Download or read book Sensor based Modeling and Analysis of Advanced Manufacturing Systems for Quality Improvements written by Farhad Imani and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced sensing is increasingly invested in modern manufacturing systems to cope with the complexity and enhance information visibility, thereby leading to data-rich environments. Generated data provide unprecedented opportunities to investigate system dynamics and further improve quality monitoring and control for advanced manufacturing in real-time. However, high-dimensionality and complex structures of sensing data pose significant challenges. Realizing full potentials of sensing data depends to a great extent on the development of novel analytical methods and tools for effective modeling, monitoring, and control of manufacturing systems. The research objective of this dissertation is to develop new learning methodologies for real-time quality monitoring and control of complex manufacturing systems. This body of research will enable and assist in 1) understanding the effect of process conditions on quality of manufacturing builds, 2) extracting sensitive features and characterizing patterns of image data, 3) diagnosing defects in low-volume and highly-customized production settings, and 4) handling high dimensional spatiotemporal data. My research accomplishments include: 1) Process mapping and monitoring of porosity in additive manufacturing (AM): In Chapter 2, spectral graph theory and multifractal analysis are developed to quantify the effect of process conditions on lack of fusion porosity in builds made using AM process, and subsequently, to detect the onset of process conditions that lead to lack of fusion porosity from in-process sensor data. 2) Multifractal and lacunarity analysis for nonlinear pattern characterization: In Chapter 3, the joint multifractal and lacunarity analysis is designed to resolve local densities and characterize the filling patterns in image profiles. Further, we derive the composite quality index by computing Hotelling T2 statistics from multifractal and lacunarity features for defect detection and characterization in ultra precision machining (UPM) and AM image profiles. 3) Image-guided variant geometry analysis of layerwise build quality: In Chapter 4, we develop a tailored deep neural network (DNN) framework that learns the broad geometrical diversity of images from builds made with AM. The proposed methodology leverages the computer-aided design (CAD) file to register the region of interest (ROI) in each layerwise image. Next, we propose a dyadic partitioning method to delineate variant ROI into distinctive regions with the same size and in multiple scales. Then, we leverage the semiparametric spatial model to characterize the complex spatial patterns in subregion ROIs. Finally, a DNN is designed to learn incipient flaws from spatial characterization images. 4) Spatiotemporal Gaussian process for AM quality monitoring: In Chapter 5, a novel spatiotemporal Gaussian process (STGP) is introduced to model the standard geometric profile within ROIs and capture layer-to-layer spatiotemporal deviations for quality monitoring. Finally, we leverage the STGP model to develop new monitoring charts, namely, the STT2 and STLR tests, for the anomaly detection in AM processes. This framework enables on-the-fly assessment of AM build quality.

Download or read book Manufacturing Systems Design Modeling and Analysis Advanced Condition Monitoring and Maintenance Technologies Advances in Metrology Applications and Implementation Ready Technologies New Developments in Sensors Integration Micro manufacturing Processes and Equipment Quality and Reliability of Machining Systems Nanomanufacturing written by American Society of Mechanical Engineers Manufacturing Engineering Division and published by . This book was released on 2007 with total page 17 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Condition Monitoring and Control for Intelligent Manufacturing written by Lihui Wang and published by Springer Science & Business Media. This book was released on 2006-08-02 with total page 411 pages. Available in PDF, EPUB and Kindle. Book excerpt: Condition modelling and control is a technique used to enable decision-making in manufacturing processes of interest to researchers and practising engineering. Condition Monitoring and Control for Intelligent Manufacturing will be bought by researchers and graduate students in manufacturing and control and engineering, as well as practising engineers in industries such as automotive and packaging manufacturing.

