报告人：Ran Jin
时间：10月28日（周二）上午10:30-11:30
地点：机电所老楼4楼会议室
Data Fusion for Quality Engineering in Manufacturing Systems
Ran Jin
Assistant Professor
Grado Department of Industrial and Systems Engineering
Virginia Tech, Blacksburg, VA 24061, USA
jran5@vt.edu
http://www.ise.vt.edu/People/Faculty/Bios/JinRan_bio.html
Ran Jin
Assistant Professor
Grado Department of Industrial and Systems Engineering
Virginia Tech, Blacksburg, VA 24061, USA
jran5@vt.edu
http://www.ise.vt.edu/People/Faculty/Bios/JinRan_bio.html
The rapid advancements of sensors, sensor networks and computing technologies have resulted in both temporally and spatially dense data-rich environments in manufacturing systems. With massive data readily available, there is a pressing need to develop advanced methodologies to reveal inherent relationships among events, in order to meet various decision making objectives, such as monitoring, detection, diagnosis and control. Addressing the need is considered very challenging because of a collection of factors, such as the complexity of the manufacturing system, the uncertainty, heterogeneity and high dimensionality of the data, and the increasing expectation and requirements on the decision-making capabilities. A unified data fusion methodology is needed to integrate heterogeneous types of data, information and variables with engineering domain knowledge in manufacturing system modeling and analysis.
Motivated by this, this presentation presents an ensemble modeling method to integrate experimental and observational data for manufacturing modeling. In modern manufacturing scale-up efforts, design of experiments is widely used to identify optimal process settings, followed by production runs to validate these process settings. Both types of data are collected. However, current methodologies often use a single type of data for manufacturing modeling. The proposed method uses a constrained likelihood approach, where the constraints incorporate the sequential nature and inherent features of the two types of data. It therefore achieves better estimation and prediction than conventional methods. Simulations and a case study on wafer manufacturing are provided to illustrate the merits of the proposed method.
Motivated by this, this presentation presents an ensemble modeling method to integrate experimental and observational data for manufacturing modeling. In modern manufacturing scale-up efforts, design of experiments is widely used to identify optimal process settings, followed by production runs to validate these process settings. Both types of data are collected. However, current methodologies often use a single type of data for manufacturing modeling. The proposed method uses a constrained likelihood approach, where the constraints incorporate the sequential nature and inherent features of the two types of data. It therefore achieves better estimation and prediction than conventional methods. Simulations and a case study on wafer manufacturing are provided to illustrate the merits of the proposed method.
Ran Jin 简介：
Dr. Ran Jin is an Assistant Professor at the Grado Department of Industrial and Systems Engineering at Virginia Tech. He received his Ph.D. degree in Industrial Engineering from Georgia Tech, Atlanta, his master’s degrees in Industrial Engineering and in Statistics, both from the University of Michigan, Ann Arbor, and his bachelor’s degree in Electronic Engineering from Tsinghua University, Beijing.
Dr. Jin’s research interests are in engineering driven data fusion for manufacturing system modeling and quality improvement, such as quality engineering in new product realization and manufacturing scale-up, and variation reduction based on spatial correlated responses. His research includes quality control in wafer manufacturing, ingot crystal growth manufacturing, continuous fiber manufacturing, and thermal spray coating processes. He is a member of Institute of Operations Research and the Management Sciences (INFORMS), Institute of Industrial Engineers (IIE), and American Society of Mechanical Engineers (ASME). For more details about his research, please visit http://www.ise.vt.edu/People/Faculty/Bios/JinRan_bio.html.
Dr. Jin’s research interests are in engineering driven data fusion for manufacturing system modeling and quality improvement, such as quality engineering in new product realization and manufacturing scale-up, and variation reduction based on spatial correlated responses. His research includes quality control in wafer manufacturing, ingot crystal growth manufacturing, continuous fiber manufacturing, and thermal spray coating processes. He is a member of Institute of Operations Research and the Management Sciences (INFORMS), Institute of Industrial Engineers (IIE), and American Society of Mechanical Engineers (ASME). For more details about his research, please visit http://www.ise.vt.edu/People/Faculty/Bios/JinRan_bio.html.
此次学术报告也会涉及到Virginia Tech在亚洲的招生。有意出国的同学可于10月28日现场咨询