Online Detection and Prediction of Fused Deposition Modelled Parts Using Artificial Intelligence

2022 ◽  
pp. 194-209
Author(s):  
Sachin Salunkhe ◽  
G. Kanagachidambaresan ◽  
C. Rajkumar ◽  
K. Jayanthi
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Critical Issue ◽  
Extrusion Process ◽  
Human Interaction ◽  
Fused Deposition Modelling ◽  
Fused Deposition ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Complex Parts

Fused deposition modelling (FDM) is a technology used for filament deposition of heated plastic filaments by a given pattern by the melted extrusion process. Delamination is a critical issue of FDM's incredibly complex parts. In this chapter, the artificial intelligence (machine learning) model is used for online detections and prediction of FDM parts. The proposed machine learning and convolutional neural network model is capable of online detect delamination of FDM parts. The proposed model can also be applied for different types of additive manufacturing materials with less human interaction.

AN EFFICIENT MACHINE LEARNING MODEL FOR PREDICTION OF ACUTE MYOCARDIAL INFARCTION

2020 ◽  
Vol 13 ◽  
Author(s):  
Dhilsath Fathima.M ◽  
S. Justin Samuel ◽  
R. Hari Haran
Keyword(s):  
Machine Learning ◽  
Myocardial Infarction ◽  
Acute Myocardial Infarction ◽  
Logistic Regression ◽  
Decision Tree ◽  
Learning Model ◽  
Training Dataset ◽  
Data Set ◽  
Machine Learning Model ◽  
Proposed Model

Aim: This proposed work is used to develop an improved and robust machine learning model for predicting Myocardial Infarction (MI) could have substantial clinical impact. Objectives: This paper explains how to build machine learning based computer-aided analysis system for an early and accurate prediction of Myocardial Infarction (MI) which utilizes framingham heart study dataset for validation and evaluation. This proposed computer-aided analysis model will support medical professionals to predict myocardial infarction proficiently. Methods: The proposed model utilize the mean imputation to remove the missing values from the data set, then applied principal component analysis to extract the optimal features from the data set to enhance the performance of the classifiers. After PCA, the reduced features are partitioned into training dataset and testing dataset where 70% of the training dataset are given as an input to the four well-liked classifiers as support vector machine, k-nearest neighbor, logistic regression and decision tree to train the classifiers and 30% of test dataset is used to evaluate an output of machine learning model using performance metrics as confusion matrix, classifier accuracy, precision, sensitivity, F1-score, AUC-ROC curve. Results: Output of the classifiers are evaluated using performance measures and we observed that logistic regression provides high accuracy than K-NN, SVM, decision tree classifiers and PCA performs sound as a good feature extraction method to enhance the performance of proposed model. From these analyses, we conclude that logistic regression having good mean accuracy level and standard deviation accuracy compared with the other three algorithms. AUC-ROC curve of the proposed classifiers is analyzed from the output figure.4, figure.5 that logistic regression exhibits good AUC-ROC score, i.e. around 70% compared to k-NN and decision tree algorithm. Conclusion: From the result analysis, we infer that this proposed machine learning model will act as an optimal decision making system to predict the acute myocardial infarction at an early stage than an existing machine learning based prediction models and it is capable to predict the presence of an acute myocardial Infarction with human using the heart disease risk factors, in order to decide when to start lifestyle modification and medical treatment to prevent the heart disease.

Smishing Detection: Using Artificial Intelligence

2021 ◽  
Vol 9 (8) ◽  
pp. 2218-2224
Author(s):  
Samyak Sadanand Shravasti
Keyword(s):  
Artificial Intelligence ◽  
Short Message Service ◽  
Personal Information ◽  
Critical Issue ◽  
Cyber Attacks ◽  
Sensitive Information ◽  
Short Message ◽  
Proposed Model ◽  
Message Service ◽  
A Company