Download or read book Advances in Production Management Systems Artificial Intelligence for Sustainable and Resilient Production Systems written by Alexandre Dolgui and published by Springer Nature. This book was released on 2021-09-01 with total page 715 pages. Available in PDF, EPUB and Kindle. Book excerpt: The five-volume set IFIP AICT 630, 631, 632, 633, and 634 constitutes the refereed proceedings of the International IFIP WG 5.7 Conference on Advances in Production Management Systems, APMS 2021, held in Nantes, France, in September 2021.* The 378 papers presented were carefully reviewed and selected from 529 submissions. They discuss artificial intelligence techniques, decision aid and new and renewed paradigms for sustainable and resilient production systems at four-wall factory and value chain levels. The papers are organized in the following topical sections: Part I: artificial intelligence based optimization techniques for demand-driven manufacturing; hybrid approaches for production planning and scheduling; intelligent systems for manufacturing planning and control in the industry 4.0; learning and robust decision support systems for agile manufacturing environments; low-code and model-driven engineering for production system; meta-heuristics and optimization techniques for energy-oriented manufacturing systems; metaheuristics for production systems; modern analytics and new AI-based smart techniques for replenishment and production planning under uncertainty; system identification for manufacturing control applications; and the future of lean thinking and practice Part II: digital transformation of SME manufacturers: the crucial role of standard; digital transformations towards supply chain resiliency; engineering of smart-product-service-systems of the future; lean and Six Sigma in services healthcare; new trends and challenges in reconfigurable, flexible or agile production system; production management in food supply chains; and sustainability in production planning and lot-sizing Part III: autonomous robots in delivery logistics; digital transformation approaches in production management; finance-driven supply chain; gastronomic service system design; modern scheduling and applications in industry 4.0; recent advances in sustainable manufacturing; regular session: green production and circularity concepts; regular session: improvement models and methods for green and innovative systems; regular session: supply chain and routing management; regular session: robotics and human aspects; regular session: classification and data management methods; smart supply chain and production in society 5.0 era; and supply chain risk management under coronavirus Part IV: AI for resilience in global supply chain networks in the context of pandemic disruptions; blockchain in the operations and supply chain management; data-based services as key enablers for smart products, manufacturing and assembly; data-driven methods for supply chain optimization; digital twins based on systems engineering and semantic modeling; digital twins in companies first developments and future challenges; human-centered artificial intelligence in smart manufacturing for the operator 4.0; operations management in engineer-to-order manufacturing; product and asset life cycle management for smart and sustainable manufacturing systems; robotics technologies for control, smart manufacturing and logistics; serious games analytics: improving games and learning support; smart and sustainable production and supply chains; smart methods and techniques for sustainable supply chain management; the new digital lean manufacturing paradigm; and the role of emerging technologies in disaster relief operations: lessons from COVID-19 Part V: data-driven platforms and applications in production and logistics: digital twins and AI for sustainability; regular session: new approaches for routing problem solving; regular session: improvement of design and operation of manufacturing systems; regular session: crossdock and transportation issues; regular session: maintenance improvement and lifecycle management; regular session: additive manufacturing and mass customization; regular session: frameworks and conceptual modelling for systems and services efficiency; regular session: optimization of production and transportation systems; regular session: optimization of supply chain agility and reconfigurability; regular session: advanced modelling approaches; regular session: simulation and optimization of systems performances; regular session: AI-based approaches for quality and performance improvement of production systems; and regular session: risk and performance management of supply chains *The conference was held online.

Download or read book Advances in Manufacturing Production Management and Process Control written by Beata Mrugalska and published by Springer Nature. This book was released on 2020-06-30 with total page 391 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the latest advances in the broadly defined field of advanced manufacturing and process control. It reports on cutting-edge strategies for sustainable production and product life cycle management, and on a variety of people-centered issues in the design, operation and management of manufacturing systems and processes. Further, it presents digital modeling systems and additive manufacturing technologies, including advanced applications for different purposes, and discusses in detail the implementation of and challenges imposed by 3D printing technologies. Based on three AHFE 2020 Conferences (the AHFE 2020 Virtual Conference on Human Aspects of Advanced Manufacturing, the AHFE 2020 Virtual Conference on Advanced Production Management and Process Control and the AHFE 2020 Virtual Conference on Additive Manufacturing, Modeling Systems and 3D Prototyping, the book merges ergonomics research, design applications, and up-to-date analyses of various engineering processes. It brings together experimental studies, theoretical methods and best practices, highlights future trends and suggests directions for further technological developments and the improved integration of technologies and humans in the manufacturing industry.

