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Department of Mechanical Engineering

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Undergraduate
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Faculty
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Senior Design Projects

In this section

About
Undergraduate
Graduate
Faculty
Research
Industrial Advisory Board
Resources
Senior Design Projects

Department of Mechanical Engineering

Video: Mechanical Engineering - making the world work better

Mechanical engineers design and develop everything you think of as a machine—from an artificial liver to power tools. We design:

everything that is mechanical—wheels and axles, airplane bodies, pumps, hard drives, heart-lung machines, and much more
systems that produce power and systems that consume power, such as refrigeration equipment, elevators, and loud-speakers
all the tools and processes necessary to make an infinite variety of products: from robots that assemble cars to methods for shaping metals and plastics

Mechanical engineering is an extremely broad field, extending from the technology to freeze living tissue to the exploration of ocean currents. Join us in this exciting, challenging, and rewarding profession.

Academic programs

BS in Mechanical Engineering
Manufacturing - Undergraduate Concentration
MS in Mechanical Engineering
Graduate Certificate in Industrial & Systems Engineering
PhD in Engineering & Applied Science
PhD in Biomedical Engineering & Biotechnology

Department News

News

Hangjian Ling receives NSF CAREER award

The $505K award provides funding for research and student training in hydrodynamics and marine technology.

UMass Dartmouth signs first international accelerated MS degree agreement

International agreement will create an accelerated pathway for MS degree in mechanical engineering with an industrial and systems engineering option

drop of water, photo courtesy of Pixabay

Charting the blue economy

Clean energy project earns UMassD student team “Best Poster” award in the Marine Energy Collegiate Competition.

Contact

Department of Mechanical Engineering

Science & Engineering Building, Room 116
UMass Dartmouth
285 Old Westport Road
Dartmouth, MA 02747-2300

508-999-8492
508-999-8881
scunha@umassd.edu

Chairperson

Sankha Bhowmick, PhD

Professor / Chairperson
Mechanical Engineering
Textiles 208

508-999-8619
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Events

Apr

3:00PM

Mechanical Engineering MS Project Presentation by Mr Joshua James Banez

Mechanical Engineering MS Project Presentation by Mr. Joshua James Banez DATE: April 24, 2024 TIME: 3:00 p.m. - 5:00 p.m. LOCATION: Science & Engineering (SENG), Room 110 (Materials Science Lab) Zoom link: https://us04web.zoom.us/j/6854837191?pwd=YmZJY3liZjRiM1NXT1VJNnlpMi9uUT09 (Contact scunha@umassd.edu or mraessi@umassd.edu for the Meeting ID# and Passcode) TOPIC: Advancing the AST Probe Calibration Process through Computational and Experimental Analyses and Novel Fixture Design ABSTRACT: Ametek Brookfield's Advanced Sensing Technology (AST) probe calibration process currently takes over 109 hours to fully complete, which is very long and costly. By shortening the calibration process, lead-times on AST instruments could be decreased, more probes could be calibrated, and the cost to manufacture would be reduced. AST probes are high-tech and precise measurement tools used in a variety of applications to report temperature and viscosity of the desired fluid. The calibration process for such an instrument requires time in an environmental chamber for three different cycles: the burn-in cycle, the air cycle, and the oil cycle. The burn-in cycle is a 27-hour process that cycles the temperature to set a base for the AST probes. The probes are then put through a 16-hour calibration where the probes are suspended in air and then a 66-hour calibration where the probes are submersed in a calibrated oil standard. The solution chosen to speed up calibration required re-design of the current tray fixture for holding the oil in the calibration cycle. The current fixture takes up a lot of mass in the system during both the air and oil cycles of the calibration process. This mass leads to a greater heat capacity in the system which, in turn, adds to the time it takes for the system to come to steady state at each calibration point. The project involved both designing a new fixture and analyzing the time that could be saved in the calibration cycle. Experimental testing with prototypes was then used alongside the simulation to provide more accurate results. Using a combined knowledge of heat transfer, thermal systems, design, and manufacturing engineering this project was able to create a novel fixture that reduced the heat capacity in the system by 31.65% during the oil calibration. This led to an estimated time savings of 33 hours, or 50%, for the just oil cycle from the simulation analysis. The prototypes were machined and experimentally tested to show an actual time savings for the oil cycle of 29.8 hours resulting in an oil cycle that takes only 54.85% of the current time. The new design delivers similar levels of accuracy in the calibration process, while significantly shortening the process. ADVISOR: - Dr. Mehdi Raessi, Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: - Dr. Wenzhen Huang, Professor, Department of Mechanical Engineering, UMass Dartmouth - Dr. Sankha Bhowmick, Professor / Chairperson, Department of Mechanical Engineering, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Mehdi Raessi (mraessi@umassd.edu).