Abstract: Phishing occurs when people's personal information is stolen via email, phone, or text communications. In Smishing Short Message Service (SMS) is used for cyber-attacks, Smishing is a type of theft of sensitive information. People are more likely to give personal information such as account details and passwords when they receive SMS messages. This data could be used to steal money or personal information from a person or a company. As a result, Smishing is a critical issue to consider. The proposed model uses an Artificial Intelligence to detect smishing. Analysing a SMS and successfully detecting Smishing is possible. Finally, we evaluate and analyse our proposed model to show its efficacy. Keywords: Phishing, Smishing, Artificial Intelligence, LSTM, RNN

Introduction to Machine Learning and Its Application

2020 ◽  
pp. 262-290
Author(s):  
Ladly Patel ◽  
Kumar Abhishek Gaurav
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Algorithm ◽  
Machine Learning Algorithms ◽  
Human Interaction ◽  
Machine Learning Algorithm ◽  
Past Data ◽  
Applications Of Machine Learning ◽  
Learning Frameworks ◽  
Learning Data

In today's world, a huge amount of data is available. So, all the available data are analyzed to get information, and later this data is used to train the machine learning algorithm. Machine learning is a subpart of artificial intelligence where machines are given training with data and the machine predicts the results. Machine learning is being used in healthcare, image processing, marketing, etc. The aim of machine learning is to reduce the work of the programmer by doing complex coding and decreasing human interaction with systems. The machine learns itself from past data and then predict the desired output. This chapter describes machine learning in brief with different machine learning algorithms with examples and about machine learning frameworks such as tensor flow and Keras. The limitations of machine learning and various applications of machine learning are discussed. This chapter also describes how to identify features in machine learning data.

Thermomechanical investigations of PEKK-HAp-CS composites

2019 ◽  
Vol 233 (11) ◽  
pp. 1196-1203
Author(s):  
Rupinder Singh ◽  
Gurchetan Singh ◽  
Jaskaran Singh ◽  
Ranvijay Kumar ◽  
Md Mustafizur Rahman ◽  
...  
Keyword(s):  
Melt Flow Index ◽  
Extrusion Process ◽  
Flow Index ◽  
Fused Deposition Modelling ◽  
Index Test ◽  
Melt Flow ◽  
Scanning Calorimetry ◽  
Fused Deposition ◽  
Ether Ketone ◽  
Input Variables

In this experimental study, a composite of poly-ether-ketone-ketone by reinforcement of hydroxyapatite and chitosan has been prepared for possible applications as orthopaedic scaffolds. Initially, different weight percentages of hydroxyapatite and chitosan were reinforced in the poly-ether-ketone-ketone matrix and tested for melt flow index in order to check the flowability of different compositions/proportions. Suitable compositions revealed by the melt flow index test were then taken forward for the extrusion of filament required for fused deposition modelling. For thermomechanical investigations, Taguchi-based design of experiments has been used with input variables in the extrusion process as follows: temperature, load applied and different composition/proportions. The specimens in the form of feedstock filament produced by the extrusion process were made to undergo tensile testing. The specimens were also inspected by differential scanning calorimetry and photomicrographs. Finally, the specimen showing the best performance from the thermomechanical viewpoint has been selected to extrude the filament for the fused deposition modelling process.

Geometric Accuracy Prediction for Additive Manufacturing Through Machine Learning of Triangular Mesh Data

2019 ◽  
Author(s):  
Nathan Decker ◽  
Qiang Huang
Keyword(s):  
Machine Learning ◽  
Additive Manufacturing ◽  
Three Dimensional ◽  
Triangular Mesh ◽  
Geometric Accuracy ◽  
New Approach ◽  
Machine Learning Model ◽  
3D Printed ◽  
3D Shapes ◽  
Complex Parts

Abstract While additive manufacturing has seen tremendous growth in recent years, a number of challenges remain, including the presence of substantial geometric differences between a three dimensional (3D) printed part, and the shape that was intended. There are a number of approaches for addressing this issue, including statistical models that seek to account for errors caused by the geometry of the object being printed. Currently, these models are largely unable to account for errors generated in freeform 3D shapes. This paper proposes a new approach using machine learning with a set of predictors based on the geometric properties of the triangular mesh file used for printing. A direct advantage of this method is the simplicity with which it can describe important properties of a 3D shape and allow for predictive modeling of dimensional inaccuracies for complex parts. To evaluate the efficacy of this approach, a sample dataset of 3D printed objects and their corresponding deviations was generated. This dataset was used to train a random forest machine learning model and generate predictions of deviation for a new object. These predicted deviations were found to compare favorably to the actual deviations, demonstrating the potential of this approach for applications in error prediction and compensation.

