Bead Geometry Prediction in Laser-Wire Additive Manufacturing Process Using Machine Learning: Case of Study DOI Creative Commons
Natago Guilé Mbodj, Mohammad Abuabiah, Peter Plapper

et al.

Applied Sciences, Journal Year: 2021, Volume and Issue: 11(24), P. 11949 - 11949

Published: Dec. 15, 2021

In Laser Wire Additive Manufacturing (LWAM), the final geometry is produced using layer-by-layer deposition (beads principle). To achieve good geometrical accuracy in product, proper implementation of bead essential. For this reason, paper focuses on process and proposes a layer (width height) prediction model to improve accuracy. More specifically, machine learning regression algorithm applied several experimental data predict across layers. Furthermore, neural network-based approach was used study influence different parameters, namely laser power, wire-feed rate travel speed geometry. validate effectiveness proposed approach, test split validation strategy train models. The results show particular evolutionary trend confirm that parameters have direct geometry, so, too, part. Several been found obtain an accurate with low errors deposition. Finally, indicates can efficiently be could help later designing controller LWAM process.

Language: Английский

3D/4D Printing of Polymers: Fused Deposition Modelling (FDM), Selective Laser Sintering (SLS), and Stereolithography (SLA) DOI Open Access
Abishek Kafle,

Eric Luis,

Raman Silwal

et al.

Polymers, Journal Year: 2021, Volume and Issue: 13(18), P. 3101 - 3101

Published: Sept. 15, 2021

Additive manufacturing (AM) or 3D printing is a digital process and offers virtually limitless opportunities to develop structures/objects by tailoring material composition, processing conditions, geometry technically at every point in an object. In this review, we present three different early adopted, however, widely used, polymer-based processes; fused deposition modelling (FDM), selective laser sintering (SLS), stereolithography (SLA) create polymeric parts. The main aim of review offer comparative overview correlating polymer material-process-properties for techniques. Moreover, the advanced material-process requirements towards 4D via these print methods taking example magneto-active polymers covered. Overall, highlights aspects serves as guide select suitable technique targeted material-based applications also discusses implementation practices systems with current state-of-the-art approach.

Language: Английский

Citations

355
Additively manufactured fiber-reinforced composites: A review of mechanical behavior and opportunities DOI
Jiahui Li, Yvonne Durandet, Xiaodong Huang

et al.

Journal of Material Science and Technology, Journal Year: 2022, Volume and Issue: 119, P. 219 - 244

Published: March 2, 2022

Language: Английский

Citations

81
3D printing in upcycling plastic and biomass waste to sustainable polymer blends and composites: A review DOI Creative Commons
Malik Hassan, Amar K. Mohanty, Manjusri Misra

et al.

Materials & Design, Journal Year: 2023, Volume and Issue: 237, P. 112558 - 112558

Published: Dec. 13, 2023

Mishandling of waste plastics and biomasses is a major global concern. Every year, around 380 million tons plastic are produced, with only 9% being recycled, leading to widespread pollution. Similarly, biomass generation from agricultural forestry sectors accounts for 140 billion metric tons, in addition 2.01 municipal solid waste. This review paper addresses the gap regarding integration 3D printing, upcycling recycled plastics, utilization sustainable composites. printed parts have shown comparable mechanical properties compared virgin materials, which been further improved by biomass-derived fillers. The acknowledges that different printing parameters substantial influence on strength, ductility, crystallinity, dimensional accuracy parts. Therefore, optimizing these becomes crucial achieving performance. Moreover, incorporating reinforcing agents, stabilizers, chain extenders, compatibilizers, surface modifiers recycling presents an excellent opportunity enhance properties, thermal stability, adhesion, stability. Additionally, identifies research gaps proposes machine learning artificial intelligence enhanced process control material development, expanding possibilities this field.

Language: Английский

Citations

57
3D printing with a 3D printed digital material filament for programming functional gradients DOI Creative Commons
Sang‐Joon Ahn, Howon Lee, Kyu‐Jin Cho

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: May 7, 2024

Additive manufacturing, or 3D printing attracts growing attention as a promising method for creating functionally graded materials. Fused deposition modeling (FDM) is widely available, but due to its simple process, spatial gradation of diverse properties using FDM challenging. Here, we present printed digital material filament that structured towards functional gradients, utilizing only readily available printer and filaments. The DM consists multiple base materials combined with specific concentrations distributions, which are printed. When the supplied same printer, constituent homogeneously blended during extrusion, resulting in desired final structure. This enables programming extreme variations, including mechanical strength, electrical conductivity, color, otherwise impossible achieve traditional FDMs. Our approach can be adopted any standard enabling low-cost production gradients.

