Artificial Intelligence (AI) is currently one of the most talked-about technologies, both among scientists and in public media. Several factors have contributed to its development in recent years. The first is access to vast quantities of data, such as in the industrial field, the advent of Industry 4.0, which promotes automation and data sharing in several technologies. Another factor is the continuous improvement in computing power thanks to the development of ever more powerful processors and the optimization of algorithms. With these two limitations removed, the focus of most AI developments is on the quality of predictions. The integration of AI into the industrial domain represents an exciting new frontier for innovation. Just as AI has transformed many other sectors, its application to mechanical technologies enables significant improvements in design, manufacturing and quality control processes: from computer-aided design (CAD) to printing parameter optimization, defect detection and real-time monitoring. This type of technology requires computer systems, data with management systems and advanced algorithms which can be used by AIs. In mechanical engineering, AI offers many possibilities in mechanical construction, predictive maintenance, plant monitoring, robotics, additive manufacturing, materials, vibration, etc. Methods and Applications of Artificial Intelligence is dedicated to the methods and applications of AI in mechanical engineering. Each chapter clearly sets out the techniques used and developed and accompanies them with illustrative examples. The book is aimed at students but is also a valuable resource for practicing engineers and research lecturers.

Table of Contents:
Preface xi Abdelkhalak EL HAMI Chapter 1. Dynamic of PP-GF70 Using an Intelligent Method 1 Abdelilah ELBAZZE and Bouchaib RADI 1.1. Introduction 1 1.2. Crash analysis 3 1.2.1. Dynamic equations 3 1.2.2. Explicit simulation 7 1.2.3. Shell elements 11 1.3. Application to automobile crashes 12 1.3.1. Description of the study 12 1.3.2. Model preprocessing 13 1.3.3. Mesh generation according to ANSA 14 1.3.4. Materials and properties 14 1.3.5. Generation of part assembly connections 15 1.3.6. Assembly of components 16 1.3.7. Interfaces 17 1.3.8. Side crash with mobile barrier 18 1.4. Numerical results and discussions 21 1.4.1. Numerical stability of the model 21 1.4.2. Model behavior 23 1.5. Behavior of the target part during impact 24 1.5.1. Deformation of the target part 24 1.5.2. Plastic deformation 26 1.6. Conclusion 27 1.7. References 27 Chapter 2. AI Integration into Additive Manufacturing 31 Adnane ZOUBEIR, Bouchaib RADI and Abdelkhalak EL HAMI 2.1. Introduction 31 2.2. Additive manufacturing processes 32 2.2.1. Principle of additive manufacturing 32 2.2.2. Various stages of additive manufacturing 33 2.3. Integration of AI into additive manufacturing 35 2.3.1. Introduction 35 2.3.2. Feasibility of AI-based solutions 36 2.3.3. Training data 36 2.3.4. Prediction models and selection of the proper solution 37 2.4. Development of an AI model for additive manufacturing 37 2.4.1. Introduction 37 2.4.2. Machine learning and deep learning 38 2.4.3. Data preparation for training an AI model 38 2.4.4. AI model training 39 2.4.5. Performance of the AI model prediction 41 2.4.6. Application to the optimization of manufacturing parameters 41 2.5. Conclusion 43 2.6. References 44 Chapter 3. Optimization of BGA Components under Real Service Conditions 47 Sinda GHENAM, Abdelkhalak EL HAMI, Wajih GAFSI, Ali AKROUT and Mohamed HADDAR 3.1. Introduction 47 3.2. Presentation of the optimization method 49 3.3. Presentation of the system 50 3.4. Methodology 51 3.4.1. Deterministic design optimization (DDO) 51 3.4.2. Reliability-based design optimization (RBDO) 52 3.5. Results 55 3.5.1. DDO 55 3.5.2. RBDO 57 3.6. Conclusion 60 3.7. References 61 Chapter 4. Electronic