3D Printing and Additive Manufacturing Technologies

Kumar, L. Jyothish; Pandey, Pulak M.; Wimpenny, D. I.

doi:10.1007/978-981-13-0305-0

Cited by 95 publications

(12 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The unique capabilities of the additive manufacturing (AM) process such as complexityfree, customization and functionally gradient part properties, offers a broad application of AM in industries [1]. Specifically, AM is advantageous for developing complex turbine blades, customized bio-implants, and internal cooling channels [2]. As the AM is growing in the industry, process establishment for new materials with better processibility, functionality, and desired mechanical properties are challenging task that needs to be explored.…”

Section: Introductionmentioning

confidence: 99%

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

et al. 2022

View full text Add to dashboard Cite

Machine learning (ML) is one of the artificial intelligence tools which uses past data to learn the relationship between input and output and helps to predict future trends. Powder bed fusion additive manufacturing (PBF-AM) is extensively used for a wide range of applications in the industry. The AM process establishment for new material is a crucial task with trial-and-error approaches. In this work, ML techniques have been applied for the prediction of the density of PBF-AM. Density is the most vital property in evaluating the overall quality of the AM building part. The ML techniques, namely, artificial neural network (ANN), K-nearest neighbor (KNN), support vector machine (SVM), and linear regression (LR), are used to develop a model for predicting the density of the stainless steel (SS) 316L build part. These four methods are validated using R-squared values and different error functions to compare the predicted result. The ANN and SVM model performed well with the R-square value of 0.95 and 0.923, respectively, for the density prediction. The ML models would be beneficial for the prediction of the process parameters. Further, the developed ML model would also be helpful for the future application of ML in additive manufacturing.

show abstract

Section: Introductionmentioning

confidence: 99%

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Additive manufacturing (AM), also known as 3D printing, comprises a set of technologies that makes the manufacturing of parts directly from a digital model possible, generally in a layer-by-layer approach [ 1 , 2 , 3 ]. Among others, the polymeric material extrusion (PME) technologies are the most implemented in industrial sectors due to their cost efficient and easily scalable alternatives.…”

Section: Introductionmentioning

confidence: 99%

Basalt Fiber Composites with Reduced Thermal Expansion for Additive Manufacturing

et al. 2022

View full text Add to dashboard Cite

Fused filament fabrication (FFF) is gaining attention as an efficient way to create parts and replacements on demand using thermoplastics. This technology requires the development of new materials with a reliable printability that satisfies the requirement of final parts. In this context, a series of composites based on acrylonitrile styrene acrylate (ASA) reinforced with basalt fiber (BF) are reported in this work. First, several surface modification treatments are applied onto the BF to increase their compatibility with the ASA matrix. Then, once the best treatment is identified, the mechanical properties, coefficient of thermal expansion (CTE) and warping distortion of the different specimens designed and prepared by FFF are studied. It was found that the silanized BF is appropriate for an adequate printing, obtaining composites with higher stiffness, tensile strength, low CTE and a significant reduction in part distortion. These composites are of potential interest in the design and manufacturing of final products by FFF, as they show much lower CTE values than pure ASA, which is essential to successfully fabricate large objects using this technique.

show abstract

“…For studying the impact of wide range of ID on the mechanical performance of fabricated structures, ID has been varied from 20% to 100% in the current study. The previously reported attempts (Chacón et al , 2017; Khatwani and Srivastava, 2018; Rajpurohit and Dave, 2019; Sood et al , 2010; Tao et al , 2020; Wang et al , 2017) have focused on LH varying from 0.1 to 0.4 mm, with higher values of LH showing inverse impact on mechanical strength of the FDM parts. Depending on the values offered by the FDM-based 3D printer, LH has differed from 0.1 to 0.5 mm for the present work.…”

Section: Materials and Methodologymentioning

confidence: 99%

Machine learning for forecasting the biomechanical behavior of orthopedic bone plates fabricated by fused deposition modeling

Sharma,

Gupta,

Mudgal

et al. 2024

RPJ

View full text Add to dashboard Cite

Purpose Three-dimensional (3D) printing is highly dependent on printing process parameters for achieving high mechanical strength. It is a time-consuming and expensive operation to experiment with different printing settings. The current study aims to propose a regression-based machine learning model to predict the mechanical behavior of ulna bone plates. Design/methodology/approach The bone plates were formed using fused deposition modeling (FDM) technique, with printing attributes being varied. The machine learning models such as linear regression, AdaBoost regression, gradient boosting regression (GBR), random forest, decision trees and k-nearest neighbors were trained for predicting tensile strength and flexural strength. Model performance was assessed using root mean square error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). Findings Traditional experimentation with various settings is both time-consuming and expensive, emphasizing the need for alternative approaches. Among the models tested, GBR model demonstrated the best performance in predicting both tensile and flexural strength and achieved the lowest RMSE, highest R2 and lowest MAE, which are 1.4778 ± 0.4336 MPa, 0.9213 ± 0.0589 and 1.2555 ± 0.3799 MPa, respectively, and 3.0337 ± 0.3725 MPa, 0.9269 ± 0.0293 and 2.3815 ± 0.2915 MPa, respectively. The findings open up opportunities for doctors and surgeons to use GBR as a reliable tool for fabricating patient-specific bone plates, without the need for extensive trial experiments. Research limitations/implications The current study is limited to the usage of a few models. Other machine learning-based models can be used for prediction-based study. Originality/value This study uses machine learning to predict the mechanical properties of FDM-based distal ulna bone plate, replacing traditional design of experiments methods with machine learning to streamline the production of orthopedic implants. It helps medical professionals, such as physicians and surgeons, make informed decisions when fabricating customized bone plates for their patients while reducing the need for time-consuming experimentation, thereby addressing a common limitation of 3D printing medical implants.

show abstract

3D Printing and Additive Manufacturing Technologies

Cited by 95 publications

References 0 publications

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

Density Prediction in Powder Bed Fusion Additive Manufacturing: Machine Learning-Based Techniques

Basalt Fiber Composites with Reduced Thermal Expansion for Additive Manufacturing

Machine learning for forecasting the biomechanical behavior of orthopedic bone plates fabricated by fused deposition modeling

Contact Info

Product

Resources

About