Machine Learning in Manufacturing
Quality 4.0 and the Zero Defects Vision
- 1st Edition - March 22, 2024
- Latest edition
- Authors: Carlos A. Escobar, Ruben Morales-Menendez
- Language: English
Machine Learning in Manufacturing: Quality 4.0 and the Zero Defects Vision reviews process monitoring based on machine learning algorithms and the technologies of the fourth… Read more
Machine Learning in Manufacturing: Quality 4.0 and the Zero Defects Vision reviews process monitoring based on machine learning algorithms and the technologies of the fourth industrial revolution and proposes Learning Quality Control (LQC), the evolution of Statistical Quality Control (SQC). This book identifies 10 big data issues in manufacturing and addresses them using an ad-hoc, 5-step problem-solving strategy that increases the likelihood of successfully deploying this Quality 4.0 initiative. With two case studies using structured and unstructured data, this book explains how to successfully deploy AI in manufacturing and how to move quality standards forward by developing virtually defect-free processes. This book enables engineers to identify Quality 4.0 applications and manufacturing companies to successfully implement Quality 4.0 practices.
- Provides an understanding of the most relevant challenges posed to the application of Artificial Intelligence (AI) in manufacturing
- Includes analytical developments and applications and merges a quality vision with machine learning algorithms
- Features structured and unstructured data case studies to illustrate how to develop intelligent monitoring systems with the capacity to replace manual and visual tasks
Professional engineers, managers, and directors, Graduate students, the book will also include details for obtaining a green, black, and master black belt certification, can be used also by certifications institutions
1. Introduction
2. The technologies
3. The data
4. Binary classification
5. Machine learning
6. Feature engineering
7. Classifier development
8. Learning quality control
9. Case studies; structured and unstructured data
10. Conclusion and call to action
2. The technologies
3. The data
4. Binary classification
5. Machine learning
6. Feature engineering
7. Classifier development
8. Learning quality control
9. Case studies; structured and unstructured data
10. Conclusion and call to action
- Edition: 1
- Latest edition
- Published: March 22, 2024
- Language: English
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Carlos A. Escobar
Carlos A. Escobar is a recognized expert in industrial artificial intelligence with 15+ years of experience leading machine learning innovations across aerospace, automotive, and logistics. As a Sr. Machine Learning Principal Engineer at Howmet Aerospace, Carlos is pioneering the use of Generative AI, autoencoders, and diffusion models to drive zero-defect manufacturing. His work bridges research and real-world deployment, drawing on prior experience developing AI systems at Amazon for last-mile logistics and leading AI-driven process optimization at General Motors.
Carlos also served as a Research Assistant at Harvard University, contributing to projects at the intersection of AI, education, and innovation. He is the author of the book Machine Learning in Manufacturing: Quality 4.0 and the Zero Defects Vision (Elsevier, 2024), and his second book, Alpha Sigma: Artificial Intelligence Manufacturing, is under development. He has delivered keynotes at Reuters Momentum AI, the American Society for Quality (ASQ), and the Society of Quality Assurance (SQA), and currently serves as an adjunct professor in Trine University's MS in Business Analytics program.
Affiliations and expertise
Howmet AerospaceRM
Ruben Morales-Menendez
Dr. Morales-Menendez obtained a Ph.D. in AI while at the Computational Intelligence Lab at the University of British Columbia, Canada (2003). As a consultant specializing in analysis and design of control systems, he has carried out projects with more than 20 international companies. Through international research projects, he has advised doctoral students at the institute of industrial automation (Spain) and the Institut Polytechnique de Grenoble (Gipsa-Lab, France). The Mexican Research System accredits his scientific production as Level 2 (2014) with more than 250 research papers. Dr. Morales-Menendez is listed in the Elsevier and Stanford University Top 2 % Scientists list in the Industrial Engineering & Automation (2023 & 2024). He is a member of the Mexican Academy of Sciences (2015) and the Mexican Academy of Engineering (2016).
Affiliations and expertise
Professor at Tecnológico de MonterreyRead Machine Learning in Manufacturing on ScienceDirect