http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
Artificial intelligence in manufacturing
Artificial Intelligence in Manufacturing: Applications and Case Studies provides detailed technical descriptions of emerging applications of AI in manufacturing using case studies to explain implementation.
Front cover -- Half title -- Title -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1 Artificial intelligence for paints/coatings manufacturing -- 1.1 Introduction -- 1.2 The machine learning problem -- 1.3 Overview of applied methods -- 1.3.1 Traditional machine learning methods -- 1.3.2 Deep learning methods -- 1.3.3 CNN followed by an MLP with LAB regularization -- 1.4 Results and discussion -- 1.4.1 Regression -- 1.4.2 Convolutional neural network followed by a multilayer perceptron -- 1.4.3 CNN followed by an MLP with dropout layers -- 1.5 Qualitative analysis of modeling approaches -- 1.6 Experimental setup -- 1.7 Conclusion -- Acknowledgment -- References -- Chapter 2 Data-driven discovery and design of additives for controlled polymer morphology and performance -- 2.1 Introduction -- 2.2 Polymer crystal-growth predictions using molecular dynamics -- 2.2.1 Measuring surface nucleation and crystal growth rates -- 2.2.2 Characterizing effects of nucleating agents on polymer crystallization -- 2.3 Data-driven modeling and search through a materials genome -- 2.3.1 An evolutionary modeling approach for materials recommendation -- 2.3.2 General perspectives on materials discovery through an evolutionary genome model -- 2.4 Experimental validation of nucleating agents in polymers -- 2.4.1 Experimental measurement of heterogeneous nucleation -- 2.5 Conclusion -- Acknowledgment -- References -- Chapter 3 Data, machine learning, first-principles, and hybrid models in the petrochemical industry -- 3.1 Introduction -- 3.2 Data type and processing in petrochemical industry -- 3.2.1 Scalar -- 3.2.2 One-dimensional array -- 3.2.3 Two-dimensional matrix -- 3.2.4 Higher-dimensional data -- 3.2.5 Unstructured data -- 3.2.6 Data quality control -- 3.3 Machine learning and other data-driven models in the petrochemical industry.
3.3.1 Supervised learning -- 3.3.2 Unsupervised learning -- 3.3.3 Reinforcement learning -- 3.3.4 Transfer learning -- 3.3.5 Other data-driven models -- 3.4 First-principles models versus hybrid and data-driven models: a sliding scale -- 3.4.1 First-principles models -- 3.4.2 Key differences between first-principles and data-driven models -- 3.4.3 Hybrid models -- 3.5 Outlook -- 3.6 Conclusion -- References -- Chapter 4 Perspectives on artificial intelligence for plasma-assisted manufacturing in semiconductor industry -- 4.1 Introduction -- 4.2 Plasma-assisted processes in semiconductor manufacturing -- 4.3 AI for process design and production -- 4.3.1 Reactor design optimization using surrogate models -- 4.3.2 Generative design for optimization of parts -- 4.3.3 Material identification and characterization -- 4.4 AI for process optimization and efficiency enhancement -- 4.4.1 Image processing for scanning electron microscopy/transmission electron microscopy analysis -- 4.4.2 Process parameter space identification and inverse design -- 4.4.3 Automated endpoint detection -- 4.4.4 Defect classification -- 4.4.5 Auto clean of process modules -- 4.4.6 Virtual fabrication -- 4.5 AI for process operation and adaptive control -- 4.5.1 Sensor development with machine learning -- 4.5.2 Virtual metrology and fleet matching for yield improvement -- 4.5.3 Predictive and prescriptive maintenance -- 4.5.4 Automated part replacement -- 4.5.5 Predictive control and scheduling -- 4.6 Conclusion -- Acknowledgment -- References -- Chapter 5 Machine learning in reaction engineering -- 5.1 Introduction -- 5.2 Machine learning in catalyst design -- 5.2.1 Dataset construction -- 5.2.2 Representation of catalysts -- 5.2.3 Machine learning models -- 5.3 Machine learning for predicting reaction mechanism and kinetics.
