Smart Manufacturing - Applications and Case Studies
Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. This book includes information about the key applications of these new methods, as well as practitioners' accounts of real-life applications and case studies. Written by tho...
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| Main Authors | , , |
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| Format | eBook |
| Language | English |
| Published |
Chantilly
Elsevier
2020
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| Edition | 1 |
| Subjects | |
| Online Access | Get full text |
| ISBN | 0128200286 9780128200285 |
| DOI | 10.1016/B978-0-12-820028-5.09990-2 |
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| Abstract | Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. This book includes information about the key applications of these new methods, as well as practitioners' accounts of real-life applications and case studies. Written by thought leaders in the field from around the world, this book is essential reading for graduate students, researchers, process engineers and managers. It is complemented by a companion book titled Smart Manufacturing: Concepts and Methods, which describes smart manufacturing methods in detail. |
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| AbstractList | Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. This book includes information about the key applications of these new methods, as well as practitioners' accounts of real-life applications and case studies. Written by thought leaders in the field from around the world, this book is essential reading for graduate students, researchers, process engineers and managers. It is complemented by a companion book titled Smart Manufacturing: Concepts and Methods, which describes smart manufacturing methods in detail. Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Applications and Case Studies includes information about the key applications of these new methods, as well as practitioners' accounts of real-life applications and case studies. Written by thought leaders in the field from around the world, Smart Manufacturing: Applications and Case Studies is essential reading for graduate students, researchers, process engineers and managers. It is complemented by a companion book titled Smart Manufacturing: Concepts and Methods, which describes smart manufacturing methods in detail. |
| Author | Soroush Masoud Baldea Michael Edgar Thomas F |
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| DOI | 10.1016/B978-0-12-820028-5.09990-2 |
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| Snippet | Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. This book includes information about... Research efforts in the past decade have led to considerable advances in the concepts and methods of smart manufacturing. Smart Manufacturing: Applications and... |
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| SubjectTerms | Manufacturing Competitiveness Manufacturing Engineering Manufacturing industries Manufacturing processes Manufacturing processes-Automation |
| TableOfContents | Title Page
Preface
Table of Contents
1. Smart Manufacturing in the Food Industry
2. Advancing Smart Manufacturing in the Pharmaceutical Industry
3. Smart Manufacturing in the Semiconductor Industry: An Evolving Nexus of Business Drivers, Technologies, and Standards
4. Smart Reservoir Management in the Oil and Gas Industry
5. Smart Manufacturing in Fine/Specialty Chemical Industries
6. Smart Manufacturing of Paints and Coatings
7. Smart Manufacturing in Additive Manufacturing
8. Smart Manufacturing Enabled by Continuous Monitoring and Control of Polymer Characteristics
9. Application of Smart Manufacturing Methods to Steam Methane Reformers
10. Smart Manufacturing in Industrial Gas Production: A Digital Transformation
11. Advanced Decision-Support Technologies for the Design and Management of Industrial Gas Supply Chains
12. Smart Manufacturing: A Sustainable Energy Perspective
13. Smart Manufacturing and Smart Grids
14. Smart Manufacturing: Machine Learning-Based Economic MPC and Preventive Maintenance
