INTRODUCTION TO COMPOSITE MATERIALS Chapter Objectives Introduction Classi?cation Recycling Fiber-Reinforced Composites Mechanics Terminology Summary Key Terms Exercise Set References MACROMECHANICAL ANALYSIS OF A LAMINA Chapter Objectives Introduction Review of De?nitions Hooke's Law for Different Types of Materials Hooke's Law for a Two-Dimensional Unidirectional Lamina Hooke's Law for a Two-Dimensional Angle Lamina Engineering Constants of an Angle Lamina Invariant Form of Stiffness and Compliance Matrices for an Angle Lamina Strength Failure Theories of an Angle Lamina Hygrothermal Stresses and Strains in a Lamina Summary Key Terms Exercise Set References APPENDIX A: MATRIX ALGEBRA Key Terms APPENDIX B: TRANSFORMATION OF STRESSES AND STRAINS Transformation of Stress Transformation of Strains Key Terms MICROMECHANICAL ANALYSIS OF A LAMINA Chapter Objectives Introduction Volume and Mass Fractions, Density, and Void Content Evaluation of the Four Elastic Moduli Ultimate Strengths of a Unidirectional Lamina Coefficients of Thermal Expansion Coefficients of Moisture Expansion Summary Key Terms Exercise Set References MACROMECHANICAL ANALYSIS OF LAMINATES Chapter Objectives Introduction Laminate Code Stress-Strain Relations for a Laminate In-Plane and Flexural Modulus of a Laminate Hygrothermal Effects in a Laminate Summary Key Terms Exercise Set References FAILURE, ANALYSIS, AND DESIGN OF LAMINATES Chapter Objectives Introduction Special Cases of Laminates Failure Criterion for a Laminate Design of a Laminated Composite Other Mechanical Design Issues Summary Key Terms Exercise Set References BENDING OF BEAMS Chapter Objectives Introduction Symmetric Beams Nonsymmetric Beams Summary Key Terms Exercise Set References INDEX

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Mechanics of composite materials

Fused deposition modelling (FDM) is one of the fastest-growing additive manufacturing methods used in printing fibre-reinforced composites (FRC). The performances of the resulting printed parts are limited compared to those by other manufacturing methods due to their inherent defects. Hence, the effort to develop treatment methods to overcome these drawbacks has accelerated during the past few years. The main focus of this study is to review the impact of those defects on the mechanical performance of FRC and therefore to discuss the available treatment methods to eliminate or minimize them in order to enhance the functional properties of the printed parts. As FRC is a combination of polymer matrix material and continuous or short reinforcing fibres, this review will thoroughly discuss both thermoplastic polymers and FRCs printed via FDM technology, including the effect of printing parameters such as layer thickness, infill pattern, raster angle and fibre orientation. The most common defects on printed parts, in particular, the void formation, surface roughness and poor bonding between fibre and matrix, are explored. An inclusive discussion on the effectiveness of chemical, laser, heat and ultrasound treatments to minimize these drawbacks is provided by this review.

FDM-Based 3D Printing of Polymer and Associated Composite: A Review on Mechanical Properties, Defects and Treatments.

Recent progress on polymer materials for additive manufacturing

Additive manufacturing (AM), also known as three-dimensional (3D) printing, has boomed over the last 30 years, and its use has accelerated during the last 5 years AM is a materials-oriented manufacturing technology, and printing resolution versus printing scalability/speed trade-off exists among various types of materials, including polymers, metals, ceramics, glasses, and composite materials Four-dimensional (4D) printing, together with versatile transformation systems, drives researchers to achieve and utilize high dimensional AM Multiple perspectives of the AM of structural materials have been raised and illustrated in this review, including multi-material AM (MMa-AM), multi-modulus AM (MMo-AM), multi-scale AM (MSc-AM), multi-system AM (MSy-AM), multi-dimensional AM (MD-AM), and multi-function AM (MF-AM) The rapid and tremendous development of AM materials and methods offers great potential for structural applications, such as in the aerospace field, the biomedical field, electronic devices, nuclear industry, flexible and wearable devices, soft sensors, actuators, and robotics, jewelry and art decorations, land transportation, underwater devices, and porous structures

Additive manufacturing of structural materials

In this study, a new technique is proposed to forecast short-term electrical load. Load forecasting is an integral part of power system planning and operation. Precise forecasting of load is essential for unit commitment, capacity planning, network augmentation and demand side management. Load forecasting can be generally categorized into three classes such as short-term, midterm and long-term. Short-term forecasting is usually done to predict load for next few hours to few weeks. In the literature, various methodologies such as regression analysis, machine learning approaches, deep learning methods and artificial intelligence systems have been used for short-term load forecasting. However, existing techniques may not always provide higher accuracy in short-term load forecasting. To overcome this challenge, a new approach is proposed in this paper for short-term load forecasting. The developed method is based on the integration of convolutional neural network (CNN) and long short-term memory (LSTM) network. The method is applied to Bangladesh power system to provide short-term forecasting of electrical load. Also, the effectiveness of the proposed technique is validated by comparing the forecasting errors with that of some existing approaches such as long short-term memory network, radial basis function network and extreme gradient boosting algorithm. It is found that the proposed strategy results in higher precision and accuracy in short-term load forecasting.

