Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives

Wang, Yuanbin; Zheng, Pai; Peng, Tao; Yang, Huayong; Zou, Jun

doi:10.1007/s11431-020-1581-2

Cited by 58 publications

(21 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[14,21,[24][25][26]47,[63][64][65] • 3D printing with the design for manufacturing, including bioinspired structures, simulation and modeling, artificial intelligence, data science, and the corresponding manufac turing avenues. [20,[66][67][68][69] However, there has not been an indepth and systematic review among these reviews regarding AMenabled particle alignment based on WebofKnowledge literature research. The purpose of this review is to summarize currently available strategies for particle assembly and discuss future perspec tives.…”

Section: Overview Of 3d Printingmentioning

confidence: 99%

3D Printing‐Enabled Nanoparticle Alignment: A Review of Mechanisms and Applications

Jambhulkar

Ravichandran

et al. 2021

Small

View full text Add to dashboard Cite

3D printing (additive manufacturing (AM)) has enormous potential for rapid tooling and mass production due to its design flexibility and significant reduction of the timeline from design to manufacturing. The current state-of-the-art in 3D printing focuses on material manufacturability and engineering applications. However, there still exists the bottleneck of low printing resolution and processing rates, especially when nanomaterials need tailorable orders at different scales. An interesting phenomenon is the preferential alignment of nanoparticles that enhance material properties. Therefore, this review emphasizes the landscape of nanoparticle alignment in the context of 3D printing. Herein, a brief overview of 3D printing is provided, followed by a comprehensive summary of the 3D printing-enabled nanoparticle alignment in wellestablished and in-house customized 3D printing mechanisms that can lead to selective deposition and preferential orientation of nanoparticles. Subsequently, it is listed that typical applications that utilized the properties of ordered nanoparticles (e.g., structural composites, heat conductors, chemo-resistive sensors, engineered surfaces, tissue scaffolds, and actuators based on structural and functional property improvement). This review's emphasis is on the particle alignment methodology and the performance of composites incorporating aligned nanoparticles. In the end, significant limitations of current 3D printing techniques are identified together with future perspectives.

show abstract

Section: Overview Of 3d Printingmentioning

confidence: 99%

3D Printing‐Enabled Nanoparticle Alignment: A Review of Mechanisms and Applications

Jambhulkar

Ravichandran

et al. 2021

Small

View full text Add to dashboard Cite

show abstract

“…Recently, advanced learning algorithms have been introduced to model pavement engineering applications [18][19][20][21][22]. Fakhri and Shahni Dezfoulian [23] provide a satisfactory correlation between international roughness index (IRI), pavement surface evaluation and rating index (PASER), and structural indices based on deflection measurements by ANN and regression models.…”

Section: Related Workmentioning

confidence: 99%

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Shi

Cao

et al. 2022

Sci. China Technol. Sci.

View full text Add to dashboard Cite

The use of non-destructive testing (NDT) equipment, such as the falling weight deflectometer (FWD), provides important estimates of road health and helps to optimize road management regimes. However, periodic road testing and post-processing of the collected data are cumbersome and require much expertise, a considerable amount of time, money, and other resources. This study attempts to develop a reliable prediction method for estimating the deflection basin area of different asphalt pavements using road temperature, load time, and load pressure as main characteristics. The data are obtained from 19 kinds of asphalt pavements on a 2.038-km-long full-scale field accelerated pavement testing track named RIOHTrack (Research Institute of Highway Track) in Tongzhou, Beijing. In addition, a chaotic particle swarm algorithm (CPSO) and a segmented regression strategy are proposed in this paper to optimize the XGBoost model. The experiment results of the proposed method are compared with those of classical machine learning algorithms and achieve an average of mean square error and mean absolute error respectively by 5.80 and 1.59. The experiments demonstrate the superiority of the XGBoost algorithm over classical machine learning methods in dealing with nonlinear problems in road engineering. Significantly, the method can reduce the frequency of deflection tests without affecting its estimation accuracy, which is a promising alternative way to facilitate the rapid assessment of pavement conditions.

show abstract

“…The 3D printing technology can quickly address the deficiencies of medical materials and spare parts of medical equipment, however, the processing time, high cost, and lack of manpower can be potential barriers for applying 3D printing on a larger scale ( Ishack & Lipner, 2020 ). AI techniques can play a role in optimizing the 3D design process and reducing the cost of printing ( Wang et al, 2020 ; Longhitano, Nunes & Candido, 2021 ; Almalki & Azeez, 2020 ).…”

Section: Future Research Directionsmentioning

confidence: 99%

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

Kumar

Singh

Kaur

et al. 2021

PeerJ Computer Science

View full text Add to dashboard Cite

Background Until now, there are still a limited number of resources available to predict and diagnose COVID-19 disease. The design of novel drug-drug interaction for COVID-19 patients is an open area of research. Also, the development of the COVID-19 rapid testing kits is still a challenging task. Methodology This review focuses on two prime challenges caused by urgent needs to effectively address the challenges of the COVID-19 pandemic, i.e., the development of COVID-19 classification tools and drug discovery models for COVID-19 infected patients with the help of artificial intelligence (AI) based techniques such as machine learning and deep learning models. Results In this paper, various AI-based techniques are studied and evaluated by the means of applying these techniques for the prediction and diagnosis of COVID-19 disease. This study provides recommendations for future research and facilitates knowledge collection and formation on the application of the AI techniques for dealing with the COVID-19 epidemic and its consequences. Conclusions The AI techniques can be an effective tool to tackle the epidemic caused by COVID-19. These may be utilized in four main fields such as prediction, diagnosis, drug design, and analyzing social implications for COVID-19 infected patients.

show abstract

Smart additive manufacturing: Current artificial intelligence-enabled methods and future perspectives

Cited by 58 publications

References 99 publications

3D Printing‐Enabled Nanoparticle Alignment: A Review of Mechanisms and Applications

3D Printing‐Enabled Nanoparticle Alignment: A Review of Mechanisms and Applications

CPSO-XGBoost segmented regression model for asphalt pavement deflection basin area prediction

Overview of current state of research on the application of artificial intelligence techniques for COVID-19

Contact Info

Product

Resources

About