scholarly journals Tensile Mechanical Behaviour of Multi-Polymer Sandwich Structures via Fused Deposition Modelling

Polymers ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 651 ◽  
Author(s):  
David Moises Baca Lopez ◽  
Rafiq Ahmad
Keyword(s):  
Mechanical Properties ◽  
Low Cost ◽  
Acrylonitrile Butadiene Styrene ◽  
Extrusion Process ◽  
Poly Lactic Acid ◽  
Fused Deposition Modelling ◽  
Fused Deposition ◽  
Homogeneous Materials ◽  
Flexural Tests ◽  
High Level

The application of single homogeneous materials produced through the fused deposition modelling (FDM) technology restricts the production of high-level multi-material components. The fabrication of a sandwich-structured specimen with different material combinations using conventional thermoplastics such as poly (lactic acid) (PLA), acrylonitrile butadiene styrene (ABS) and high impact polystyrene (HIPS) through the filament-based extrusion process can demonstrate an improvement on its properties. This paper aims to assess among these materials, the best material sandwich-structured arrangement design, to enhance the mechanical properties of a part and to compare the results with the homogeneous materials selected. The samples were subjected to tensile testing to identify the tensile strength, elongation at break and Young’s modulus of each material combination. The experimental results demonstrate that applying the PLA-ABS-PLA sandwich arrangement leads to the best mechanical properties between these materials. This study enables users to consider sandwich structure designs as an alternative to manufacturing multi-material components using conventional and low-cost materials. Future work will consider the flexural tests to identify the maximum stresses and bending forces under pressure.

scholarly journals In AI We Trust Incrementally: a Multi-layer Model of Trust to Analyze Human-Artificial Intelligence Interactions

2019 ◽  
Vol 33 (3) ◽  
pp. 523-539 ◽  
Author(s):  
Andrea Ferrario ◽  
Michele Loi ◽  
Eleonora Viganò
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Ethical Dilemmas ◽  
Academic Research ◽  
Business Organizations ◽  
Layer Model ◽  
Incremental Model ◽  
Program Of Study ◽  
Proposed Model

Abstract Real engines of the artificial intelligence (AI) revolution, machine learning (ML) models, and algorithms are embedded nowadays in many services and products around us. As a society, we argue it is now necessary to transition into a phronetic paradigm focused on the ethical dilemmas stemming from the conception and application of AIs to define actionable recommendations as well as normative solutions. However, both academic research and society-driven initiatives are still quite far from clearly defining a solid program of study and intervention. In this contribution, we will focus on selected ethical investigations around AI by proposing an incremental model of trust that can be applied to both human-human and human-AI interactions. Starting with a quick overview of the existing accounts of trust, with special attention to Taddeo’s concept of “e-trust,” we will discuss all the components of the proposed model and the reasons to trust in human-AI interactions in an example of relevance for business organizations. We end this contribution with an analysis of the epistemic and pragmatic reasons of trust in human-AI interactions and with a discussion of kinds of normativity in trustworthiness of AIs.

Effects of Build Orientations on Tensile Properties of PLA Material Processed by FDM

2014 ◽  
Vol 1044-1045 ◽  
pp. 31-34 ◽  
Author(s):  
Mst Faujiya Afrose ◽  
S.H. Masood ◽  
Mostafa Nikzad ◽  
Pio Iovenitti
Keyword(s):  
Tensile Properties ◽  
Low Cost ◽  
Testing Machine ◽  
Extrusion Process ◽  
Fused Deposition Modelling ◽  
Tensile Testing Machine ◽  
Thermoplastic Material ◽  
Fused Deposition ◽  
Dog Bone ◽  
3D Printers