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A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network

The nature of stock market movement has always been ambiguous for investors because of various influential factors. This study aims to significantly reduce the risk of trend prediction with machine learning and deep learning algorithms. Four stock market groups, namely diversified financials, petroleum, non-metallic minerals and basic metals from Tehran stock exchange, are chosen for experimental evaluations. This study compares nine machine learning models (Decision Tree, Random Forest, Adaptive Boosting (Adaboost), eXtreme Gradient Boosting (XGBoost), Support Vector Classifier (SVC), Naive Bayes, K-Nearest Neighbors (KNN), Logistic Regression and Artificial Neural Network (ANN)) and two powerful deep learning methods (Recurrent Neural Network (RNN) and Long short-term memory (LSTM). Ten technical indicators from ten years of historical data are our input values, and two ways are supposed for employing them. Firstly, calculating the indicators by stock trading values as continuous data, and secondly converting indicators to binary data before using. Each prediction model is evaluated by three metrics based on the input ways. The evaluation results indicate that for the continuous data, RNN and LSTM outperform other prediction models with a considerable difference. Also, results show that in the binary data evaluation, those deep learning methods are the best; however, the difference becomes less because of the noticeable improvement of models' performance in the second way.

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Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

The prediction of stock groups values has always been attractive and challenging for shareholders due to its inherent dynamics, non-linearity, and complex nature. This paper concentrates on the future prediction of stock market groups. Four groups named diversified financials, petroleum, non-metallic minerals, and basic metals from Tehran stock exchange were chosen for experimental evaluations. Data were collected for the groups based on 10 years of historical records. The value predictions are created for 1, 2, 5, 10, 15, 20, and 30 days in advance. Various machine learning algorithms were utilized for prediction of future values of stock market groups. We employed decision tree, bagging, random forest, adaptive boosting (Adaboost), gradient boosting, and eXtreme gradient boosting (XGBoost), and artificial neural networks (ANN), recurrent neural network (RNN) and long short-term memory (LSTM). Ten technical indicators were selected as the inputs into each of the prediction models. Finally, the results of the predictions were presented for each technique based on four metrics. Among all algorithms used in this paper, LSTM shows more accurate results with the highest model fitting ability. In addition, for tree-based models, there is often an intense competition between Adaboost, Gradient Boosting, and XGBoost.

Deep Learning for Stock Market Prediction

An Experimental Study of Nozzle Temperature and Heat Treatment (Annealing) Effects on Mechanical Properties of High‐Temperature Polylactic Acid in Fused Deposition Modeling

The intermittent and uncertain nature of wind places a premium on accurate wind power forecasting for the reliable and efficient operation of power grids with large-scale wind power penetration. Herein, six-month-ahead wind power forecasting models were developed using tree-based learning algorithms. Three models were developed to investigate the impact of input data on forecasting accuracy. The first model was trained with the average and standard deviation of wind speed values measured at a height of 40 m with a 10-min sampling time. To evaluate the impact of sampling time on model performance, a second model was trained with wind speed values measured at a height of 40 m with 1-h, 12-h, and 24-h sampling times. To assess the effect of measuring height on model accuracy, the third model was trained with wind speed values measured at 40 m extrapolated from values measured at heights of 30 m and 10 m. Experiments revealed that using longer time intervals and height extrapolation leads to considerable accuracy degradation in forecasted models. Finally, to study the generalization ability of the forecasted models, they were tested against wind data measured at heights and locations different from what the models had been trained with. Simulation results substantiated that tree-based learning algorithms can be successfully adopted not only for long-term wind power forecasting, but for potential wind power forecasting at different heights and geographical locations.

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Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms

Recently, applications of three-dimensional (3-D) printers have extensively been increased in various industries. Fused deposition modeling process is one of the most widely used 3-D printing metho...

Mojtaba Nabipour

Papers

Predicting Stock Market Trends Using Machine Learning and Deep Learning Algorithms Via Continuous and Binary Data; a Comparative Analysis

Deep Learning for Stock Market Prediction

An Experimental Study of Nozzle Temperature and Heat Treatment (Annealing) Effects on Mechanical Properties of High‐Temperature Polylactic Acid in Fused Deposition Modeling

Long-Term Wind Power Forecasting Using Tree-Based Learning Algorithms

An experimental study of FDM parameters effects on tensile strength, density, and production time of ABS/Cu composites: