Cover -- Title Page -- Copyright Page -- Contents -- Preface -- Chapter 1. New Intelligence Method for Machine Tool Energy Consumption Estimation -- 1.1. Introduction -- 1.2. Mathematical model for estimating power consumption by the spindle and table of a mill -- 1.3. ICA method -- 1.3.1. Pretreatment of signals -- 1.3.2. Separation -- 1.4. Results and discussion -- 1.5. Conclusion -- 1.6. References -- Chapter 2. Uncertainty and Artificial Intelligence: Applications to Maintenance in Additive Manufacturing -- 2.1. Introduction -- 2.2. Integration of uncertainty
2.2.1. Definition of uncertainty -- 2.2.2. Types of uncertainty -- 2.3. Uncertainty in artificial intelligence -- 2.3.1. Concepts and challenges in artificial intelligence -- 2.3.2. Implementing artificial intelligence -- 2.3.3. Decision-making in the case of uncertainty -- 2.4. Artificial intelligence in additive manufacturing -- 2.4.1. Notions and issues in additive manufacturing -- 2.4.2. Uncertainty in additive manufacturing -- 2.4.3. Probable failure scenarios in additive manufacturing -- 2.4.4. Integration of artificial intelligence -- 2.5. Predictive maintenance in additive manufacturing
2.5.1. Tendencies in maintenance technology -- 2.5.2. Diagnostic and prognostic models -- 2.5.3. Implementation in additive manufacturing -- 2.6. Proposed strategy and applications -- 2.6.1. Examples of uncertainty in additive manufacturing -- 2.6.2. Advanced system of quality control in additive manufacturing -- 2.7. Conclusion -- 2.8. References -- Chapter 3. Bio-Composite Structural Durability: Using Artificial Intelligence for Cluster Classification -- 3.1. Introduction -- 3.2. The state of acoustic emission technology -- 3.2.1. Definition -- 3.2.2. Applications of acoustic emissions
3.2.3. Mechanisms responsible for AE in materials -- 3.2.4. Propagation of waves through materials -- 3.2.5. Phenomenological approach to acoustic emissions -- 3.3. The concept behind classification -- 3.3.1. Introduction -- 3.3.2. Statistical analysis in the identification of different source mechanisms -- 3.3.3. Classification method -- 3.4. Application to the classification of damage mechanisms in agro-composites -- 3.4.1. Fabrication of agro-composite test tubes -- 3.4.2. Characterization of tensile tests -- 3.4.3. Classification of damage mechanisms
3.5. Artificial neural networks for classifying damage -- 3.5.1. Introduction -- 3.5.2. Organization of neurons -- 3.5.3. How the neural network functions -- 3.5.4. Results and analysis -- 3.6. Conclusion -- 3.7. References -- Chapter 4. Intelligent Control for Attenuating Vertical Vibrations in Vehicles -- 4.1. Introduction -- 4.2. Limits of passive and semi-active control strategies -- 4.2.1. Limits of passive suspension -- 4.2.2. Limits of semi-active suspension -- 4.3. Model-free control (MFC) -- 4.3.1. Ultra-local model -- 4.3.2. Online estimates of modeled and non-modeled perturbances