Index 1. Introduction -- 2. Methods and concepts -- 2.1. Data preprocessing -- 2.1.1. Smart approach -- 2.2. Reservoir analytics and diagnostics -- 2.2.1. Smart approach -- 2.3. Field development opportunities (FDPs) -- 2.3.1. Smart approach -- 2.4. Seismic interpretation -- 2.4.1. Smart approach -- 2.5. Geological model update -- 2.5.1. Smart approach -- 2.6. Well and reservoir forecasting -- 2.6.1. Smart approach for well and reservoir forecasting -- 2.7. Surface facilities -- 2.7.1. Smart approaches -- 3. Case studies -- 3.1. Data preprocessing -- 3.2. Reservoir analytics and diagnostics -- 3.3. Field development opportunities (FDPs) -- 3.4. Seismic interpretation -- 3.5. Geological model update -- 3.6. Well and reservoir forecasting -- 3.7. Surface facilities -- 4. Conclusion -- References -- Chapter 5: Smart manufacturing in fine/specialty chemical industries -- 1. Introduction -- 2. What is process intensification? -- 3. Basic principles of process intensification -- 4. Process intensification as a design platform -- 5. Application to design -- 6. Use of a heat map to identify high potential areas for PI -- 7. Intensification using continuous modular versus batch processing -- 8. Modeling and simulation as ``in silico´´ process design -- 9. Establishing specific company targets/metrics for process intensification -- 10. Process intensification value to manufacturing and the enterprise -- 11. Conclusion -- References -- Chapter 6: Smart manufacturing of paints and coatings -- 1. Introduction -- 1.1. Resin intermediates -- 1.2. Dispersion intermediates -- 1.3. Paint manufacturing -- 2. Smart manufacturing concepts and methods -- 2.1. Sample smart manufacturing opportunities -- 2.1.1. Batch-to-batch recipe optimization -- 2.1.2. Model-based sensing and control -- 2.1.3. Model-based process monitoring and product defect detection 1.2.1. GEM: SEMI E30, etc. -- 1.2.2. GEM300: SEMI E40, E87, E90, E94, E157 -- 1.2.3. EDA: SEMI E120, E125, E132, E134, E164, etc. -- 1.2.4. The end result -- 2. Historical background -- 2.1. Industry structure evolution -- 2.2. Industry standards evolution -- 2.3. Collaboration culture evolution -- 2.4. Business drivers evolution -- 3. Definition of smart manufacturing -- 4. Smart manufacturing standards -- 4.1. Connectivity standards evolution -- 4.2. Evolution of information models -- 4.3. Metrics standards -- 4.3.1. Overall equipment effectiveness -- 4.4. Product time management -- 4.5. Aggregating metrics standards for benchmarking -- 4.6. Traceability standards -- 5. Smart manufacturing implementation checklist -- 5.1. Partitioning the domain -- Costs -- Benefits -- 5.2. Smart manufacturing stakeholders -- 5.3. Capturing and incorporating stakeholder requirements -- 6. Smart manufacturing application technologies -- 6.1. Real-time throughput monitoring -- 6.1.1. Problem statement -- 6.1.2. Solution components -- 6.1.3. SEMI EDA standards leverage -- 6.1.4. Key ROI factors -- 6.2. Precision fault detection and classification -- 6.2.1. Problem statement -- 6.2.2. Key solution components -- 6.2.3. SEMI standards leverage -- 6.2.4. Key ROI factors -- 6.2.5. So what? -- 6.3. Complete product traceability -- 6.3.1. Problem statement -- 6.3.2. Key solution components -- 6.3.3. SEMI standards leverage -- 6.3.4. Key ROI factors -- 6.4. Other applications -- 6.5. Implications for equipment suppliers -- 7. Where are we headed? -- 7.1. Smart manufacturing system components -- 7.2. Platform-based system architectures and supporting ecosystem -- 7.3. SEMI Smart Manufacturing Community activities -- 7.4. International Roadmap for Devices and Systems -- 8. Conclusion -- References -- Chapter 4: Smart reservoir management in the oil and gas industry 5.3. Design considerations for polymer SLS -- 5.3.1. Material selection -- 5.3.2. Orientation within build chamber -- 5.3.3. Location within build chamber -- 5.3.4. Machine variability -- 5.4. Other considerations -- 6. Process control in industrial selective laser sintering -- 6.1. Failure modes in SLS processing -- 6.2. SLS process control -- 6.3. Future controls -- 7. Conclusion -- References -- Chapter 8: Smart manufacturing enabled by continuous monitoring and control of polymer characteristics -- 1. Introduction -- 2. Manufacturing process -- 3. Current methods for polymerization monitoring and control -- 4. Automatic Continuous Online Monitoring of Polymerization reactions (ACOMP) and smart polymer manufacturing -- 5. ACOMP principles and operation -- 5.1. The front end: Extraction, dilution, conditioning -- 5.2. Delay time and response time -- 5.3. The detector train -- 5.3.1. UV and visible spectrophotometry -- 5.3.2. Refractometry -- 5.3.3. Light scattering (SLS, DLS, and Mie) -- Viscometry in dilute solutions -- Viscosity measurements -- 5.3.4. Simultaneous measurement of Newtonian and non-Newtonian polymer properties -- 5.3.5. Conductivity and other measurements -- 5.3.6. Example ACOMP