Download or read book Advanced Manufacturing and Automation V written by K. Wang and published by WIT Press. This book was released on 2016-02-03 with total page 453 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Manufacturing and Automation V contains the proceedings of the 5th International Workshop of Advanced Manufacturing and Automation (IWAMA 2015). This meeting continues the success of this important international workshop series and disseminates the works of academic and industrial experts, from around the world, in the areas of advanced manufacturing and automation. The disciplines of manufacturing and automation have attained paramount importance and are vital factors for the maintenance and improvement of the economy of a nation and the quality of life. Manufacturing and automation are advancing at a rapid pace and new technologies are constantly emerging in the fields. The challenges faced by today’s engineers are forcing them to keep on top of the emerging trends through continuous research and development. The papers comprising these proceedings cover various topics including: Robotics and automation; Computational intelligence; Design and optimization; Product life-cycle management; Integration of CAD/CAPP/CAM/CIMS; Advanced manufacturing systems; Manufacturing operations management; Knowledge-based manufacturing; Manufacturing quality control and management; Sustainable production; Diagnosis and prognosis of machines; Lean and agile manufacturing; Virtual and grid manufacturing; Resource and asset management; Logistics and supply chain management; RFID applications; Predictive maintenance; Reliability and maintainability in manufacturing; Project management; Renewable energy development; Environment protection; Intelligent detection.

Download or read book Recent Advances in Manufacturing Modelling and Optimization written by Shailendra Kumar and published by Springer Nature. This book was released on 2022-04-21 with total page 830 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents the selected proceedings of 2nd International Conference on Recent Advances in Manufacturing (RAM 2021). The book provides insights to current research trends and opportunities in modelling and optimization of manufacturing processes and systems. The topics covered include modelling analysis, computing and simulation, traditional and non-traditional optimization techniques, surface coating methods, additive manufacturing processes, CAD/CAM, robotics and automation, welding and joining processes, supply chain management and CAE and reverse engineering. This book will be a good reference for beginners, researchers and professionals interested in modelling and optimization related to manufacturing engineering and related fields.

Download or read book Data driven Variation Modeling and Management with Application of Advanced Manufacturing Processes and Systems written by Jaesung Lee and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Manufacturing variations refer to the uncertainties in the processes and inconsistency in the products produced. There have been increasing efforts to minimize the manufacturing variations, and reducing manufacturing variations in advanced manufacturing processes and systems is becoming more important. Advanced manufacturing processes and systems integrate manufacturing with innovative science and technologies and boost manufacturing efficiency and productivity. The integration with sensor technology now provides massive data, creating unprecedented research opportunities to model and analyze through data-driven models and methods. However, at the same time, advanced manufacturing processes and systems involve new critical challenges in modeling and managing the manufacturing variations. Many advanced manufacturing processes and systems have complex dynamics and transformation and multiple components involved, which create significant variations and uncertainties. However, physics-based models are often unavailable and often fail to address the uncertainties. This dissertation addresses multiple challenges in modeling and managing the manufacturing variations: (1) Variation source identification in multistage manufacturing systems: In multistage manufacturing systems, where multiple operations are performed in a series of stages (e.g., workstations), the variations produced from operations propagate to downstream measurements. In such systems, it is crucial to identify faulty operations with excessive variations among a large number of operations based on the quality measurements. We consider a common case where the measurements are not directly taken from the operations but from products in the downstream stage and the number of operations is much larger than the number of measurements. However, inferring underlying variations of numerous operations by limited measurements cause technical challenges in statistical inference. Therefore, we want to establish a statistical model that can identify faulty operations by leveraging the Engineering domain knowledge. Three types of domain knowledge are considered: a) The fact that faults occur sporadically; b) Practitioners' empirical knowledge of the faults occurrence frequency; c) Various tolerance levels on variations across operations. (2) Modeling inkjet printing manufacturing process: The inkjet printing manufacturing process involves significant random variations due to the complex physical and chemical dynamics of the nanomaterial pieces in the printed ink. Process variations create significant uncertainties in the manufactured product quality, but such uncertainties have not been studied. Therefore, it is crucial to model the randomness in the manufacturing outcome in terms of process parameters. Building upon the statistical model, this work further aims to establish a statistic that evaluates the manufacturing outcome quality, and ultimately identifies abnormal manufacturing outcomes. (3) Statistical calibration of underlying physical variable: In designing manufacturing processes and products, inferring the underlying physical input variable, called statistical calibration, is often needed. For example, by using the GFET nanosensor outputs, inferring the amount of the target substance in the environment is important. Furthermore, the uncertainty of the inferred variable needs to be quantified. However, due to significant process variations in manufacturing, the GFET nanosensor outputs involve significant random variations, and thus precise inferring is challenging. Specifically, random shapes and random locations of functional data need to be modeled for precise calibration. (4) Optimal parameter design through Bayesian optimization: It is very crucial to design manufacturing processes or products so that they have small quality variations while satisfying the overall quality (i.e., robust design). However, data are often costly to acquire especially in the designing stage. Furthermore, the underlying exact relationships between the design variables and the mean and variance of the outputs are not known and are in complex forms. Therefore, a sample efficient data-driven method to find the robust design needs to be established. To address these challenges listed above, four problems are investigated in this dissertation. (i) To build a special sparsity-enhanced Bayesian linear random-effects model to reflect Engineering domain knowledge. With the proposed model, Engineering domain knowledge on sparse faults with excessive variations is incorporated into the model, and the variation sources are successfully identified. (ii) To model the uncertainties in the inkjet printing manufacturing process in terms of physical process parameters. Building upon the proposed model, abnormal manufacturing outcomes are successfully identified. (iii) To establish a non-parametric model to characterize functional data with significant variations. The issue with random shapes and random shifting of functional data is addressed. (iv) To establish sample-efficient stochastic constrained optimization method for constrained robust parameter design. The proposed technique minimizes the variations while satisfying a constraint on the mean of the quality measurements by conducting a small number of experiments. Because the proposed methods are driven by data, these models and methods are very flexible and can be used to address many general problems in other manufacturing processes.

