scholarly journals Complex Discrete System Analysis of Process Design and Tourist Souvenir Making Based on Artificial Intelligence 3D Printing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuchuan Guo
Keyword(s):  
Artificial Intelligence ◽  
3D Printing ◽  
System Analysis ◽  
Focal Length ◽  
Rapid Development ◽  
Three Dimensional ◽  
Three Dimensional Model ◽  
Printing Technology ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

With the rapid development of 3D (three-dimensional) printing technology, it has been widely used in the field of ceramic arts and crafts. However, due to the complexity of 3D printing technology, it will face complex modeling and calculation when designing ceramic art crafts. To this end, the artificial intelligence algorithm is introduced, and using the data measured by the built-in modeling instruction of LAMMPS of the artificial intelligence algorithm, the program is used to reset its coordinates, length, width, height, and focal length. The obtained data are modified by postprocessing to correct its coordinates and the size of the simulation frame, so that the nanopowder model is placed in the center, forming a solid ellipsoidal aluminum nanopowder and cutting it into a three-dimensional model of teapot, which is transformed into the STL file of two-dimensional cross section, and the finished product is printed out to the 3D printer. Finally, the RTM model is used to test the quality of tourist souvenirs. The results show that the homogeneity of variance is much greater than 0.10. It can be inferred that the tourist souvenirs of pottery teapots have met the requirements of national technological quality standards.

scholarly journals Traditional versus mirror three-dimensional printing technology for isolated acetabular fractures: a retrospective study with a median follow-up of 25 months

2021 ◽  
Vol 49 (6) ◽  
pp. 030006052110285
Author(s):  
Kai Xiao ◽  
Bo Xu ◽  
Lin Ding ◽  
Weiguang Yu ◽  
Lei Bao ◽  
...  
Keyword(s):  
3D Printing ◽  
Three Dimensional ◽  
Operation Time ◽  
Harris Hip Score ◽  
Acetabular Fractures ◽  
Printing Technology ◽  
Three Dimensional Printing ◽  
3D Printing Technology ◽  
Significant Difference

Objective To assess the outcomes of traditional three-dimensional (3D) printing technology (TPT) versus mirror 3D printing technology (MTT) in treating isolated acetabular fractures (IAFs). Methods Consecutive patients with an IAF treated by either TPT or MTT at our tertiary medical centre from 2012 to 2018 were retrospectively reviewed. Follow-up was performed 1, 3, 6, and 12 months postoperatively and annually thereafter. The primary outcome was the Harris hip score (HHS), and the secondary outcomes were major intraoperative variables and key orthopaedic complications. Results One hundred fourteen eligible patients (114 hips) with an IAF (TPT, n = 56; MTT, n = 58) were evaluated. The median follow-up was 25 months (range, 21–28 months). At the last follow-up, the mean HHS was 82.46 ±14.70 for TPT and 86.30 ± 13.26 for MTT with a statistically significant difference. Significant differences were also detected in the major intraoperative variables (operation time, intraoperative blood loss, number of fluoroscopic screenings, and anatomical reduction number) and the major orthopaedic complications (loosening, implant failure, and heterotopic ossification). Conclusion Compared with TPT, MTT tends to produce accurate IAF reduction and may result in better intraoperative variables and a lower rate of major orthopaedic complications.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

scholarly journals Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 14
Author(s):  
Mei Dong ◽  
Hongyu Wu ◽  
Hui Hu ◽  
Rafig Azzam ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Monitoring System ◽  
Slope Instability ◽  
Mountainous Area ◽  
Probabilistic Forecasting ◽  
Intelligence Algorithm ◽  
Good Prediction Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Steep Slopes

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

scholarly journals Porous PLAs with Controllable Density by FDM 3D Printing and Chemical Foaming Agent

Micromachines ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 866
Author(s):  
A. R. Damanpack ◽  
André Sousa ◽  
M. Bodaghi
Keyword(s):  
3D Printing ◽  
Flow Rate ◽  
Three Dimensional ◽  
Poly Lactic Acid ◽  
Layer By Layer ◽  
Printing Technology ◽  
Material Density ◽  
Foaming Agent ◽  
3D Printing Technology ◽  
Fabrication Parameters

This paper shows how fused decomposition modeling (FDM), as a three-dimensional (3D) printing technology, can engineer lightweight porous foams with controllable density. The tactic is based on the 3D printing of Poly Lactic Acid filaments with a chemical blowing agent, as well as experiments to explore how FDM parameters can control material density. Foam porosity is investigated in terms of fabrication parameters such as printing temperature and flow rate, which affect the size of bubbles produced during the layer-by-layer fabrication process. It is experimentally shown that printing temperature and flow rate have significant effects on the bubbles’ size, micro-scale material connections, stiffness and strength. An analytical equation is introduced to accurately simulate the experimental results on flow rate, density, and mechanical properties in terms of printing temperature. Due to the absence of a similar concept, mathematical model and results in the specialized literature, this paper is likely to advance the state-of-the-art lightweight foams with controllable porosity and density fabricated by FDM 3D printing technology.

3 D printing technology and experimental study of chitosan gel using atomized solidification liquid-assisted extrusion molding

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yangwei Wang ◽  
Peilun Lv ◽  
Jian Li ◽  
Liying Yu ◽  
Guodong Yuan ◽  
...  
Keyword(s):  
3D Printing ◽  
Three Dimensional ◽  
Theoretical Research ◽  
Error Sources ◽  
Printing Technology ◽  
Content Type ◽  
Printing Ink ◽  
Printing Parameters ◽  
Chitosan Gel ◽  
Printing Method

Purpose This paper aims to propose a suitable atomizing solidification chitosan (CS) gel liquid extrusion molding technology for the three dimensional (3D) printing method, and experiments verify the feasibility of this method. Design/methodology/approach This paper mainly uses experimental means, combined with theoretical research. The preparation method, solidification forming method and 3D printing method of CS gel solution were studied. The CS gel printing mechanism and printing error sources are analyzed on the basis of the CS gel ink printing results, printing performance with different ratios of components by constructing a gel print prototype, experiments evaluating the CS gel printing technology and the effects of the process parameters on the scaffold formation. Findings CS printing ink was prepared; the optimal formula was found; the 3 D printing experiment of CS was completed; the optimal printing parameters were obtained; and the reliability of the forming prototype, printing ink and gel printing process was verified, which allowed for the possibility to apply the 3 D printing technology to the manufacturing of a CS gel structure. Originality/value This study can provide theoretical and technical support for the potential application of CS 3 D printed gels in tissue engineering.

Sign in / Sign up

Export Citation Format

Share Document