scholarly journals Optimization of Extrusion-Based 3D Printing Process Using Neural Networks for Sustainable Development

Materials ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 2737
Author(s):  
Izabela Rojek ◽  
Dariusz Mikołajewski ◽  
Marek Macko ◽  
Zbigniew Szczepański ◽  
Ewa Dostatni
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Risk Assessment ◽  
Sustainable Development ◽  
3D Printing ◽  
Computational Intelligence ◽  
Learning Program ◽  
Printing Process ◽  
Printing Processes ◽  
Self Learning

Technological and material issues in 3D printing technologies should take into account sustainable development, use of materials, energy, emitted particles, and waste. The aim of this paper is to investigate whether the sustainability of 3D printing processes can be supported by computational intelligence (CI) and artificial intelligence (AI) based solutions. We present a new AI-based software to evaluate the amount of pollution generated by 3D printing systems. We input the values: printing technology, material, print weight, etc., and the expected results (risk assessment) and determine if and what precautions should be taken. The study uses a self-learning program that will improve as more data are entered. This program does not replace but complements previously used 3D printing metrics and software.

Influence of Constructive and Technological Parameters at Generated Spiral Parts with DLP 3D Printing Process

2019 ◽  
Vol 56 (4) ◽  
pp. 801-811
Author(s):  
Mircea Dorin Vasilescu
Keyword(s):  
3D Printing ◽  
Great Influence ◽  
Polymerization Process ◽  
Generation Process ◽  
Technological Parameters ◽  
Printing Process ◽  
The Third ◽  
Solid Models ◽  
Specific Factors ◽  
Printing Processes

This work are made for determine the possibility of generating the specific parts of a threaded assembly. If aspects of CAD generating specific elements was analysed over time in several works, the technological aspects of making components by printing processes 3D through optical polymerization process is less studied. Generating the threaded appeared as a necessity for the reconditioning technology or made components of the processing machines. To determine the technological aspects of 3D printing are arranged to achieve specific factors of the technological process, but also from the specific elements of a trapezoidal thread or spiral for translate granular material in supply process are determined experimentally. In the first part analyses the constructive generation process of a spiral element. In the second part are identified the specific aspects that can generation influence on the process of realization by 3D DLP printing of the two studied elements. The third part is affected to printing and determining the dimensions of the analysed components. We will determine the specific value that can influence the process of making them in rapport with printing process. The last part is affected by the conclusions. It can be noticed that both the orientation and the precision of generating solid models have a great influence on the made parts.

scholarly journals Synergistic application of continuous granulation and selective laser sintering 3D printing for the development of pharmaceutical dosage forms with enhanced dissolution rates and physical properties

2021 ◽  
Author(s):  
Rishi Thakkar ◽  
Yu Zhang ◽  
Jiaxiang Zhang ◽  
Mohammed Maniruzzaman
Keyword(s):  
Solid State ◽  
3D Printing ◽  
Physical Properties ◽  
Dosage Forms ◽  
Flow Properties ◽  
Printing Process ◽  
Powder Bed ◽  
Printing Processes ◽  
Binder Jetting ◽  
Physical Mixtures

AbstractThis study demonstrated the first case of combining novel continuous granulation with powder-based pharmaceutical 3-dimensional (3D) printing processes to enhance the dissolution rate and physical properties of a poorly water-soluble drug. Powder bed fusion (PBF) and binder jetting 3D printing processes have gained much attention in pharmaceutical dosage form manufacturing in recent times. Although powder bed-based 3D printing platforms have been known to face printing and uniformity problems due to the inherent poor flow properties of the pharmaceutical physical mixtures (feedstock). Moreover, techniques such as binder jetting currently do not provide any solubility benefits to active pharmaceutical ingredients (APIs) with poor aqueous solubility (>40% of marketed drugs). For this study, a hot-melt extrusion-based versatile granulation process equipped with UV-Vis process analytical technology (PAT) tools for the in-line monitoring of critical quality attributes (i.e., solid-state) of indomethacin was developed. The collected granules with enhanced flow properties were mixed with vinylpyrrolidone-vinyl acetate copolymer and a conductive excipient for efficient sintering. These mixtures were further characterized for their bulk properties observing an excellent flow and later subjected to a PBF-3D printing process. The physical mixtures, processed granules, and printed tablets were characterized using conventional as well as advanced solid-state characterization. These characterizations revealed the amorphous nature of the drug in the processed granules and printed tablets. Further, the in vitro release testing of the tablets with produced granules as a reference standard depicted a notable solubility advantage (100% drug released in 5 minutes at >pH 6.8) over the pure drug and the physical mixture. Our developed system known as DosePlus combines innovative continuous granulation and PBF-3D printing process which can potentially improve the physical properties of the bulk drug and formulations in comparison to when used in isolation. This process can further find application in continuous manufacturing of granules and additive manufacturing of pharmaceuticals to produce dosage forms with excellent uniformity and solubility advantage.Abstract Figure

