Machine Learning in Manufacturing
Quality 4.0 and the Zero Defects Vision
- 1st Edition - March 17, 2024
- Authors: Carlos A. Escobar, Ruben Morales-Menendez
- Language: English
- Paperback ISBN:9 7 8 - 0 - 3 2 3 - 9 9 0 2 9 - 5
- eBook ISBN:9 7 8 - 0 - 3 2 3 - 9 9 0 3 0 - 1
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
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Request a sales quote- 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
- Cover image
- Title page
- Table of Contents
- Copyright
- Biographies
- Preface
- Chapter 1. Introduction
- 1.1. Motivation
- 1.2. Smart manufacturing
- 1.3. Evolution of modern quality control in manufacturing
- 1.4. Breakdown of traditional quality control methods
- 1.5. The rise of quality 4.0
- Chapter 2. The technologies
- 2.1. Artificial intelligence
- 2.2. Cloud storage and computing
- 2.3. Industrial internet of things
- Chapter 3. The data
- 3.1. Big data
- 3.2. Manufacturing big data
- 3.3. Transforming big data into a learning data
- 3.4. Binary classification of quality data sets
- Chapter 4. Classification
- 4.1. Binary Classification
- 4.2. Binary classification of quality
- 4.3. Classification errors
- 4.4. Empirical case study on classification metrics
- 4.5. Binary patterns
- Chapter 5. Machine learning theory
- 5.1. Overfitting and underfitting
- 5.2. Learning curves
- 5.3. Curse of dimensionality
- 5.4. Early stopping
- 5.5. Loss function for binary classification
- 5.6. Summary
- Chapter 6. Feature engineering
- 6.1. Feature creation
- 6.2. Feature selection
- 6.3. Feature visualization
- 6.4. Feature preprocessing
- 6.5. Summary
- Chapter 7. Classifier development
- 7.1. Modeling paradigms
- 7.2. Machine-learning algorithms
- 7.3. Classifier fusion
- 7.4. Data-driven insights
- 7.5. Manufacturing pattern-recognition problem
- 7.6. Summary
- Chapter 8. Learning quality control
- 8.1. Definition
- 8.2. Applications
- 8.3. Problem-solving strategy
- 8.4. Boosting statistical process control
- 8.5. Managerial implications
- 8.6. Summary
- Chapter 9. Case studies
- 9.1. Case study 1—Structured data
- 9.2. Case study 2—Unstructured data
- Chapter 10. Conclusions and call to action
- 10.1. Call to action
- Appendices
- Index
- No. of pages: 300
- Language: English
- Edition: 1
- Published: March 17, 2024
- Imprint: Elsevier
- Paperback ISBN: 9780323990295
- eBook ISBN: 9780323990301
CE
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.
RM
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.