Advertisement

Artificial Cognitive Architecture with Self-Learning and Self-Optimization Capabilities

Case Studies in Micromachining Processes

  • Gerardo Beruvides
Book
  • 748 Downloads

Part of the Springer Theses book series (Springer Theses)

Table of contents

  1. Front Matter
    Pages i-xxix
  2. Gerardo Beruvides
    Pages 1-33
  3. Gerardo Beruvides
    Pages 35-82
  4. Back Matter
    Pages 155-195

About this book

Introduction

This book introduces three key issues: (i) development of a gradient-free method to enable multi-objective self-optimization; (ii) development of a reinforcement learning strategy to carry out self-learning and finally, (iii) experimental evaluation and validation in two micromachining processes (i.e., micro-milling and micro-drilling). The computational architecture (modular, network and reconfigurable for real-time monitoring and control) takes into account the analysis of different types of sensors, processing strategies and methodologies for extracting behavior patterns from representative process’ signals. The reconfiguration capability and portability of this architecture are supported by two major levels: the cognitive level (core) and the executive level (direct data exchange with the process). At the same time, the architecture includes different operating modes that interact with the process to be monitored and/or controlled. The cognitive level includes three fundamental modes such as modeling, optimization and learning, which are necessary for decision-making (in the form of control signals) and for the real-time experimental characterization of complex processes. In the specific case of the micromachining processes, a series of models based on linear regression, nonlinear regression and artificial intelligence techniques were obtained. On the other hand, the executive level has a constant interaction with the process to be monitored and/or controlled. This level receives the configuration and parameterization from the cognitive level to perform the desired monitoring and control tasks.

Keywords

Self-learning Self-optimization Micromachining Processes Computational Intelligence Models Self-adaptive control Q-learning Algorithm Multi-objective Cross-entropy Roughness Surface Model Sensors Expert Systems Cyber-physical Systems Self-decision-making Force Signal Processing Vibration Analysis Raspberry Implementation Distributed Control Architecture Fuzzy Controllers Predictive Models Industrial Use Case

Authors and affiliations

  • Gerardo Beruvides
    • 1
  1. 1.Centre for Automation and Robotic (CAR-CSIC)MadridSpain

Bibliographic information

Industry Sectors
Automotive
Chemical Manufacturing
Biotechnology
Electronics
IT & Software
Telecommunications
Aerospace
Pharma
Materials & Steel
Oil, Gas & Geosciences
Engineering