Manufacturing is entering a period of substantial innovation and change driven by digitization, increased integration of sensors into production equipment, increasingly available data, advances in robotics and automaton, and the “Internet of Things”. The combination of these advances provides unprecedented opportunities to develop new and better ways of doing manufacturing. Led by Dr. Chang, Intelligent System Lab has been working on the development of next-generation analytics and control frameworks for smart manufacturing powered by domain knowledge, advanced robotics, big data, IoT, and machine learning techniques.
Knowledge-guided Machine Learning Based Control for Smart Manufacturing Systems
This research aims to develop an integrated control framework that deliver optimal control decisions and coordinates different levels in production systems, e.g. machine-level, process-level, and system-level, to achieve higher efficiency and lower costs for the whole system.
Study on the Efficiency and Safety of the Human-Robot Collaboration
In this project, we focus on establishing a safe and efficient HRC system, in which robots will not be regarded as programmed tools but working partners of humans who can not only communicate but also learn from both humans and robots to improve the system performance cooperatively.
Integrated Modeling and Real-time Control of Advanced Manufacturing System
The rapid development in battery technology and increasing needs in energy storages has introduced enormous challenges and complexities on battery production systems. Battery manufacturing must quickly ramp up with the newly developed technologies, new tools and equipment, and resources in order to meet battery production needs for a variety of stationary and motive applications.
Modeling and Analysis of Multistage Manufacturing Systems with Quality Rework Loops
A multi-stage serial manufacturing system is stochastic and nonlinear that constantly faces the challenges of random disruption events. The machine random failure is one of the most concerned disruption events since it directly impacts the production throughput. When considering quality issues, the quality defect problem adds another kind of random disruption event, which inherently impact the system performance and lower the throughput.
Coordinated Control for Distributed Generation(DG) Micro-grid
Driven by wind and solar photovoltaics technology, the power industry is shifting towards a distributed generation (DG) paradigm. We focus on DC micro-grid, and then extend to grid connected DG system.
Simulation Modeling and Analysis for Readmission
System simulation and analysis methodology are developed to identify the key factors in the after-discharging systems which may contribute to high readmission rate, and hence to improve system performance and reduce readmission rate with lower cost.
Motion Planning for Human-Robot Collaboration based on Reinforcement Learning
Robots are good at performing repetitive tasks in modern manufacturing industries. However, robot motions are mostly planned and preprogramed beforehand with a notable lack of adaptivity to task changes. Even for slightly changed tasks, the whole system must be reprogrammed by robotics experts. Therefore, it is highly desirable to have a flexible motion planning method, with which robots can adapt to certain task changes in unstructured environments, such as production systems or warehouses, with little or no intervention needed from non-expert personnel. In this research, we propose a reinforcement learning (RL) based motion planning method to enable robots to automatically generate their motion plans across different tasks by learning from a few kinesthetic human demonstrations.
Gantry Work Cell Analysis and Machine Learning Based Gantry Assignment
A production line in a factory can be made up of multiple production stations or work cells.In each work cell, robots or humans move between machines/process steps to load and unload parts. In this project, we focus on the analysis of this kind of work cell, referred to as a gantry work cell.
Data-driven Modeling and Production System Real-time Diagnosis and Prognosis
The research integrates available sensor data with the knowledge of production system physical properties. Such methods can be transferred to a computer for system self-diagnosis/prognosis to provide users with deeper understanding of the underlying relationships between system status and performance, and to facilitate real-time production control and decision making.
Real-time Control for Energy Aware Operations for Smart Manufacturing Systems
This research establishes theory and develops automated real-time distributed control schemes to cope with the complex nature of manufacturing processes and systems to achieve higher energy efficiency and profit.
Supply Chain – Containerization Method Analysis and Control
Logistics cost is an important contributor to the overall cost in a supply chain system. By using collapsible containers, the frequency of return freight can be reduced and the return of containers can be optimized, leading to potential logistic cost savings.