Download or read book Machine Learning for Powder Based Metal Additive Manufacturing written by Gurminder Singh and published by Elsevier. This book was released on 2024-09-04 with total page 291 pages. Available in PDF, EPUB and Kindle. Book excerpt: Machine Learning for Powder-based Metal Additive Manufacturing outlines machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs. The book combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications. The book covers ML for design in AM, ML for materials development and intelligent monitoring in metal AM, both geometrical deviation and physics informed machine learning modeling, as well as data-driven cost estimation by ML. In addition, optimization for slicing and orientation, ML to create models of materials for AM processes, ML prediction for better mechanical and microstructure prediction, and feature extraction by sensing data are all covered, and each chapter includes a case study. Covers machine learning (ML) methods for additive manufacturing (AM) of metals that will improve product quality, optimize manufacturing processes, and reduce costs Combines ML and AM methods to develop intelligent models that train AM techniques in pre-processing, process optimization, and post-processing for optimized microstructure, tensile and fatigue properties, and biocompatibility for various applications Discusses algorithm development of ML for metal AM, metal AM process modeling and optimization, mathematical and simulation studies of metal AM, and pre- and post-processing smart methods for metal AM

Download or read book Multimodal and Tensor Data Analytics for Industrial Systems Improvement written by Nathan Gaw and published by Springer Nature. This book was released on with total page 388 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Industrial Process Sensors written by David M. Scott and published by CRC Press. This book was released on 2007-11-02 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: As manufacturing processes become increasingly complex, industry must rely on advanced sensor technology and process control to improve efficiency and product quality. Processes now need a variety of on-line measurements, such as film thickness, particle size, solids concentrations, and contamination detection. Industrial Process Sensors provides a coherent review of the physical principles, design, and implementation of a wide variety of in-process sensors used to control manufacturing operations. Real data from commercial installations illustrates the operation and limitations of these devices. The book begins with a review of the basic physics of sound, light, electricity, and radiation, with a focus on their role in sensor devices. The author introduces the generic sensor model and discusses the propagation of measurement errors. He goes on to describe conventional process sensors that measure temperature, pressure, level, and flow. The second half of the book focuses on more advanced topics, such as particle size measurement in slurries and emulsions, tomography and process imaging of manufacturing operations, on-line measurement of film thickness, identification of polymer type for recycling, and characterization of reinforced polymers and composites. By exploring both theory and final implementation of sensors used to control industrial manufacturing processes, Industrial Process Sensors provides the information you need to develop solutions to a wide range of industrial measurement needs.

Download or read book Modeling Manufacturing Systems written by Paolo Brandimarte and published by Springer Science & Business Media. This book was released on 1999-03-29 with total page 236 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced modeling techniques are a necessary tool in order to design and manage manufacturing systems effectively. This book contains a set of tutorial chapters on topics ranging from aggregate production planning to real time control, including predictive and reactive scheduling, flow management in assembly systems, simulation of robotic cells, design of manufacturing systems under uncertainty and a historical perspective on production management philosophies. The book will be of interest both to researchers and practitioners, including graduate students in Manufacturing Engineering and Operations Research.