5.3.1 Mechanism prediction for gas phase reactions (including heterogeneous catalytic systems) -- 5.3.2 Mechanism prediction for liquid/solution phase reactions (mainly organic synthesis reactions) -- 5.4 Data-driven reaction predictions -- 5.5 Conclusion -- References -- Chapter 6 Artificial intelligence in catalysis -- 6.1 Introduction -- 6.2 Applications of AI in improving catalytic models -- 6.2.1 AI-enhanced microkinetic models -- 6.2.2 AI enhanced prediction of adsorbate energies -- 6.2.3 AI-enhanced prediction of transition state energies -- 6.2.4 AI-enhanced sampling in ab initio molecular and metadynamics -- 6.3 Applications of AI in computational design of catalysts -- 6.4 AI to elucidate and design catalytic systems directly using experiments -- 6.4.1 Structure determination from spectra -- 6.4.2 Machine-learned models from experimental kinetics data -- 6.4.3 AI-guided experimentation -- 6.5 Allied and cross-cutting topics of AI in catalysis -- 6.5.1 Uncertainty quantification -- 6.5.2 Interpretable and domain cognizant models -- 6.5.3 Databases for machine learning -- 6.6 Conclusion -- References -- Chapter 7 Advanced manufacturing of touch-sensitive textiles -- 7.1 Introduction -- 7.2 Sensor interaction and machine learning -- 7.2.1 Continuous capacitive touch localization -- 7.2.2 Touch localization and gesture recognition -- 7.3 Gesture recognition system -- 7.3.1 Knitted sensor -- 7.3.2 Measurement circuit -- 7.3.3 Signal filtering -- 7.3.4 Neural network architecture -- 7.3.5 Model deployment -- 7.4 Experiments -- 7.5 Methods and results -- 7.5.1 Cross-validation and evaluation results -- 7.5.2 Resources and time performance -- 7.6 Building interactive applications -- 7.7 Conclusion -- References -- Chapter 8 Cyber-physical systems framework for AI in smart manufacturing and maintenance -- 8.1 Introduction.
8.2 Cyber-physical systems framework for maintenance and service innovation -- 8.2.1 5C architecture for cyber-physical systems framework -- 8.2.2 Enabling technologies for cyber-physical systems framework -- 8.2.3 Cyber-physical systems architecture for maintenance and service innovation -- 8.3 Development of digital twins: methodology and analytics -- 8.3.1 Digital twin for maintenance and service innovation -- 8.3.2 Methodology for digital twin development and deployment -- 8.3.3 Data analytics for digital twin development and deployment -- 8.4 Case studies -- 8.4.1 Deep learning enhanced machine degradation assessment -- 8.4.2 Cyber manufacturing for distributed assets -- 8.5 Conclusion -- References -- Chapter 9 Dynamic data feature engineering for process operation troubleshooting -- 9.1 Introduction -- 9.2 Latent dynamic time-series analytics -- 9.3 Dynamic latent variable feature extraction -- 9.3.1 Dynamic latent feature analysis via DiCCA -- 9.3.2 Dynamic latent relations -- 9.4 The DELFA troubleshooting procedure -- 9.4.1 Composite loadings for latent feature contribution analysis -- 9.4.2 The DELFA procedure for plant-wide troubleshooting -- 9.5 Troubleshooting plant-wide oscillations -- 9.5.1 Preliminary analysis -- 9.5.2 Removing the effect of TC3.OP and TI8.PV -- 9.5.3 Analysis of the low-frequency oscillation feature -- 9.5.4 Analysis of the high-frequency oscillation feature -- 9.6 Comparing DiCCA with slow feature analysis -- 9.6.1 Preliminary feature extraction based on all data -- 9.6.2 Dynamic features and troubleshooting based on one day of data -- 9.7 Conclusion -- Acknowledgment -- References -- Chapter 10 Advanced manufacturing of biopharmaceuticals -- 10.1 Introduction -- 10.2 Mammalian cell bioreactor simulator -- 10.2.1 Review of existing bioreactor models -- 10.2.2 Foundations of bioreactor simulator software.
10.3 Data-driven bioreactor process modeling -- 10.4 Model predictive control -- 10.4.1 Trajectory-tracking predictive control -- 10.4.2 Critical quality attribute predictive control -- 10.5 Data-driven modeling and closed-loop control results from mammalian cell bioreactor simulator -- 10.5.1 Bioreactor modeling results -- 10.5.2 Trajectory-tracking predictive control results -- 10.5.3 Critical quality attribute predictive control results -- 10.6 Integration of artificial intelligence in biopharmaceutical production processes -- 10.7 Conclusion -- Acknowledgment -- References -- Index -- Back cover.
edited by Masoud Soroush, Richard D. Braatz.