Analysis and Optimization of Warpage Deformation in 3D Printing Training Teaching -- Taking Jilin University Engineering Training Center as an example

Geng, Dongni; Zhao, Jun

doi:10.2991/erss-18.2019.165

Cited by 4 publications

(3 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The other important parameter is the ambient temperature which causes part warpage. For example, PLA part warpage of about 50%, 30%, and 10% at 10 °C, 15 °C, and 20 °C (ambient print environment temperature) respectively, is reported [ 100 ]. The process parameters affecting part properties in FDM are raster angle, extrusion width, extrusion rate, bed temperature, nozzle temperature, and nozzle speed.…”

Section: Printing Processmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

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

show abstract

Section: Printing Processmentioning

confidence: 99%

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

et al. 2021

View full text Add to dashboard Cite

show abstract

“…H. Gunaydin et al have stated the different errors that occurs, such as clogging of the nozzle, adhesion problem, vibration or shocks, misalignment of the print platform, etc., which causes loss of material, time, and money [8]. D. Geng and J. Zhao stated the severity of warpage problems, which are caused due to improper cooling [5]. Apart from machine errors, printing components are subjected to errors due to structural and material shortcomings such as porosity, cracking, residual stresses, etc.…”

Section: Literature Reviewmentioning

confidence: 99%

“…These errors are mainly due to carelessness of the operator and are not part of the research. Warping [5] Improper cooling of the printed component or due to materials used in the process…”

mentioning

confidence: 99%

Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

Kadam

Kumar

Bongale

et al. 2021

ASI

View full text Add to dashboard Cite

In the era of Industry 4.0, the idea of 3D printed products has gained momentum and is also proving to be beneficial in terms of financial and time efforts. These products are physically built layer-by-layer based on the digital Computer Aided Design (CAD) inputs. Nonetheless, 3D printed products are still subjected to defects due to variation in properties and structure, which leads to deterioration in the quality of printed products. Detection of these errors at each layer level of the product is of prime importance. This paper provides the methodology for layer-wise anomaly detection using an ensemble of machine learning algorithms and pre-trained models. The proposed combination is trained offline and implemented online for fault detection. The current work provides an experimental comparative study of different pre-trained models with machine learning algorithms for monitoring and fault detection in Fused Deposition Modelling (FDM). The results showed that the combination of the Alexnet and SVM algorithm has given the maximum accuracy. The proposed fault detection approach has low experimental and computing costs, which can easily be implemented for real-time fault detection.

show abstract

Identifying structural defects in 3D printed material using image processing through machine learning

Palakurthi,

Jakkampudi,

Rakkiannan

et al. 2023

SECOND INTERNATIONAL VIRTUAL CONFERENCE ON INTELLIGENT ROBOTICS, MECHATRONICS AND AUTOMATION SYSTEMS (IRMAS2022): Theme: Innova

View full text Add to dashboard Cite

Analysis and Optimization of Warpage Deformation in 3D Printing Training Teaching -- Taking Jilin University Engineering Training Center as an example

Cited by 4 publications

References 1 publication

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

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

Enhancing Surface Fault Detection Using Machine Learning for 3D Printed Products

Identifying structural defects in 3D printed material using image processing through machine learning

Contact Info

Product

Resources

About