CAREER: Sensor-based Modeling and Control of Nonlinear Dynamics in Complex Systems for Quality Improvements in Manufacturing and Healthcare

职业：复杂系统中基于传感器的非线性动力学建模和控制，以提高制造和医疗保健的质量

• 批准号: 1617148
• 负责人: Hui Yang
• 金额: $ 45.22万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-18 至 2022-02-28
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1617148&HistoricalAwards=false
• 关键词: CAREER; Sensor; based; Modeling; Control

This Faculty Early Career Development (CAREER) Program grant will create a sensor-based nonlinear dynamics methodology for real-time system informatics, monitoring, and control. Nonlinear dynamics arise whenever multifarious entities of a system cooperate, compete, or interfere. In order to cope with system complexity and dynamic environments, modern industries are investing in a variety of advanced sensing systems. Effective monitoring and control of nonlinear dynamics will increase system quality and integrity, thereby leading to significant economic and societal impacts. For example, advancing sensing, measurement and process control in manufacturing industries will improve the overall quality and productivity, which is essential to achieving competitive advantage in advanced manufacturing. However, there is a critical gap in the knowledge base that pertains to integrating nonlinear dynamics research with quality engineering. This award supports fundamental research for new methodological development and curriculum innovation to vertically advance the scientific base of sensor-based modeling and control of complex systems. Integrated with the research is an ambitious education plan aiming at training the next generation of engineers to bring nonlinear thinking into advanced sensing, system informatics and control. Broad impacts will also be generated through developing curriculum modules, creating a museum exhibit, developing outreach workshops, as well as actively recruiting and involving under-represented students.Nonlinear dynamics in complex systems pose significant challenges for quality engineering. Real-time sensing of complex systems gives rise to big data. Realizing the full potential of big data for quality control requires fundamentally new methodologies to harness and exploit complexity. Many industries, including manufacturing and healthcare, have identified the urgent need to harness and exploit nonlinear dynamics for creating new products (or services) with exceptional features such as adaptation, customization, responsiveness, and quality in unprecedented scales. The success of this research project will advance the scientific base of modeling and control of complex systems by contributing new "nonlinear dynamics" concepts, methods and algorithms. In addition, this research will enrich the theory of nonlinear dynamics and expand its research domain to interdisciplinary applications in both manufacturing and healthcare. The research methodologies are also generally applicable to a wide variety of complex systems exhibiting nonlinear dynamics such as heart, brain, precision machining, biomanufacturing, and nanomanufacturing.

该学院早期职业发展（CAREER）计划拨款将为实时系统信息学，监测和控制创建基于传感器的非线性动力学方法。当系统中的各种实体合作、竞争或干扰时，非线性动力学就会出现。为了科普系统复杂性和动态环境，现代工业正在投资各种先进的传感系统。对非线性动力学的有效监测和控制将提高系统质量和完整性，从而产生重大的经济和社会影响。例如，在制造业中推进传感、测量和过程控制将提高整体质量和生产率，这对于在先进制造业中获得竞争优势至关重要。然而，有一个关键的差距，在知识基础，涉及到整合非线性动力学研究与质量工程。该奖项支持新方法开发和课程创新的基础研究，以垂直推进基于传感器的复杂系统建模和控制的科学基础。与这项研究相结合的是一项雄心勃勃的教育计划，旨在培养下一代工程师，将非线性思维带入先进的传感，系统信息学和控制。通过开发课程模块、创建博物馆展览、开展外联研讨会以及积极招募和参与代表性不足的学生，也将产生广泛的影响。复杂系统中的非线性动力学对质量工程提出了重大挑战。对复杂系统的实时感知产生了大数据。要充分发挥大数据在质量控制方面的潜力，需要全新的方法来利用和开发复杂性。包括制造业和医疗保健在内的许多行业都迫切需要利用非线性动力学来创造具有特殊功能的新产品（或服务），如适应性，定制性，响应性和质量。该研究项目的成功将为复杂系统的建模和控制提供新的“非线性动力学”概念、方法和算法。此外，本研究将丰富非线性动力学理论，并将其研究领域扩展到制造业和医疗保健领域的跨学科应用。研究方法也普遍适用于各种各样的复杂系统表现出非线性动力学，如心脏，大脑，精密加工，生物制造和纳米制造。

项目成果

Constrained Markov Decision Process Modeling for Sequential Optimization of Additive Manufacturing Build Quality

• DOI: 10.1109/access.2018.2872391
• 发表时间: 2018-09
• 期刊: IEEE Access
• 影响因子: 3.9
• 作者: B. Yao;Hui Yang

Dynamic network monitoring and control of in situ image profiles from ultraprecision machining and biomanufacturing processes

对超精密加工和生物制造过程中的原位图像轮廓进行动态网络监测和控制

• DOI: 10.1002/qre.2163
• 发表时间: 2017
• 期刊: Quality and Reliability Engineering International
• 影响因子: 2.3
• 作者: Kan, Chen;Yang, Hui
• 通讯作者: Yang, Hui

Markov Decision Process for Image-Guided Additive Manufacturing

• DOI: 10.1109/lra.2018.2839973
• 发表时间: 2018-05
• 期刊: IEEE Robotics and Automation Letters
• 影响因子: 5.2
• 作者: B. Yao;Farhad Imani;Hui Yang

Joint Multifractal and Lacunarity Analysis of Image Profiles for Manufacturing Quality Control

• DOI: 10.1115/1.4042579
• 发表时间: 2019-04-01
• 期刊: JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING-TRANSACTIONS OF THE ASME
• 影响因子: 4
• 作者: Imani, Farhad;Yao, Bing;Yang, Hui
• 通讯作者: Yang, Hui

Heterogeneous recurrence representation and quantification of dynamic transitions in continuous nonlinear processes

• DOI: 10.1140/epjb/e2016-60850-y
• 发表时间: 2016-06-20
• 期刊: EUROPEAN PHYSICAL JOURNAL B
• 影响因子: 1.6
• 作者: Chen, Yun;Yang, Hui
• 通讯作者: Yang, Hui