Machine learning in manufacturing: quality 4.0 and the zero defects vision
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Amsterdam
Elsevier
[2024]
|
| Online Access: | DE-1050 DE-706 Volltext |
| Physical Description: | 1 Online-Ressource (x, 236 Seiten) |
| ISBN: | 9780323990301 |
| DOI: | 10.1016/C2021-0-01383-3 |
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| 505 | 8 | |a Front Cover -- Machine Learning in Manufacturing -- Machine Learning in Manufacturing -- Copyright -- Contents -- Biographies -- Preface -- 1 -- Introduction -- 1.1 Motivation -- 1.2 Smart manufacturing -- 1.2.1 Technologies -- 1.2.2 Building blocks -- 1.2.3 Characteristics -- 1.2.4 Research areas -- 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 -- 2 -- The technologies -- 2.1 Artificial intelligence -- 2.1.1 Machine learning -- 2.1.1.1 Desirable characteristics of ML projects | |
| 505 | 8 | |a 2.1.2 ChatGPT for quality 4.0 -- 2.2 Cloud storage and computing -- 2.2.1 Cloud framework for quality 4.0 -- 2.2.2 Data lake, data warehouse, and database -- 2.3 Industrial internet of things -- 2.3.1 The manufacturing things -- 2.3.1.1 Smart sensors -- 2.3.1.2 Actuators -- 2.3.2 Process monitoring and quality control in manufacturing -- 3 -- The data -- 3.1 Big data -- 3.2 Manufacturing big data -- 3.3 Transforming big data into a learning data -- 3.3.1 Data frame -- 3.3.2 Learning data sets -- 3.3.2.1 Recommendations on data splits -- 3.3.2.2 Type of data | |
| 505 | 8 | |a 3.4 Binary classification of quality data sets -- 3.4.1 Data annotation -- 3.4.2 BCoQ data sets examples -- 4 -- Classification -- 4.1 Binary Classification -- 4.2 Binary classification of quality -- 4.3 Classification errors -- 4.3.1 Alpha and beta errors -- 4.3.2 Metrics of classification performance -- 4.4 Empirical case study on classification metrics -- 4.5 Binary patterns -- 4.5.1 Discriminative information -- 4.5.1.1 Virtual case study one-3D pattern -- 4.5.1.2 Virtual case study two-separation -- 5 -- Machine learning theory -- 5.1 Overfitting and underfitting -- 5.2 Learning curves | |
| 505 | 8 | |a 5.3 Curse of dimensionality -- 5.4 Early stopping -- 5.5 Loss function for binary classification -- 5.6 Summary -- 6 -- Feature engineering -- 6.1 Feature creation -- 6.2 Feature selection -- 6.3 Feature visualization -- 6.4 Feature preprocessing -- 6.4.1 Feature imputation -- 6.4.2 Feature permutation -- 6.4.3 Outlier analysis -- 6.4.4 Feature transformation -- 6.5 Summary -- 7 -- Classifier development -- 7.1 Modeling paradigms -- 7.1.1 Statistical modeling versus machine-learning modeling -- 7.1.2 Observational data versus experimental data -- 7.1.3 Data snooping | |
| 505 | 8 | |a 7.1.4 Accuracy versus interpretability -- 7.1.5 Model selection -- 7.2 Machine-learning algorithms -- 7.2.1 Hyperparameter optimization -- 7.2.2 Artificial neural networks -- 7.2.2.1 Fully connected networks -- 7.2.2.2 Convolutional neural networks -- 7.2.3 Regularization for artificial neural networks -- 7.2.3.1 Dropout -- 7.2.3.2 L1 regularization -- 7.2.3.3 L2 regularization -- 7.2.4 Logistic regression -- 7.2.5 Tree models -- 7.2.5.1 Random forest -- 7.2.6 XGBoost -- 7.2.7 Classification threshold definition -- 7.2.7.1 ROC curve for finding the optimal threshold -- 7.3 Classifier fusion | |
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| author | Escobar Belalcázar, Carlos Arnulfo Morales-Menendez, Ruben |
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| contents | Front Cover -- Machine Learning in Manufacturing -- Machine Learning in Manufacturing -- Copyright -- Contents -- Biographies -- Preface -- 1 -- Introduction -- 1.1 Motivation -- 1.2 Smart manufacturing -- 1.2.1 Technologies -- 1.2.2 Building blocks -- 1.2.3 Characteristics -- 1.2.4 Research areas -- 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 -- 2 -- The technologies -- 2.1 Artificial intelligence -- 2.1.1 Machine learning -- 2.1.1.1 Desirable characteristics of ML projects 2.1.2 ChatGPT for quality 4.0 -- 2.2 Cloud storage and computing -- 2.2.1 Cloud framework for quality 4.0 -- 2.2.2 Data lake, data warehouse, and database -- 2.3 Industrial internet of things -- 2.3.1 The manufacturing things -- 2.3.1.1 Smart sensors -- 2.3.1.2 Actuators -- 2.3.2 Process monitoring and quality control in manufacturing -- 3 -- The data -- 3.1 Big data -- 3.2 Manufacturing big data -- 3.3 Transforming big data into a learning data -- 3.3.1 Data frame -- 3.3.2 Learning data sets -- 3.3.2.1 Recommendations on data splits -- 3.3.2.2 Type of data 3.4 Binary classification of quality data sets -- 3.4.1 Data annotation -- 3.4.2 BCoQ data sets examples -- 4 -- Classification -- 4.1 Binary Classification -- 4.2 Binary classification of quality -- 4.3 Classification errors -- 4.3.1 Alpha and beta errors -- 4.3.2 Metrics of classification performance -- 4.4 Empirical case study on classification metrics -- 4.5 Binary patterns -- 4.5.1 Discriminative information -- 4.5.1.1 Virtual case study one-3D pattern -- 4.5.1.2 Virtual case study two-separation -- 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 -- 6 -- Feature engineering -- 6.1 Feature creation -- 6.2 Feature selection -- 6.3 Feature visualization -- 6.4 Feature preprocessing -- 6.4.1 Feature imputation -- 6.4.2 Feature permutation -- 6.4.3 Outlier analysis -- 6.4.4 Feature transformation -- 6.5 Summary -- 7 -- Classifier development -- 7.1 Modeling paradigms -- 7.1.1 Statistical modeling versus machine-learning modeling -- 7.1.2 Observational data versus experimental data -- 7.1.3 Data snooping 7.1.4 Accuracy versus interpretability -- 7.1.5 Model selection -- 7.2 Machine-learning algorithms -- 7.2.1 Hyperparameter optimization -- 7.2.2 Artificial neural networks -- 7.2.2.1 Fully connected networks -- 7.2.2.2 Convolutional neural networks -- 7.2.3 Regularization for artificial neural networks -- 7.2.3.1 Dropout -- 7.2.3.2 L1 regularization -- 7.2.3.3 L2 regularization -- 7.2.4 Logistic regression -- 7.2.5 Tree models -- 7.2.5.1 Random forest -- 7.2.6 XGBoost -- 7.2.7 Classification threshold definition -- 7.2.7.1 ROC curve for finding the optimal threshold -- 7.3 Classifier fusion |
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| spelling | Escobar Belalcázar, Carlos Arnulfo Verfasser (DE-588)1056632054 aut Machine learning in manufacturing quality 4.0 and the zero defects vision Carlos A. Escobar, Ruben Morales-Menendez Amsterdam Elsevier [2024] 1 Online-Ressource (x, 236 Seiten) txt rdacontent c rdamedia cr rdacarrier Front Cover -- Machine Learning in Manufacturing -- Machine Learning in Manufacturing -- Copyright -- Contents -- Biographies -- Preface -- 1 -- Introduction -- 1.1 Motivation -- 1.2 Smart manufacturing -- 1.2.1 Technologies -- 1.2.2 Building blocks -- 1.2.3 Characteristics -- 1.2.4 Research areas -- 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 -- 2 -- The technologies -- 2.1 Artificial intelligence -- 2.1.1 Machine learning -- 2.1.1.1 Desirable characteristics of ML projects 2.1.2 ChatGPT for quality 4.0 -- 2.2 Cloud storage and computing -- 2.2.1 Cloud framework for quality 4.0 -- 2.2.2 Data lake, data warehouse, and database -- 2.3 Industrial internet of things -- 2.3.1 The manufacturing things -- 2.3.1.1 Smart sensors -- 2.3.1.2 Actuators -- 2.3.2 Process monitoring and quality control in manufacturing -- 3 -- The data -- 3.1 Big data -- 3.2 Manufacturing big data -- 3.3 Transforming big data into a learning data -- 3.3.1 Data frame -- 3.3.2 Learning data sets -- 3.3.2.1 Recommendations on data splits -- 3.3.2.2 Type of data 3.4 Binary classification of quality data sets -- 3.4.1 Data annotation -- 3.4.2 BCoQ data sets examples -- 4 -- Classification -- 4.1 Binary Classification -- 4.2 Binary classification of quality -- 4.3 Classification errors -- 4.3.1 Alpha and beta errors -- 4.3.2 Metrics of classification performance -- 4.4 Empirical case study on classification metrics -- 4.5 Binary patterns -- 4.5.1 Discriminative information -- 4.5.1.1 Virtual case study one-3D pattern -- 4.5.1.2 Virtual case study two-separation -- 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 -- 6 -- Feature engineering -- 6.1 Feature creation -- 6.2 Feature selection -- 6.3 Feature visualization -- 6.4 Feature preprocessing -- 6.4.1 Feature imputation -- 6.4.2 Feature permutation -- 6.4.3 Outlier analysis -- 6.4.4 Feature transformation -- 6.5 Summary -- 7 -- Classifier development -- 7.1 Modeling paradigms -- 7.1.1 Statistical modeling versus machine-learning modeling -- 7.1.2 Observational data versus experimental data -- 7.1.3 Data snooping 7.1.4 Accuracy versus interpretability -- 7.1.5 Model selection -- 7.2 Machine-learning algorithms -- 7.2.1 Hyperparameter optimization -- 7.2.2 Artificial neural networks -- 7.2.2.1 Fully connected networks -- 7.2.2.2 Convolutional neural networks -- 7.2.3 