Introduction

2013 ◽  
pp. 1-5
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Evolutionary Computing ◽  
Historical Background ◽  
Intelligent Computing ◽  
Biometric Security ◽  
Introductory Overview ◽  
Over Time

This chapter presents an introductory overview of the application of computational intelligence in biometrics. Starting with the historical background on artificial intelligence, the chapter proceeds to the evolutionary computing and neural networks. Evolutionary computing is an ability of a computer system to learn and evolve over time in a manner similar to humans. The chapter discusses swarm intelligence, which is an example of evolutionary computing, as well as chaotic neural network, which is another aspect of intelligent computing. At the end, special concentration is given to a particular application of computational intelligence—biometric security.

Exploring the Multi-Stage Effects of Material Preparation and Printing on 3D Printing Product Quality

2019 ◽  
Author(s):  
Sahand Hajifar ◽  
Ramanarayanan Purnanandam ◽  
Hongyue Sun ◽  
Chi Zhou
Keyword(s):  
3D Printing ◽  
Material Properties ◽  
Good Property ◽  
Metal Powders ◽  
Promising Technique ◽  
Printing Process ◽  
Multi Stage ◽  
Flexible Parts ◽  
Printing Processes ◽  
Material Preparation

Abstract 3D printing is a promising technique to fabricate flexible parts and reduce the supply chain. Various materials, such as metal powders, plastics, ultraviolet (UV) sensitive resins, can be fabricated from 3D printing and form the final printed part. Currently, most researchers either focus on exploring printable materials with good property or focus on the process quality control given a certain type of material. However, for many 3D printing processes, the printing process and product properties are dependent on both the material properties and process settings. To the best of the authors’ knowledge, the quantitative analysis of the interactions of material properties and printing process settings are rarely studied. In this paper, we treat the material preparation and 3D printing as different manufacturing stages, and we explore the multi-stage effects in 3D printing. In particular, we add carbon fiber to the CLEAR resin to alter the material properties for a stereolithography (SLA) 3D printing process. It is observed that the part properties are jointly affected by material properties and printing process settings. Therefore, the material property and process settings should be jointly considered for optimizing 3D printing processes.

FDM 3D Printing Process - Risks and Environmental Aspects

2021 ◽  
Vol 890 ◽  
pp. 152-156
Author(s):  
Mirela Ciornei ◽  
Răzvan Ionuț Iacobici ◽  
Ionel Dănuț Savu ◽  
Dalia Simion
Keyword(s):  
3D Printing ◽  
Extrusion Process ◽  
Milling Process ◽  
Pollutant Emissions ◽  
Direct Result ◽  
Printing Process ◽  
Technological Processes ◽  
Metal Printing ◽  
Pollution Emissions ◽  
Printing Processes

The application of the 3D printing processes is continuously increasing due to their large number of technical and economic advantages when produce prototypes, but in the mass fabrication as well, especially for metal printing of low dimension products. The process produces pollution as all technological processes. Noise, fume and polymer wastes are the main elements which exit from the process and they are not products. The types and the volumes of those pollution emissions depend on the process parameters. The paper presents the results of FDM process emissions analysis. It was recorded the noise for different stages of the printer functioning. It was measured the volume and the contents of the fume produced during the extrusion of the polymer, for PLA polymer and for ABS polymer filaments. Specific risks were analysed and conclusions were reported. The measurement was done for a random chosen product and the results were compared with the pollutant emissions from traditional technological processes applied to erect the same type of product. It has been concluded that the noise emitted during the FDM printing is about 82-85% of the noise produced when apply milling to create similar shapes and dimensions (it was recorded values for the sound pressure in a large range: 42-68 dB, depending on the working regime). Regarding the fume emission, the intensity of emission was up to 40% higher in the FDM process comparing to the milling process. That was explained as being a direct result of the fluid-viscous state in which the material is put during the printing process. When discuss about the risks, most of the main identified risks in the milling and/or extrusion process were almost inexistent in the FDM printing. Electrical injuries and heat injuries are the main risks to which the operator is exposed. Mechanical injuries are sensitively lower than in the traditional processes, as milling The FDM process is safer and produces lower material wastes. It can be concluded that the FDM printing process has lower impact with the environment and with the operator.