Language: Английский

Citations

21
Fused Filament Fabrication Process: A Review of Numerical Simulation Techniques DOI Open Access
Ans Al Rashid, Muammer Koç‬

Polymers, Journal Year: 2021, Volume and Issue: 13(20), P. 3534 - 3534

Published: Oct. 14, 2021

Three-dimensional printing (3DP), also known as additive manufacturing (AM), has rapidly evolved over the past few decades. Researchers around globe have been putting their efforts into AM processes improvement and materials development. One of most widely used extrusion-based technology under is Fused Deposition Modeling (FDM), Filament Fabrication (FFF). Numerical simulation tools are being employed to predict FFF process complexities material behavior. These allow exploring candidate for potential use in improvements. The prime objective this study provide a comprehensive review state-of-the-art scientific achievements numerical simulations polymers composites. first section presents an in-depth discussion process's physical phenomena highlights multi-level complexity. subsequent discusses research efforts, specifically on techniques reported literature process. Finally, conclusions drawn based reviewed literature, future directions identified.

Language: Английский

Citations

72
The effect of six key process control parameters on the surface roughness, dimensional accuracy, and porosity in material extrusion 3D printing of polylactic acid: Prediction models and optimization supported by robust design analysis DOI Creative Commons
Nectarios Vidakis, Constantine David, Markos Petousis

et al.

Advances in Industrial and Manufacturing Engineering, Journal Year: 2022, Volume and Issue: 5, P. 100104 - 100104

Published: Nov. 1, 2022

In the material extrusion (MEX) Additive Manufacturing (AM) technology, layer-by-layer nature of fabricated parts, induces specific features which affect their quality and may restrict operating performance. Critical indicators with distinct technological industrial impact are surface roughness, dimensional accuracy, porosity, among others. Their achieving scores can be optimized by adjusting 3D printing process parameters. The effect six (6) control parameters, i.e., raster deposition angle, infill density, nozzle temperature, bed speed, layer thickness, on aforementioned is investigated herein. Optical Microscopy, Profilometry, Micro Χ-Ray Computed Tomography were employed to investigate document these characteristics. Experimental data processed Robust Design Theory. An L25 Taguchi orthogonal array (twenty-five runs) was compiled, for parameters five levels each one them. predictive quadratic regression models then validated two additional confirmation runs, replicas each. For first time, features, as well geometrical structural characteristics in such depth (>500 GB raw experimental produced processed). A deep insight into MEX printed parts provided allowing parameters' ranking optimization. Prediction equations functions introduced herein, merit market-driven practice.

Language: Английский

Citations

66
Optimization of key quality indicators in material extrusion 3D printing of acrylonitrile butadiene styrene: The impact of critical process control parameters on the surface roughness, dimensional accuracy, and porosity DOI
Nectarios Vidakis, Constantine David, Markos Petousis

et al.

Materials Today Communications, Journal Year: 2022, Volume and Issue: 34, P. 105171 - 105171

Published: Dec. 17, 2022

Language: Английский

Citations

61
Tensile Strength Enhancement of Fused Filament Fabrication Printed Parts: A Review of Process Improvement Approaches and Respective Impact DOI
Thang Q. Tran, Feng Lin Ng,

Justin Tan Yu Kai

et al.

Additive manufacturing, Journal Year: 2022, Volume and Issue: 54, P. 102724 - 102724

Published: March 8, 2022

Language: Английский

Citations

59
A digital twin ecosystem for additive manufacturing using a real-time development platform DOI Open Access
Minas Pantelidakis, Konstantinos Mykoniatis, Jia Liu

et al.

The International Journal of Advanced Manufacturing Technology, Journal Year: 2022, Volume and Issue: 120(9-10), P. 6547 - 6563

Published: April 13, 2022

Language: Английский

Citations

49
The use of machine learning in process–structure–property modeling for material extrusion additive manufacturing: a state-of-the-art review DOI

Ziadia Abdelhamid,

Mohamed Habibi, Sousso Kélouwani

et al.

Journal of the Brazilian Society of Mechanical Sciences and Engineering, Journal Year: 2024, Volume and Issue: 46(2)

Published: Jan. 11, 2024

Language: Английский

Citations

14