Modeling the Producibility of 3D Printing in Polylactic Acid Using Artificial Neural Networks and Fused Filament Fabrication

Polylactic acid (PLA) is a highly applicable material that is used in 3D printers due to some significant features such as its deformation property and affordable cost. For improvement of the end-use quality, it is of significant importance to enhance the quality of fused filament fabrication (FFF)-printed objects in PLA. The purpose of this investigation was to boost toughness and to reduce the production cost of the FFF-printed tensile test samples with the desired part thickness. To remove the need for numerous and idle printing samples, the response surface method (RSM) was used. Statistical analysis was performed to deal with this concern by considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The artificial intelligence method of artificial neural network (ANN) and ANN-genetic algorithm (ANN-GA) were further developed to estimate the toughness, part thickness, and production-cost-dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could enhance the accuracy of modeling by about 7.5, 11.5, and 4.5% for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other hand, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects.

Download Full-text

Modeling Fused Filament Fabrication using Artificial Neural Networks

Production Engineering ◽

10.1007/s11740-021-01020-y ◽

2021 ◽

Author(s):

Paul Oehlmann ◽

Paul Osswald ◽

Juan Camilo Blanco ◽

Martin Friedrich ◽

Dominik Rietzel ◽

...

Keyword(s):

Real Time ◽

Large Scale ◽

Cost Effective ◽

Print Quality ◽

Ann Model ◽

Production Environment ◽

Fused Filament Fabrication ◽

Artificial Neural ◽

Using Data ◽

Artificial Neural Network Ann

AbstractWith industries pushing towards digitalized production, adaption to expectations and increasing requirements for modern applications, has brought additive manufacturing (AM) to the forefront of Industry 4.0. In fact, AM is a main accelerator for digital production with its possibilities in structural design, such as topology optimization, production flexibility, customization, product development, to name a few. Fused Filament Fabrication (FFF) is a widespread and practical tool for rapid prototyping that also demonstrates the importance of AM technologies through its accessibility to the general public by creating cost effective desktop solutions. An increasing integration of systems in an intelligent production environment also enables the generation of large-scale data to be used for process monitoring and process control. Deep learning as a form of artificial intelligence (AI) and more specifically, a method of machine learning (ML) is ideal for handling big data. This study uses a trained artificial neural network (ANN) model as a digital shadow to predict the force within the nozzle of an FFF printer using filament speed and nozzle temperatures as input data. After the ANN model was tested using data from a theoretical model it was implemented to predict the behavior using real-time printer data. For this purpose, an FFF printer was equipped with sensors that collect real time printer data during the printing process. The ANN model reflected the kinematics of melting and flow predicted by models currently available for various speeds of printing. The model allows for a deeper understanding of the influencing process parameters which ultimately results in the determination of the optimum combination of process speed and print quality.

Download Full-text

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

Algorithms, Methods, and Applications in Mobile Computing and Communications - Advances in Computer and Electrical Engineering ◽

10.4018/978-1-5225-5693-0.ch007 ◽

2019 ◽

pp. 158-177

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

The IEEE 802.16 system offers power-saving class type II as a power-saving algorithm for real-time services such as voice over internet protocol (VoIP) service. However, it doesn't take into account the silent periods of VoIP conversation. This chapter proposes a power conservation algorithm based on artificial neural network (ANN-VPSM) that can be applied to VoIP service over WiMAX systems. Artificial intelligent model using feed forward neural network with a single hidden layer has been developed to predict the mutual silent period that used to determine the sleep period for power saving class mode in IEEE 802.16. From the implication of the findings, ANN-VPSM reduces the power consumption during VoIP calls with respect to the quality of services (QoS). Experimental results depict the significant advantages of ANN-VPSM in terms of power saving and quality-of-service (QoS). It shows the power consumed in the mobile station can be reduced up to 3.7% with respect to VoIP quality.