scholarly journals Subjectness of Digital Communication in the Context of the Technological Evolution of the Contemporary Society: Threats, Challenges, and Risks

2021 ◽  
pp. 437-456
Author(s):  
Sergey Volodenkov ◽  
Sergey Fedorchenko
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Political Communication ◽  
Digital Communication ◽  
Digital Communications ◽  
Political Communications ◽  
Wide Range ◽  
Information And Communication ◽  
Communication Practices ◽  
Self Learning

The purpose of this article is to identify the risks, threats, and challenges associated with possible social changes in the processes of digitalization of society and transformations of traditional communication practices, which is associated with the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction of people. The primary tasks of the work were to identify the potential of artificial intelligence technologies and neural networks in the field of social and political communications, as well as to analyze the features of “smart” communications in terms of their subjectness. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of social and political digitalization, as well as the method of critical analysis of current communication practices in the socio-political sphere. At the same time, when analyzing the current digitalization practices, the case study method was used. The authors substantiate the thesis that introducing technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary processes of socio-political communication creates the potential for a wide range of challenges, threats, and risks, the key of which is the problem of identifying the actual subjects of digital communication acts. The article also discusses the problem of increasing the manipulative potential of “smart” communications, for which the authors used the concepts of cyber simulacrum and information capsule developed by them. The paper shows that artificial intelligence and self-learning neural network algorithms, being increasingly widely introduced into the current practice of contemporary digital communications, form a high potential for information and communication impact on the mass consciousness from technological solutions that no longer require control by operators – humans. As a result, conditions arise to form a hybrid socio-technical reality – a communication reality of a new type with mixed subjectness. The paper also concludes that in the current practices of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. In the article, the authors suggest that digital rituals blur the line between the activity of digital avatars based on artificial intelligence and the activity of actual people, resulting in the potential for a person to lose his own subjectness in the digital communications space.

scholarly journals Subjectness of digital communication in the context of technological evolution of the Internet: features and transformation scenarios

2021 ◽  
Author(s):  
S.V. Volodenkov ◽  
S.N. Fedorchenko
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Current Practice ◽  
Digital Communication ◽  
Social Practices ◽  
Network Algorithms ◽  
Political Sphere ◽  
Communication Processes ◽  
Self Learning

The work aimed to study the peculiarities of the subjectness of the phenomenon of digital communication in the context of intensive digitalization of key spheres of life of modern society, as well as to identify the prospects and threats of introducing self-learning neural network algorithms and artificial intelligence technologies into communication processes unfolding in the social and political sphere. One of the study's key objectives was to identify scenarios of possible social changes in the context of society digitalization and the traditional social practices transformation in terms of the emergence of new digital subjects of mass public communication that form the pseudo structure of digital interaction between people. As a methodological optics, the work used the method of discourse analysis of scientific research devoted to the implementation and application of artificial intelligence technologies and self-learning neural networks in the processes of socio-political digitalization, as well as the method of critical analysis of current communication practice in the socio-political sphere. At the same time, when analyzing the current practice of digitalization in foreign countries, the case study method was used. In turn, to determine the scenarios for the transformation of traditional social space and social practices, the method of scenario techniques and scenario forecasting was applied. As a research result, it was concluded that the introduction of technological solutions based on artificial intelligence algorithms and self-learning neural networks into contemporary socio-political communication processes creates the potential for the problem of identifying the subjects of communicative acts in the socio-political sphere of the contemporary society life. Based on the results of the study, it is shown that artificial intelligence and self-learning neural network algorithms are increasingly being implemented in the current practice of contemporary digital communications, forming a high potential for information and communication impact on the mass consciousness of technological solutions that no longer require self-control from human operators. The work also concludes that in the current practice of social interactions in the digital space, a person faces a new phenomenon – interfaceization, within which self-communication stimulates the universalization and standardization of digital behavior, creating, disseminating, strengthening, and imposing special digital rituals. The article proves that digital rituals blur the line between digital avatars' activity based on artificial intelligence and the activity of real people, resulting in the potential for a person to lose their own subjectness in the digital universe.

scholarly journals X-ware: a proof of concept malware utilizing artificial intelligence

