive Manufacturing and 3D Printing Technologies
1.42
article

OPTIMIZATION OF ANNEALING AND 3D PRINTING PROCESS PARAMETERS OF PLA PARTS

Abdurrahim Temiz, Mhd Usama Alabd
article en
7 citations

Abstract

Fused Filament Fabrication (FFF) has gained significant popularity as the prevalent additive manufacturing method due to its ability to reduce production time and expenses. However, the constraints of limited dimensional precision, poor surface quality, and relatively low Ultimate Tensile Strength (UTS) hinder compliance with the stringent regulatory norms of conventional manufacturing, necessitating post-processing for enhancement. In this investigation, the response surface method was used to optimize annealing and specific printing parameters to enhance the quality of PLA parts produced by FFF. Tensile specimens were printed with varying production parameters and annealed at varying heat treatment parameters. The following parameters are specified: layer height (0.1, 0.2, and 0.3 mm), build orientation (0°, 22.5°, 45°, 67.5°, and 90°), annealing temperature (70, 90, 110, and 130 ºC), and annealing time (60, 120, 180, and 240 min). The optimization technique aimed to enhance the UTS and match the CAD dimensions while minimizing surface roughness. The RSM optimization analysis identified the optimal parameters as layer height of 0.1 mm, build orientation at 0 degrees, annealing temperature of 110 degrees, and annealing time of 180 min. The consistent achievement of high levels of agreement between estimated and experimental response values substantiates the proposed models. A composite desirability value of 0.80 was derived for the variables due to the optimization investigation.

International Journal of 3D Printing Technologies and Digital IndustryVol. 8(2)
Karabük University (TR)
Karabük Üniversitesi
Openalex Percentile: Top 19%
Additive Manufacturing and 3D Printing Technologies
1.42
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.