data -- 5.3.7. Optional use of GPC with ACOMP -- 5.4. Detector data and model-free analysis -- 5.4.1. Determining instantaneous and cumulative values of reaction characteristics -- 5.5. Primary data analysis for modeling, process development, and reaction control -- 6. Case studies -- 6.1. Free radical homopolymerization and copolymerization -- 6.2. Emulsion and inverse emulsion polymer reactions -- 6.3. Continuous flow reactions -- 6.4. Semibatch control of Mw using ACOMP [53] -- 6.5. Residual monomer prediction -- 6.6. Automatic feedback control of polymerization reactions -- 6.6.1. Control of monomer concentration -- 6.6.2. Control of molar mass Intro -- Smart Manufacturing: Applications and Case Studies -- Copyright -- Contents -- Contributors -- Preface -- Chapter 1: Smart manufacturing in the food industry -- 1. Introduction -- 2. Overview of the food processing industry in the United States: Challenges and pressures -- 3. Manufacturing process -- 4. Prior methods and concepts, their benefits and disadvantages -- 4.1. ISA-88 -- 4.2. ISA-95 -- 4.3. ISA-TR88.00.02-2015 (PackML) -- 4.4. Industrial Internet Reference Architecture v 1.8 (IIRA) -- 4.5. Platform Industrie 4.0 Reference Architecture Model Industrie 4.0 (RAMI 4.0) -- 4.6. Various commercial control and information platforms -- 5. Smart manufacturing concepts and methods, and their benefits -- 6. Case studies -- 6.1. Traceability -- 6.2. Machine learning for improved product weight control -- 6.3. Demand-driven supply chain -- 7. Future opportunities -- 7.1. Dynamic processing of variable inputs -- 7.2. Energy as an ingredient -- 7.3. Continuous improvement -- 8. Conclusion -- References -- Chapter 2: Advancing smart manufacturing in the pharmaceutical industry -- 1. Introduction -- 2. Modernization of pharmaceutical manufacturing -- 2.1. Quality by Design -- 2.2. Toward cyber-physical systems -- 2.3. Continuous processing -- 2.4. Collaborations -- 2.5. A Quality by Control paradigm -- 3. Continuous tablet manufacturing -- 3.1. Process -- 3.2. A complex system of systems -- 4. Developments in CM operations management -- 4.1. Process monitoring -- 4.2. Process control -- 4.3. Material tracking -- 4.4. Condition monitoring and maintenance -- 4.5. Knowledge management -- 4.6. Systems integration -- 5. Conclusion -- References -- Chapter 3: Smart manufacturing in the semiconductor industry: An evolving nexus of business drivers, technologies, and st ... -- 1. Introduction -- 1.1. Background -- 1.2. Context: The modern ``gigafab 2.1.4. Hybrid mathematical modeling -- 3. Process systems engineering view of the manufacturing problems -- 4. Mathematical modeling and inference methods -- 4.1. Statistical machine learning -- 4.2. Probabilistic forward and backward inference -- 4.3. Copulas -- 4.4. Rolling pin method of JPD estimation -- 4.4.1. Copula-based method of identifying regression models [45] -- 4.4.2. Inference using RP-based JDPs -- 4.4.3. Application of the RP method to the large-scale Tennessee Eastman chemical process [48] -- 4.5. Electronic-scale modeling of polymerization reactions -- 4.6. Polymerization experimentation and macroscopic-scale modeling -- 5. Prior methods and concepts: Their advantages and disadvantages -- 6. Future opportunities -- 6.1. Probabilistic modeling of the dependence of P/C product quality -- 6.2. Electronic-scale modeling of polymerization reactions -- 6.3. Macroscopic-scale mechanistic modeling of resin and dispersant processes -- 6.4. Real-time P/C product quality prediction -- 6.5. Computational (in silico) design of P/C products -- 6.6. Real-time P/C product defect detection and diagnosis -- 6.7. Real-time multirate control of polymer resin and P/C product quality -- 7. Conclusion -- References -- Chapter 7: Smart manufacturing in additive manufacturing -- 1. Introduction -- 2. Additive manufacturing in practice -- 3. Additive manufacturing technologies -- 3.1. Material extrusion -- 3.2. Powder bed fusion -- 3.3. Vat polymerization -- 3.4. Binder jetting -- 3.5. Material jetting -- 3.6. Directed energy deposition -- 3.7. Sheet lamination -- 4. Industrial additive manufacturing -- 5. Defect avoidance through part design -- 5.1. Design for additive manufacturing -- 5.2. Geometric defects in SLS -- 5.2.1. Part warping -- 5.2.2. Powder caking -- 5.2.3. Dimensional accuracy errors -- 5.2.4. Resolution errors 6.6.3. Chromatography-free determination of MWD using models |
| Title | Smart Manufacturing - Applications and Case Studies |
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