2022 ◽  
Vol 12 (2) ◽  
pp. 1937
Author(s):  
Thanh Cong Truong ◽  
Jan Plucar ◽  
Bao Quoc Diep ◽  
Ivan Zelinka
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Research Literature ◽  
Proof Of Concept ◽  
New Type ◽  
Credible Threat ◽  
Mitigation Methods ◽  
Dramatic Growth

<p>Recent years have witnessed a dramatic growth in utilizing computational intelligence techniques for various domains. Coherently, malicious actors are expected to utilize these techniques against current security solutions. Despite the importance of these new potential threats, there remains a paucity of evidence on leveraging these research literature techniques. This article investigates the possibility of combining artificial neural networks and swarm intelligence to generate a new type of malware. We successfully created a proof of concept malware named X-ware, which we tested against the Windows-based systems. Developing this proof of concept may allow us to identify this potential threat’s characteristics for developing mitigation methods in the future. Furthermore, a method for recording the virus’s behavior and propagation throughout a file system is presented. The proposed virus prototype acts as a swarm system with a neural network-integrated for operations. The virus’s behavioral data is recorded and shown under a complex network format to describe the behavior and communication of the swarm. This paper has demonstrated that malware strengthened with computational intelligence is a credible threat. We envisage that our study can be utilized to assist current and future security researchers to help in implementing more effective countermeasures</p>

scholarly journals Computational Intelligence in Marine Control Engineering Education

2021 ◽  
Vol 28 (1) ◽  
pp. 163-172
Author(s):  
Józef Lisowski
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Computational Intelligence ◽  
Control Engineering ◽  
New Approach ◽  
Practical Education ◽  
Ship Control ◽  
The Cost ◽  
Ship Operation

Abstract This paper presents a new approach to the existing training of marine control engineering professionals using artificial intelligence. We use optimisation strategies, neural networks and game theory to support optimal, safe ship control by applying the latest scientific achievements to the current process of educating students as future marine officers. Recent advancements in shipbuilding, equipment for robotised ships, the high quality of shipboard game plans, the cost of overhauling, dependability, the fixing of the shipboard equipment and the requesting of the safe shipping and environmental protection, requires constant information on recent equipment and programming for computational intelligence by marine officers. We carry out an analysis to determine which methods of artificial intelligence can allow us to eliminate human subjectivity and uncertainty from real navigational situations involving manoeuvring decisions made by marine officers. Trainees learn by using computer simulation methods to calculate the optimal safe traverse of the ship in the event of a possible collision with other ships, which are mapped using neural networks that take into consideration the subjectivity of the navigator. The game-optimal safe trajectory for the ship also considers the uncertainty in the navigational situation, which is measured in terms of the risk of collision. The use of artificial intelligence methods in the final stage of training on ship automation can improve the practical education of marine officers and allow for safer and more effective ship operation.

scholarly journals Real-Time 3D Printing Remote Defect Detection (Stringing) with Computer Vision and Artificial Intelligence

Processes ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 1464
Author(s):  
Konstantinos Paraskevoudis ◽  
Panagiotis Karayannis ◽  
Elias P. Koumoulos
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
3D Printing ◽  
Large Scale ◽  
Video Camera ◽  
Fused Filament Fabrication ◽  
Printing Process ◽  
3D Printed ◽  
Live Environment ◽  
Printing Parameters

This work describes a novel methodology for the quality assessment of a Fused Filament Fabrication (FFF) 3D printing object during the printing process through AI-based Computer Vision. Specifically, Neural Networks are developed for identifying 3D printing defects during the printing process by analyzing video captured from the process. Defects are likely to occur in 3D printed objects during the printing process, with one of them being stringing; they are mostly correlated to one of the printing parameters or the object’s geometries. The defect stringing can be on a large scale and is usually located in visible parts of the object by a capturing camera. In this case, an AI model (Deep Convolutional Neural Network) was trained on images where the stringing issue is clearly displayed and deployed in a live environment to make detections and predictions on a video camera feed. In this work, we present a methodology for developing and deploying deep neural networks for the recognition of stringing. The trained model can be successfully deployed (with appropriate assembly of required hardware such as microprocessors and a camera) on a live environment. Stringing can be then recognized in line with fast speed and classification accuracy. Furthermore, this approach can be further developed in order to make adjustments to the printing process. Via this, the proposed approach can either terminate the printing process or correct parameters which are related to the identified defect.

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