Download Full-text

Length of Hospital Stay Prediction at the Admission Stage for Cardiology Patients Using Artificial Neural Network

Journal of Healthcare Engineering ◽

10.1155/2016/7035463 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 24

Author(s):

Pei-Fang (Jennifer) Tsai ◽

Po-Chia Chen ◽

Yen-You Chen ◽

Hao-Yuan Song ◽

Hsiu-Mei Lin ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Coronary Atherosclerosis ◽

Inpatient Care ◽

Length Of Hospital Stay ◽

Ann Models ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Christian Hospital

For hospitals’ admission management, the ability to predict length of stay (LOS) as early as in the preadmission stage might be helpful to monitor the quality of inpatient care. This study is to develop artificial neural network (ANN) models to predict LOS for inpatients with one of the three primary diagnoses: coronary atherosclerosis (CAS), heart failure (HF), and acute myocardial infarction (AMI) in a cardiovascular unit in a Christian hospital in Taipei, Taiwan. A total of 2,377 cardiology patients discharged between October 1, 2010, and December 31, 2011, were analyzed. Using ANN or linear regression model was able to predict correctly for 88.07% to 89.95% CAS patients at the predischarge stage and for 88.31% to 91.53% at the preadmission stage. For AMI or HF patients, the accuracy ranged from 64.12% to 66.78% at the predischarge stage and 63.69% to 67.47% at the preadmission stage when a tolerance of 2 days was allowed.

Download Full-text

Enhancing 3D Printing Producibility in Polylactic Acid Using Fused Filament Fabrication Fused Deposition Modelling and Machine Learning

10.21203/rs.3.rs-131006/v1 ◽

2020 ◽

Author(s):

Mahmoud Moradi ◽

M. Saleh Meiabadi ◽

Mojtaba Karami Moghadam ◽

Sina Ardabili ◽

Shahab S. Band ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Production Cost ◽

Fused Deposition Modeling ◽

Deformation Property ◽

Fused Filament Fabrication ◽

Fused Deposition ◽

Surface Method ◽

Deposition Modeling ◽

Part Thickness

Abstract Polylactic Polylactic acid (PLA) is one of the high applicable material which is used in the 3D printers due to some significant features like its deformation property and affordable costacid (PLA) is brittle in nature with extensive deformation property. For improvement of the end-use quality, it is of significant importance to enhance the quality of Fused Filament Fabrication (FFF)fused deposition modeling (FDM)-printed objects in PLA. The purpose of this investigation is to boost toughness and to reduce the production cost of the FDMFFF-printed tensile test samples with the desired part thickness. Due to prevent from many numerous and idle printing samples the response Surface Method (RSM) is used.To attain the research purpose number of experiments are designed and analyzed by the Response Surface Method (RSM). The statistical analysis is performed to deal with this concern considering extruder temperature (ET), infill percentage (IP), and layer thickness (LT) as controlled factors. The tensile test specimens are printed based on the designed experiments, and the tensile strength tests are conducted by SANTAM 150 universal testing machine based on ASTM D638. The pattern for filling is designed based on honeycomb which is applied to produce lightweight and high-strength specimens. The area under Force- Extension curve up to fracture is acquired as the toughness of the printed specimens. This study also developed a modeling process using artificial neural network (ANN) and artificial neural network- genetic algorithm (ANN-GA) techniques to develop an accurate estimation for toughness, part thickness, and production cost dependent variables. Results were evaluated by correlation coefficient and RMSE values. According to the modeling results, ANN-GA as a hybrid machine learning (ML) technique could could successfully improveenhances the accuracy of modeling about 7.5, 11.5 and 4.5 % for toughness, part thickness, and production cost, respectively, in comparison with those for the single ANN method. On the other side, the optimization results confirm that the optimized specimen is cost-effective and able to comparatively undergo deformation, which enables the usability of printed PLA objects. The research is accomplished under the constraints of PLA compatibility with existing Fused Filament Fabrication fused deposition modeling installation, in the absence of the functional assistant of the machine in the absence of the functional assistant of the machine. Although the mechanical properties and dimensional accuracy of PLA have already been studied, there is little literature on the toughness of the printed PLA with honeycomb internal fill pattern.

