Quality in the era of industry 4.0 : integrating tradition and innovation in the age of data and AI / Kai Yang.
By: Yang, Kai [author.]
Language: English Publisher: Hoboken, New Jersey : Wiley, [2024]Copyright date: ©2024Description: 1 online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9781119932444 ; 9781119932468; 9781119932451Subject(s): Product management -- Quality control | Product management -- Data processing | Manufacturing industries -- Quality control | Manufacturing processes -- Data processing | Big data -- Industrial applicationsGenre/Form: Electronic books.DDC classification: 658.5/62 LOC classification: HF5415.157Online resources: Full text is available at Wiley Online Library Click here to view Summary: "The Industry 4.0 revolution is shifting the way that quality engineers, managers, and product developers must think about quality control. Super connectivity, IoT, and big data have enabled a transition from traditional "voice of the customer" surveys to the "voice of Big Data," which communicates descriptive information from real customers about how they use products. Quality in the Era of Industry 4.0: Harnessing Data Analytics for Quality Engineering Applications guides readers on how that data can be leveraged to optimize products during use, to anticipate and mitigate the consequences of likely failures, and to build better and less expensive products in the future. In a concise, straightforward style, this book offers readers a comprehensive framework for new quality management methods under Industry 4.0. This includes new techniques like using real-world data to improve product fit and performance, leveraging connectivity to make products responsive to changing needs and use cases, and drawing upon modern manufacturing to make cost-effective, bespoke solutions that can be produced more efficiently. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how IoT-enabled product usage data can be used to bench mark the product performances, durability, and detect design vulnerabilities. Automated Product Lifecycle Management, Predictive Quality Control, and defect prevention using technologies like smart factories, IoT, digital twins, machine learning, and sensors are also covered"-- Provided by publisher.| Item type | Current location | Home library | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|---|---|
EBOOK
|
COLLEGE LIBRARY | COLLEGE LIBRARY | 658.562 Y164 2024 (Browse shelf) | Available |
Includes index.
"The Industry 4.0 revolution is shifting the way that quality engineers, managers, and product developers must think about quality control. Super connectivity, IoT, and big data have enabled a transition from traditional "voice of the customer" surveys to the "voice of Big Data," which communicates descriptive information from real customers about how they use products. Quality in the Era of Industry 4.0: Harnessing Data Analytics for Quality Engineering Applications guides readers on how that data can be leveraged to optimize products during use, to anticipate and mitigate the consequences of likely failures, and to build better and less expensive products in the future. In a concise, straightforward style, this book offers readers a comprehensive framework for new quality management methods under Industry 4.0. This includes new techniques like using real-world data to improve product fit and performance, leveraging connectivity to make products responsive to changing needs and use cases, and drawing upon modern manufacturing to make cost-effective, bespoke solutions that can be produced more efficiently. Case examples featuring applications from the automotive, mobile device, home appliance, and healthcare industries are used to illustrate how IoT-enabled product usage data can be used to bench mark the product performances, durability, and detect design vulnerabilities. Automated Product Lifecycle Management, Predictive Quality Control, and defect prevention using technologies like smart factories, IoT, digital twins, machine learning, and sensors are also covered"-- Provided by publisher.
About the Author
Kai Yang, Ph.D., is a Professor in the Department of Industrial and Systems Engineering at Wayne State University. He is a Fellow of both the American Society of Quality and the Institute of Industrial and Systems Engineers, and he was awarded the Cecil C. Craig Lifetime Achievement Award by the Automotive Division of American Society of Quality in 2016. Dr. Yang has consulted on quality control projects for General Motors, Ford, and Siemens.
Description based on print version record and CIP data provided by publisher; resource not viewed.
There are no comments for this item.