Toll Collection in the Age of Connected Vehicles 65 Adnane CABANI and Houcine CHAFOUK 4.1. Introduction 65 4.2. Electronic toll collection service using ITS-G5 66 4.2.1. Constant velocity (CV) model 69 4.2.2. Constant acceleration (CA) model 70 4.2.3. Constant turn rate and velocity (CTRV) model 70 4.2.4. Constant turn rate and acceleration (CTRA) model 71 4.3. Experimental results of the path tracking 72 4.4. Conclusion 74 4.5. References 74 Chapter 5. At the Core of Artificial Intelligence: Leveraging Machine Learning through Random Forest 77 Anas EL ATTAOUI, Younes KOULOU and Norelislam EL HAMI 5.1. Overview of AI and its branches 78 5.1.1. AI and machine learning 78 5.1.2. Robust techniques are required for classification and regression 79 5.1.3. Introduction to the random forest method 79 5.2. Decision trees 79 5.2.1. Structure of a decision tree and associated terms 79 5.2.2. Entropy 82 5.2.3. Information gain 83 5.2.4. Gini index 83 5.2.5. Decision tree generation algorithm 84 5.2.6. Example of decision tree construction 86 5.2.7. Advantages and limitations of decision trees 93 5.3. Random forest foundations 93 5.3.1. Origin and basic principle of the random forest 93 5.3.2. Stages of random forest algorithm 94 5.3.3. Random forest example for classification. 96 5.3.4. Random forest example for regression 102 5.4. Case study: detection of malware on Android 110 5.4.1. Data presentation 110 5.4.2. Results 111 5.5. Chapter essentials 113 5.6. References 114 Chapter 6. Linear Regression and Application to Artificial Intelligence 121 Sara RHOUAS, Norelislam EL HAMI and Younes KOULOU 6.1. Introduction 121 6.2. Linear regression model 122 6.2.1. Objectives and use 122 6.2.2. Applications to everyday life 123 6.2.3. Simple model vs. multiple model 124 6.2.4. Regression coefficients 125 6.3. Methods for assessing coefficients 126 6.3.1. Ordinary least squares (OLS) method 126 6.3.2. Ridge regression 127 6.3.3. LASSO regression 128 6.3.4. Elastic Net regression 129 6.3.5. Weighted least squares 131 6.3.6. Linear regression by gradient descent 132 6.4. Model evaluation 133 6.4.1. Performance evaluation metrics 133 6.4.2. Cross-validation 136 6.4.3. Model diagnosis 139 6.5. Practical cases 141 6.5.1. Data preparation and analysis 141 6.5.2. Model training 147 6.6. Conclusion 151 6.7. References 152 Chapter 7. Machine Learning and Artificial Intelligence with XGBoost Algorithm for Binary Classification 161 Hakima REDDAD, Maria ZEMZAMI, Norelislam EL HAMI and Nabil HMINA 7.1. Introduction 161 7.2. XGBoost algorithm: state of the art 164 7.2.1. Anatomy of XGBoost algorithm 165 7.2.2. Main features of XGBoost algorithm 166 7.2.3. XGBoost operation 173 7.2.4. Theoretical basis of XGBoost algorithm 174 7.2.5. Feature importance 176 7.3. Case study: application of the XGBoost algorithm - resolution of a binary classification problem 177 7.3.1. Problem statement 177 7.3.2. Data preparation 178 7.3.3. Exploratory data analysis 179 7.3.4. Construction of the predictive model with XGBoost algorithm 180 7.3.5. Evaluation of the predictive model 181 7.4. Results and discussions 183 7.5. Conclusion 185 7.6. References 185 Chapter 8. Application of Interoperability to Intelligent System of Systems 193 Younes KOULOU, Norelislam EL HAMI, Anas EL ATTAOUI and Sara RHOUAS 8.1. General context of SoS 194 8.1.1. Definitions 194 8.1.2. SoS taxonomy 195 8.1.3. Properties of SoS 197 8.2. Concept of intelligent SoS 200 8.2.1. Intelligence in SoS 200 8.2.2. Architecture of intelligent SoS 201 8.3. Interoperability 203 8.3.1. Classification of interoperability. 204 8.4. Evaluation of interoperability as a basis for the structural analysis of intelligent SoS 206 8.4.1. Quantification of interoperability barriers 207 8.4.2. Inefficiency of communication 209 8.4.3. Application to the case study 212 8.5. Conclusion 215 8.6. References 216 List of Authors 223 Index 225