Regularization for artificial neural networks -- 7.2.3.1 Dropout -- 7.2.3.2 L1 regularization -- 7.2.3.3 L2 regularization -- 7.2.4 Logistic regression -- 7.2.5 Tree models -- 7.2.5.1 Random forest -- 7.2.6 XGBoost -- 7.2.7 Classification threshold definition -- 7.2.7.1 ROC curve for finding the optimal threshold -- 7.3 Classifier fusion Morales-Menendez, Ruben Verfasser aut Erscheint auch als Druck-Ausgabe 978-0-323-99029-5 https://doi.org/10.1016/C2021-0-01383-3 Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Escobar Belalcázar, Carlos Arnulfo Morales-Menendez, Ruben Machine learning in manufacturing quality 4.0 and the zero defects vision Front Cover -- Machine Learning in Manufacturing -- Machine Learning in Manufacturing -- Copyright -- Contents -- Biographies -- Preface -- 1 -- Introduction -- 1.1 Motivation -- 1.2 Smart manufacturing -- 1.2.1 Technologies -- 1.2.2 Building blocks -- 1.2.3 Characteristics -- 1.2.4 Research areas -- 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 -- 2 -- The technologies -- 2.1 Artificial intelligence -- 2.1.1 Machine learning -- 2.1.1.1 Desirable characteristics of ML projects 2.1.2 ChatGPT for quality 4.0 -- 2.2 Cloud storage and computing -- 2.2.1 Cloud framework for quality 4.0 -- 2.2.2 Data lake, data warehouse, and database -- 2.3 Industrial internet of things -- 2.3.1 The manufacturing things -- 2.3.1.1 Smart sensors -- 2.3.1.2 Actuators -- 2.3.2 Process monitoring and quality control in manufacturing -- 3 -- The data -- 3.1 Big data -- 3.2 Manufacturing big data -- 3.3 Transforming big data into a learning data -- 3.3.1 Data frame -- 3.3.2 Learning data sets -- 3.3.2.1 Recommendations on data splits -- 3.3.2.2 Type of data 3.4 Binary classification of quality data sets -- 3.4.1 Data annotation -- 3.4.2 BCoQ data sets examples -- 4 -- Classification -- 4.1 Binary Classification -- 4.2 Binary classification of quality -- 4.3 Classification errors -- 4.3.1 Alpha and beta errors -- 4.3.2 Metrics of classification performance -- 4.4 Empirical case study on classification metrics -- 4.5 Binary patterns -- 4.5.1 Discriminative information -- 4.5.1.1 Virtual case study one-3D pattern -- 4.5.1.2 Virtual case study two-separation -- 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 -- 6 -- Feature engineering -- 6.1 Feature creation -- 6.2 Feature selection -- 6.3 Feature visualization -- 6.4 Feature preprocessing -- 6.4.1 Feature imputation -- 6.4.2 Feature permutation -- 6.4.3 Outlier analysis -- 6.4.4 Feature transformation -- 6.5 Summary -- 7 -- Classifier development -- 7.1 Modeling paradigms -- 7.1.1 Statistical modeling versus machine-learning modeling -- 7.1.2 Observational data versus experimental data -- 7.1.3 Data snooping 7.1.4 Accuracy versus interpretability -- 7.1.5 Model selection -- 7.2 Machine-learning algorithms -- 7.2.1 Hyperparameter optimization -- 7.2.2 Artificial neural networks -- 7.2.2.1 Fully connected networks -- 7.2.2.2 Convolutional neural networks -- 7.2.3 Regularization for artificial neural networks -- 7.2.3.1 Dropout -- 7.2.3.2 L1 regularization -- 7.2.3.3 L2 regularization -- 7.2.4 Logistic regression -- 7.2.5 Tree models -- 7.2.5.1 Random forest -- 7.2.6 XGBoost -- 7.2.7 Classification threshold definition -- 7.2.7.1 ROC curve for finding the optimal threshold -- 7.3 Classifier fusion |
| title | Machine learning in manufacturing quality 4.0 and the zero defects vision |
| title_auth | Machine learning in manufacturing quality 4.0 and the zero defects vision |
| title_exact_search | Machine learning in manufacturing quality 4.0 and the zero defects vision |
| title_exact_search_txtP | Machine Learning in Manufacturing Quality 4.0 and the Zero Defects Vision |
| title_full | Machine learning in manufacturing quality 4.0 and the zero defects vision Carlos A. Escobar, Ruben Morales-Menendez |
| title_fullStr | Machine learning in manufacturing quality 4.0 and the zero defects vision Carlos A. Escobar, Ruben Morales-Menendez |
| title_full_unstemmed | Machine learning in manufacturing quality 4.0 and the zero defects vision Carlos A. Escobar, Ruben Morales-Menendez |
| title_short | Machine learning in manufacturing |
| title_sort | machine learning in manufacturing quality 4 0 and the zero defects vision |
| title_sub | quality 4.0 and the zero defects vision |
| url | https://doi.org/10.1016/C2021-0-01383-3 |
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