Fused Deposition Modelling (FDM) of thermoplastic materials is generally a well-known technology among all additive manufacturing (AM) technologies and therefore, it is essential to investigate the mechanical properties of such FDM processed materials. Several open-source and low cost AM machines, known as 3D Printers, have recently been developed using thermoplastic extrusion process based on the original FDM technology. Many of these 3D Printers use Polylactic Acid (PLA) plastic for building parts. The main objective of this paper is to investigate the tensile properties of the PLA thermoplastic material processed by the Cube-2 3D Printer. In this study, the dog-bone sized PLA specimens are printed in different build orientations and a Zwick Z010 tensile testing machine is used to determine the tensile properties of PLA in different build orientation.

scholarly journals Hybrid Machine Learning Model for Body Fat Percentage Prediction Based on Support Vector Regression and Emotional Artificial Neural Networks

2021 ◽  
Vol 11 (21) ◽  
pp. 9797
Author(s):  
Solaf A. Hussain ◽  
Nadire Cavus ◽  
Boran Sekeroglu
Keyword(s):  
Machine Learning ◽  
Body Fat ◽  
Support Vector ◽  
Body Fat Percentage ◽  
Learning Models ◽  
Fat Percentage ◽  
Machine Learning Model ◽  
Proposed Model ◽  
Hybrid Machine ◽  
Machine Learning Models

Obesity or excessive body fat causes multiple health problems and diseases. However, obesity treatment and control need an accurate determination of body fat percentage (BFP). The existing methods for BFP estimation require several procedures, which reduces their cost-effectivity and generalization. Therefore, developing cost-effective models for BFP estimation is vital for obesity treatment. Machine learning models, particularly hybrid models, have a strong ability to analyze challenging data and perform predictions by combining different characteristics of the models. This study proposed a hybrid machine learning model based on support vector regression and emotional artificial neural networks (SVR-EANNs) for accurate recent BFP prediction using a primary BFP dataset. SVR was applied as a consistent attribute selection model on seven properties and measurements, using the left-out sensitivity analysis, and the regression ability of the EANN was considered in the prediction phase. The proposed model was compared to seven benchmark machine learning models. The obtained results show that the proposed hybrid model (SVR-EANN) outperformed other machine learning models by achieving superior results in the three considered evaluation metrics. Furthermore, the proposed model suggested that abdominal circumference is a significant factor in BFP prediction, while age has a minor effect.

scholarly journals Redistribution and Rekognition

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Sarah Myers West
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Learning Community ◽  
Statistical Methods ◽  
Gender Discrimination ◽  
Human Life ◽  
Human Interaction ◽  
The People ◽  
Computer Scientists ◽  
Global Population

Computer scientists, and artificial intelligence researchers in particular, have a predisposition for adopting precise, fixed definitions to serve as classifiers (Agre, 1997; Broussard, 2018). But classification is an enactment of power; it orders human interaction in ways that produce advantage or suffering (Bowker & Star, 1999). In so doing, it obscures the messiness of human life, masking the work of the people involved in training machine learning systems, and hiding the uneven distribution of its impacts on communities (Taylor, 2018; Gray, 2019; Roberts, 2019). Feminist scholars, and particularly feminist scholars of color, have made powerful critiques of the ways in which artificial intelligence systems formalize, classify, and amplify historical forms of discrimination and act to reify and amplify existing forms of social inequality (Eubanks, 2017; Benjamin, 2019; Noble, 2018). In response, the machine learning community has begun to address claims of algorithmic bias under the rubric of fairness, accountability, and transparency. But in doing so, it has largely dealt with these issues in familiar terms, using statistical methods aimed at achieving parity and deploying fairness ‘toolkits’. Yet actually existing inequality is reflected and amplified in algorithmic systems in ways that exceed the capacity of statistical methods alone. This article outlines a feminist critique of extant methods of dealing with algorithmic discrimination. I outline the ways in which gender discrimination and erasure are built into the field of AI at a foundational level; the product of a community that largely represents a small, privileged, and male segment of the global population (Author, 2019). In so doing, I illustrate how a situated mode of inquiry enables us to more closely examine a feedback loop between discriminatory workplaces and discriminatory systems.

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