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
Dr. Carlos Alberto Escobar worked as a research scientist at the Amazon Last Mile Delivery and Technology organization and as a senior researcher at the Manufacturing Systems Research Lab of General Motors, Global Research and Development. He also worked as Faculty Aide at Harvard Extension School. Dr. Escobar obtained his Ph.D. in engineering sciences with a concentration in artificial intelligence (2019) and a master’s degree in quality engineering (2005) from Tecnológico de Monterrey. He also obtained a master’s in industrial engineering (2016) from New Mexico State University. He is an industrial engineer (2001) from Instituto Tecnológico de Ciudad Juarez. Currently, he studies a master’s in management (2024) at Harvard Extension School. He has published over 30 scientific articles in top journals. His research topic has been presented in top conferences, including the American Society of Quality. According to a published bibliometric study, he is considered one of the most cited and fruitful authors in Quality 4.0 (2022). The interest in his publications (2023) lies in the 99% at the Research Gate platform compared to his cohort of researcher registered in 2015. Dr. Escobar was recognized as the SHPE STAR of Today (2021) by the Society of Hispanic Professional Engineers, the largest association of Hispanic in STEM in the U.S. Dr. Escobar was in the Mexican national team of martial arts, he was inducted into the Hall of Fame of Ciudad Juarez (2015) after his retirement. Today, he enjoys teaching his colleagues this sport.
Affiliations and expertise
Research scientist, School of Engineering and Sciences, Tecnologico de Monterrey, MexicoRM
Ruben Morales-Menendez
Dr. Morales is a chemical and systems engineer (1984) with a master's degree in chemical engineering (1986) and in control engineering (1992) from the Tecnológico de Monterrey (México). He obtained a Ph.D. in Artificial Intelligence while staying at the Computational Intelligence Laboratory at the University of British Columbia in Vancouver, Canada (2003). Dr. Morales co-founded the Industrial Automation Center (1987) at the Tecnológico de Monterrey. He was awarded the Prize for Teaching and Research (1993 and 2005). As a consultant specializing in analyzing and designing control systems, he carried out projects with more than 20 national and international companies. He was classified as a consultant and extensionist full professor (1998). He has worked at the International Federation of Automatic Control (IFAC), organizing the IFAC-CEA (2007) congress and the IFAC-SAFEPROCESS (2012) symposium. Through international research projects, he has advised doctoral theses at the Institute of Industrial Automation (Spain) and the Institut Polytechnique de Grenoble (Gipsa-Lab, France). He was a member of the Board of Directors of the Sectoral Fund for Research and Development in Naval Sciences (2010-2020). The Mexican System of Researchers accredits his scientific production as Level 2 (2014). He is a member of the Mexican Academy of Sciences (2015) and the Mexican Academy of Engineering (2016). He was Associate Research Director (2007) and Academic Vice-Director (2009). He is the National Director of Graduate Studies at the School of Engineering and Sciences (2014) at Tecnológico de Monterrey.
Affiliations and expertise
Dean of Graduate Studies, School of Engineering and Sciences, Tecnologico de Monterrey, MexicoRead Machine Learning in Manufacturing on ScienceDirect