Download Full-text

ARTIFICIAL NEURAL NETWORKS FOR PREDICTING THE GENERATION OF ACETALDEHYDE IN PET RESIN IN THE PROCESS OF INJECTION OF PLASTIC PACKAGES

International Journal for Innovation Education and Research ◽

10.31686/ijier.vol9.iss6.3150 ◽

2021 ◽

Vol 9 (6) ◽

pp. 97-119

Author(s):

Mauro Reis Nascimento ◽

David Barbosa de Alencar ◽

Manoel Henrique Reis Nascimento ◽

Carlos Alberto Monteiro

Keyword(s):

Decision Making ◽

Strategic Decision ◽

Drying Temperature ◽

Pet Bottles ◽

Injection Process ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

Method Analysis ◽

Plastic Injection

The industrial production of preforms for the manufacture of PET bottles, during the plastic injection process, is essential to regulate the drying temperature of the PET resin, to control the generation of Acetaldehyde (ACH), which alters the flavor of carbonated or non-carbonated drinks, giving the drink a citrus flavor and putting in doubt the quality of packaged products. In this work, an Artificial Neural Network (ANN) of the Backpropagation type (Cascadeforwardnet) is specified to support the decision-making process in controlling the ideal drying temperature of the PET resin, allowing specialists to make the necessary temperature regulation decisions for the best performance by decreasing ACH levels. The materials and methods were applied according to the manufacturer's characteristics on the moisture in the PET resin grain, which may contain between 50 ppm and 100 ppm of ACH. Data were collected for the method analysis, according to temperatures and residence times used in the blow injection process in the manufacture of the bottle preform, the generation of ACH from the PET bottle after solid post-condensation stage reached residual ACH levels below (3-4) ppm, according to the desired specification, reaching levels below 1 ppm. The results found through the Computational Intelligence (IC) techniques applied by the ANNs, where they allowed the prediction of the ACH levels generated in the plastic injection process of the bottle packaging preform, allowing an effective management of the parameters of production, assisting in strategic decision making regarding the use of temperature control during the drying process of PET resin.

Download Full-text

Assessing the Impact of Expert Labelling of Training Data on the Quality of Automatic Classification of Lithological Groups Using Artificial Neural Networks

Applied Computer Systems ◽

10.2478/acss-2020-0016 ◽

2020 ◽

Vol 25 (2) ◽

pp. 145-152

Author(s):

Yan Kuchin ◽

Ravil Mukhamediev ◽

Kirill Yakunin ◽

Janis Grundspenkis ◽

Adilkhan Symagulov

Keyword(s):

Neural Network ◽

Automatic Classification ◽

Training Data ◽

Classification Problems ◽

Expert Opinions ◽

Input Parameters ◽

Artificial Neural ◽

Artificial Neural Network Ann ◽

The Impact

AbstractMachine learning (ML) methods are nowadays widely used to automate geophysical study. Some of ML algorithms are used to solve lithological classification problems during uranium mining process. One of the key aspects of using classical ML methods is causing data features and estimating their influence on the classification. This paper presents a quantitative assessment of the impact of expert opinions on the classification process. In other words, we have prepared the data, identified the experts and performed a series of experiments with and without taking into account the fact that the expert identifier is supplied to the input of the automatic classifier during training and testing. Feedforward artificial neural network (ANN) has been used as a classifier. The results of the experiments show that the “knowledge” of the ANN of which expert interpreted the data improves the quality of the automatic classification in terms of accuracy (by 5 %) and recall (by 20 %). However, due to the fact that the input parameters of the model may depend on each other, the SHapley Additive exPlanations (SHAP) method has been used to further assess the impact of expert identifier. SHAP has allowed assessing the degree of parameter influence. It has revealed that the expert ID is at least two times more influential than any of the other input parameters of the neural network. This circumstance imposes significant restrictions on the application of ANNs to solve the task of lithological classification at the uranium deposits.

