RESEARCH PROJECTS
Our research approach is problem motivated -- that is, given a relevant,
interesting, real-world research problem in the cyber-physical systems design, analysis and fabrication
domain, we develop computational tools for the related problem.
During the course of research, we also
build physical devices and develop user interfaces for validation of the developed
computational tools. By combining engineering innovations with methods from machine
learning, computational geometry, statistics and optimization, metamodeling and sampling,
ontology, and uncertainty quantification, we strive to solve important research problems
in the cyber-physical system domain.
Our research is generously supported by
Current Funded Projects
Modernizing South Carolina Manufacturing Assets to Enable Industry 4.0
Develop an online industry 4.0 protocol that integrates legacy manufacturing equipment and
processes, connecting them to the cloud and processing their data with artificial intelligence,
so they can more effectively work together, regardless of their age.
Autonomous Integrated Tractor and Spraying System
The primary objective of this work is to build a fully autonomous operation pipeline for the unmanned
tractor, which can realize the functions of autonomous driving, obstacle detection and avoidance,
and intelligent control. We will develop a novel and robust autonomous operation system involving
tractor localization and navigation, obstacle detection, tractor actuation intelligent control,
and human-machine interface functions. The proposed operating system is built based on the necessary
hardware retrofitting of the human supervised tractor involving the camera, lidar, sonar, and onboard controller
Enabling Factory to Factory (F2F) Networking for Future Manufacturing across South Carolina
The proposed project pursues novel Cyber-Physical Production Systems (CPPSs) research agenda to
1) innovatively utilize state-of-art knowledge representations to avoid large data transfer,
2) fuse AI algorithms with human-in-the-loop data to enable machine-human interoperable decisions, and
3) validate the developed methodology on two case studies:
pharmaceutical manufacturing and automotive manufacturing with a human/machine collaboration focus.
Virtual Prototyping of Autonomy-Enabled Ground Systems (VIPR-GS)
The vision of VIPR-GS center grant is to develop innovative tools and methodologies
for virtual prototyping and agile physical prototyping, enabled by research breakthroughs
in offroad vehicle autonomy and vehicle propulsion, including smart management of fleet
energy resources. Specifically, our focus is on creating formal methods and associated
computational tools (such as Machine learning, Natural Language Processing, gaming and
simulation environments) to aid in the development of mission definition and the associated
key performance indicators.
An Integrated Multi-Material Digital Life Cycle Approach for Additive
Manufacturing of Ground Vehicle Structures and Components
The overarching goal of this project is to create a digital life cycle for understanding
the imapct of manufacturing processes on additively manufactured metallic and plastic
automotive components. In addition to the charcteristic properties of the components, the defects
generated during the process will be monitored and controlled using machine learning techniques
to manufacture products with minimal defects.
Multi-media Analytics Leading to Intent and Semantic Evidence (MALISE)
The project aspires to detect user-created fake videos of humans by leveraging state-of-the-art
hybrid-physics guided machine learning techniques to extract and verify the
semantic information contained in the video.
Smart Configuration Optimizer Through Transformative analYtics (SCOTTY)
This project focuses on developing algorithms to demonstrate fault mitigation in the powertrain
on a shipboard. Heterogenous information from multiple sensors will be processed by combining
physics-based and machine learning models to detect the presence of faults in maritime vessels
for their safe operation.
Hybrid modeling for energy efficient CNC grinding
In this project, we will develop a hybrid physics guided machine learning model to predict
the energy consumption of large-scale grinding processes to enable dynamic control of selected,
energy-intensive components.
COVIA: Computer Vision base Intelligent Assistant for Mistake Proofing of Complex
Maintenance Tasks on Navy Ships
The main goal of the proposed project is to investigate advanced deep learning based computer
vision methods and algorithms to enable next generation Handheld Augmented Reality (HAR) based
complex maintenance tasks.
Completed Projects
Physics LEArning (PLEA): A Hybrid Physics Guided Machine Learning Approach for Predictive
Modeling of Complex Systems.
In this research project, we propose to address the lack of success of purely data-driven constructs in
predictive modeling of complex systems in the presence of sparse and noisy data beyond their initial set
of training data. This research will lead to innovative hybrid methods, especially in studying
physically-grounded systems, that integrates systematic understanding about the systems in the form of
physics of the systems with data-driven machine learning approaches.
Coordinated Holistic Alignment of Manufacturing Processes
Small- and medium-sized organizations spend an exorbitant amount of time and money stitching together disparate
data from stove-piped legacy systems in an effort to match the value of insight provided by these dedicated systems.
The objective of this research project is development of a low cost solution that will enable manufacturing
organizations to overcome the issues associated with data heterogeneity present within and across various platforms,
in an effort to provide an alternative to costly enterprise systems. The solution provides semantic alignment of
the organization's data, leveraging a tiered and systematic ontology, to effectively characterize the process and
data associated with the product development process (PDP).
Uncertainty Propagation Methods for Networked Complex Systems
The objective of this research is development of a novel class of uncertainty quantification method for
networked complex systems. The main challenge that lies at the core of analyzing and synthesizing the dynamic
networks at the crux of modern day complex systems is: How do a collection of dynamical systems coupled
through a dense wiring topology behave as a unit in the presence of uncertainty? We are developing a
suite of novel computational uncertainty quantification methods to tackle this challenge.
IDEA: Intelligent Decision Enabled Application for additive manufacturing
The term 'rapid' in rapid manufacturing is quite misleading as the manufacturing time for even small-medium sized parts using additive manufacturing processes is found in the range of 6 hrs-12 hrs. Even, the algorithms to produce the optimal results are also computationally expensive. IDEA aims to develop an integrated data mining and optimization based routine focusing on (1) minimizing the energy consumption and material waste of the process, and (2) developing a computationally efficient technique to determine the process parameters using manufacturing by analogy.
Knowledge Representation and Design for Managing Product Obsolescence
The research objective of this project is to investigate two novel research approaches to understanding and managing technology obsolescence challenges: (1) a knowledge representation scheme and management system that can facilitate information sharing and collaboration for obsolescence management and mitigation efforts between existing tools and across different organizations, and (2) fundamental principles, teachable methods, and guidelines for designing product architectures that can evolve with changing requirements, enabling proactive obsolescence management across the entire product life cycle.
Distributed Computational Design Environment (DiCoDE)
Communication of a design concept is difficult among a multidisciplinary team, especially if the team is distributed geographically. Conceptual design in distributed environment is collaborative in nature. This research explores issues related to conceptual design in a distributed environment within a computational framework. The focus is on investigating the use tablet PCs for conceptual design communication in geographically dispersed team setting and classroom setting.
Wastewater aeration process optimization
Large scale process industries such as wastewater are typically designed with due consideration of environmental regulations. Often such designs are based on intuitions and leave a large scope for optimization, specifically at the process energy consumption level. The aim of this project was to perform data driven study of Buffalo Sewer Authority and develop energy minimization schemes while maintaining the acceptable water quality. Data driven models capturing the non-linear behavior of the aeration process is extracted and optimized with an aim to minimize the process energy consumption and material waste. Different energy savings scenarios are analyzed.
META-II
Conceptual design is perhaps the most crucial task of engineering design process. The automated generation of conceptual designs is a non-trivial task. This project explored the use of graph grammar as a concept generator for complex systems. In particular, graph grammar rules were developed for generating conceptual designs of NASA ADAPTS electrical power system and integrated with modeling packages like Modellica to simulate the performance of each design.
i-FAB
Given a CAD file of a part, graph grammar algorithms were used for automated process plannning, machine selection, and tool selection.