Download or read book Expanding the Vision of Sensor Materials written by Committee on New Sensor Technologies: Materials and Applications and published by National Academies Press. This book was released on 1995-07-06 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advances in materials science and engineering have paved the way for the development of new and more capable sensors. Drawing upon case studies from manufacturing and structural monitoring and involving chemical and long wave-length infrared sensors, this book suggests an approach that frames the relevant technical issues in such a way as to expedite the consideration of new and novel sensor materials. It enables a multidisciplinary approach for identifying opportunities and making realistic assessments of technical risk and could be used to guide relevant research and development in sensor technologies.

Download or read book New Industry 4 0 Advances in Industrial IoT and Visual Computing for Manufacturing Processes written by Luis Norberto López de Lacalle and published by MDPI. This book was released on 2020-03-18 with total page 428 pages. Available in PDF, EPUB and Kindle. Book excerpt: Modern factories are experiencing rapid digital transformation supported by emerging technologies, such as the Industrial Internet of things (IIOT), industrial big data and cloud technologies, deep learning and deep analytics, AI, intelligent robotics, cyber-physical systems and digital twins, complemented by visual computing (including new forms of artificial vision with machine learning, novel HMI, simulation, and visualization). This is evident in the global trend of Industry 4.0. The impact of these technologies is clear in the context of high-performance manufacturing. Important improvements can be achieved in productivity, systems reliability, quality verification, etc. Manufacturing processes, based on advanced mechanical principles, are enhanced by big data analytics on industrial sensor data. In current machine tools and systems, complex sensors gather useful data, which is captured, stored, and processed with edge, fog, or cloud computing. These processes improve with digital monitoring, visual data analytics, AI, and computer vision to achieve a more productive and reliable smart factory. New value chains are also emerging from these technological changes. This book addresses these topics, including contributions deployed in production, as well as general aspects of Industry 4.0.

Download or read book Proceedings of the 4th International Conference on the Industry 4 0 Model for Advanced Manufacturing written by Laszlo Monostori and published by Springer. This book was released on 2019-04-30 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 4th International Conference on the Industry 4.0 Model for Advanced Manufacturing (AMP 2019), held in Belgrade, Serbia, on 3–6 June 2019. The event marks the latest in a series of high-level conferences that bring together experts from academia and industry to exchange knowledge, ideas, experiences, research findings, and information in the field of manufacturing. The book addresses a wide range of topics, including: design of smart and intelligent products, developments in CAD/CAM technologies, rapid prototyping and reverse engineering, multistage manufacturing processes, manufacturing automation in the Industry 4.0 model, cloud-based products, and cyber-physical and reconfigurable manufacturing systems. By providing updates on key issues and highlighting recent advances in manufacturing engineering and technologies, the book supports the transfer of vital knowledge to the next generation of academics and practitioners. Further, it will appeal to anyone working or conducting research in this rapidly evolving field.

Download or read book Industry 4 0 and Advanced Manufacturing written by Amaresh Chakrabarti and published by Springer Nature. This book was released on 2020-10-28 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents selected papers from the 1st International Conference on Industry 4.0 and Advanced Manufacturing held at the Indian Institute of Science, Bangalore and includes deliberations from stakeholders in manufacturing and Industry 4.0 on the nature, needs, challenges, opportunities, problems, and solutions in these transformational areas. Special emphasis is placed on exploring avenues for creating a vision of, and enablers for, sustainable, affordable, and human-centric Industry 4.0. The book showcases cutting edge practice, research, and educational innovation in this crucial and rapidly evolving area. This book will be useful to researchers in academia and industry, and will also be useful to policymakers involved in creating ecosystems for implementation of Industry 4.0.

Download or read book Proceedings of 5th International Conference on the Industry 4 0 Model for Advanced Manufacturing written by Lihui Wang and published by Springer Nature. This book was released on 2020-05-15 with total page 370 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book gathers the proceedings of the 5th International Conference on the Industry 4.0 Model for Advanced Manufacturing (AMP 2020), held in Belgrade, Serbia, on 1–4 June 2020. The event marks the latest in a series of high-level conferences that bring together experts from academia and industry to exchange knowledge, ideas, experiences, research findings, and information in the field of manufacturing. The book addresses a wide range of topics, including: design of smart and intelligent products, developments in CAD/CAM technologies, rapid prototyping and reverse engineering, multistage manufacturing processes, manufacturing automation in the Industry 4.0 model, cloud-based products, and cyber-physical and reconfigurable manufacturing systems. By providing updates on key issues and highlighting recent advances in manufacturing engineering and technologies, the book supports the transfer of vital knowledge to the next generation of academics and practitioners. Further, it will appeal to anyone working or conducting research in this rapidly evolving field.