Download Full-text

Adaptive Power-Saving Mechanism for VoIP Over WiMAX Based on Artificial Neural Network

10.4018/978-1-6684-2408-7.ch022 ◽

2022 ◽

pp. 471-489

Author(s):

Tamer Emara

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Power Saving ◽

Ieee 802.16 ◽

Feed Forward Neural Network ◽

Artificial Neural ◽

Adaptive Power ◽

Artificial Neural Network Ann ◽

Hidden Layer

Download Full-text

Forest landscape aesthetic quality model (FLAQM): A comparative study on landscape modelling using regression analysis and artificial neural networks

Journal of Forest Science ◽

10.17221/86/2018-jfs ◽

2019 ◽

Vol 65 (No. 2) ◽

pp. 61-69 ◽

Cited By ~ 14

Author(s):

Ali Jahani

Keyword(s):

Sensitivity Analysis ◽

Forest Landscape ◽

Quality Model ◽

Aesthetic Quality ◽

Virgin Forest ◽

Representative Study ◽

Human Perceptions ◽

Artificial Neural ◽

Artificial Neural Network Ann

Today, the landscape aesthetic quality assessment is more technical and quantitative in environmental management. We aimed at developing artificial neural network (ANN) modelling and multiple regression (MLR) analysis approaches to predict the perceptional aesthetic quality of forest landscapes. The methodology, followed in this paper, can be divided into six distinct parts: (i) selection of representative study sites, (ii) mapping of landscape units, (iii) quantification of naturalness indicators, (iv) visibility analysis, (v) assessment of human perceptions, (vi) ANN and MLR modelling and sensitivity analysis. The results of ANN modelling, especially its high accuracy (R<sup>2</sup> = 0.871) in comparison with MLR results (R<sup>2</sup> = 0.782), introduced the forest landscape aesthetic quality model (FLAQM) as a comparative model for an assessment of forest landscape aesthetic quality. According to sensitivity analysis, the values of livestock density, tree harvesting, virgin forest, animal grazing, and tree richness were identified as the most significant variables which influence FLAQM. FLAQM can be used to compare the classes of aesthetic quality of forests.

Download Full-text

Research of Artificial Neural Networks in the Al2O3 Ceramic Laser Milling Application

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.528.101 ◽

2014 ◽

Vol 528 ◽

pp. 101-106 ◽

Cited By ~ 1

Author(s):

Zhao Mei Xu ◽

Zong Hai Hong ◽

Gang Yang ◽

Qin Gan Wang

Keyword(s):

Laser Power ◽

Scanning Speed ◽

Milling Quality ◽

Laser Process ◽

Laser Milling ◽

The Mean ◽

Ceramic Laser ◽

Artificial Neural ◽

Artificial Neural Network Ann

Based on the artificial neural network (ANN), a model is established to describe the relation of the laser milling quality of the Al2O3ceramics with the ceramics parameters. The milling quality of Al2O3ceramics are predicted with the model in which the input parameters consist of laser power, scanning speed and defocus amount and the output parameters include the milling depth and width. The results show that the mean error is small, and the model has good verifying precision and excellent ability of predicting. The laser process parameters can be chosen easily and accurately to improve the processing quality of laser milling.

Download Full-text

Development of an Automatic Image Cropping and Feature Extraction System for Real-Time Warping Monitoring in 3D Printing

10.32920/ryerson.14639274.v1 ◽

2021 ◽

Author(s):

Jiarui Xie

Keyword(s):

Real Time ◽

Detection System ◽

Region Of Interest ◽

Cost Effective ◽

Time Warping ◽

Time Data ◽

Fused Filament Fabrication ◽

System A ◽

Effective Manner

Fused Filament Fabrication (FFF) is an additive manufacturing technology that can produce complicated structures in a simple-to-use and cost-effective manner. Although promising, the technology is prone to defects, e.g. warping, compromising the quality of the manufactured component. To avoid the adverse effects caused by warping, this thesis utilizes deep-learning algorithms to develop a warping detection system using Convolutional Neural Networks (CNN). To create such a system, a real-time data acquisition and analysis pipeline is laid out. The system is responsible for capturing a snapshot of the print layer-bylayer and simultaneously extracting the corners of the component. The extracted region-of-interest is then passed through a CNN outputting the probability of a corner being warped. If a warp is detected, a signal is sent to pause the print, thereby creating a closed-loop monitoring system. The underlying model is tested on a real-time manufacturing environment yielding a mean accuracy of 99.21%.

Download Full-text