See description for location

Apr

1:00PM

CPE Master of Science Thesis Defense by Christopher Dentremont - ECE Department

Topic: Dataset Generation for Deep Learning to Authenticate Wireless Sensor Network (WSN) at Physical Layer for Structural Health Monitoring (SHM) of Transportation Infrastructure Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/93281343753 Meeting ID: 932 8134 3753 Passcode: 518247 Abstract: A wireless sensor network (WSN) for structural health monitoring (SHM) is a network with autonomous, spatially distributed sensor nodes that communicate wirelessly in a cooperative way to monitor physical or environmental conditions. WSN for SHM has garnered interest for protecting transportation infrastructure for the safe operation and maintenance of bridges due to their ability to collect real-time data. Two concerns that arise when designing and deploying these systems are energy consumption and information security. Limited battery capacity on sensor nodes, especially on bridges, can significantly shorten WSN's lifetime. WSNs are left vulnerable to attacks on data integrity, confidentiality and availability from malicious actors masquerading as sensor nodes. This thesis proposes a scheme to protect data transmissions in WSNs for SHM without sacrificing energy consumption. The scheme solves these problems by combining state-of-the-art technologies in deep learning, radio frequency (RF) fingerprinting and RF energy harvesting. RF Fingerprinting leverages process imperfections in transceivers that can be used in a deep neural network to authenticate known sensor nodes. Deep learning is also less computationally intensive than more common forms of data security like encryption and decryption. RF energy harvesting harnesses electromagnetic waves to convert to electrical energy that powers sensor nodes wirelessly. Deep learning requires a dataset to train the model and each device needs its own dataset generation just like collecting fingerprints to establish a directory. This unique feature due to WSN for SHM of transportation infrastructure calls for the need for a framework to systematically generate datasets from individual sensor nodes. This brings out a novel approach of common applications in deep learning. The work shown acts as a proof of concept for this framework of data generation by building a prototype to present its feasibility through experimentation with using RF energy harvesting. This work also provides a framework for generating a dataset of device RF fingerprint to be used in a deep learning network to authenticate each sensor node. Co-Advisor(s): Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Hong Liu via email at hliu@umassd.edu

See description for location

Apr

2:30PM

CPE Master of Science Thesis Defense by Bryce Afonso - ECE Department

Topic: Network-less Wireless Sensing for Structural Health Monitoring (SHM) of Bridges: Unmanned Aerial Vehicle (UAV) Investigations Location: Lester W. Cory Conference Room, Science & Engineering Building (SENG), Room 213A Zoom Conference Link: https://umassd.zoom.us/j/93281343753 Meeting ID: 932 8134 3753 Passcode: 518247 Abstract: The Internet of Things (IoT) has significantly advanced the application of Wireless Sensor Networks (WSNs) in Structural Health Monitoring (SHM), particularly for civil engineering infrastructure such as bridges. Despite the advancements, the widespread application of WSNs in SHM remains hindered by their limited network lifetime, posing a significant hurdle to their adoption. Furthermore, IoT and WSNs open a new attack surface. Designing SHM systems with wireless sensors utilizing no network allows system resiliency to cyber-attacks. Unmanned Aerial Vehicles (UAVs) have been heralded for their potential to overcome these limitations through secure and efficient data collection. This thesis expands on the existing UAV application by proposing a novel UAV-assisted WSN system that employs Bluetooth Low Energy (BLE) as the communication protocol for synchronized data gathering in SHM systems. Our design diverges from traditional multi-hop WSNs by leveraging UAVs as mobile data sinks, reducing the energy burden on individual sensor nodes, and significantly prolonging the sensor's operational life. Through an analytical study, we demonstrate that our UAV-BLE system offers a remarkable improvement in network lifetime in comparison to conventional network routed WSNs. Additionally, the use of BLE facilitates a lightweight authentication scheme, providing secure wireless communication between sensor nodes and the UAV. Thus, this novel approach enhances the overall robustness and longevity of SHM systems. A proof-of-concept implementation utilizing a PASCO bridge kit equipped with wireless load cell sensors, demonstrates the feasibility of our approach. To the best of the authors' knowledge, this is the first exploration of a BLE-centric synchronization scheme in the context of SHM, marking a significant leap toward secure, safe, reliable, and efficient monitoring of civil engineering structures. Co-Advisor(s): Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Liudong Xing, Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Tzuyang Yu, Professor, Department of Civil and Environmental Engineering, UMASS Lowell NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Hong Liu via email at hliu@umassd.edu

See description for location

Apr

9:00AM

Mechanical Engineering Senior Design (Capstone) Presentations, Class of 2024

Mechanical Engineering (MNE) Senior Design (Capstone) Presentations April 30, 2024 9:00 a.m. - 4:30 p.m. (Poster and prototype preview begins at 8:00 a.m.) Woodland Commons The Mechanical Engineering Department is proud to share this highly anticipated event with students, faculty, staff, family, friends, and any other interested guests! This is a culmination of the Class of 2024's Senior year team project with industry, or UMD research faculty. Attend all day, or come and go as your schedule allows. For more information please contact Dr. Hamed Samandari/Instructor (hsamandari@umassd.edu) or Sue Cunha/Administrative Assistant (scunha@umassd.edu).

Woodland Commons, UMass Dartmouth Campus

Apr

10:00AM

ELEC Research Component of PhD Qualifier Exam by Joshua Steakelum - ECE Department

Topic: Multi-phase Algorithm Design for Accurate and Efficient Model Fitting Location: Claire T. Carney Library (LIB), Room 314 Zoom Conference Link: https://umassd.zoom.us/j/98963429286 Meeting ID: 989 6342 9286 Passcode: 283650 Abstract: Recent research applies soft computing techniques to fit software reliability growth models. However, runtime performance and the distribution of the distance from an optimal solution over multiple runs must be explicitly considered to justify the practical utility of these approaches, promote comparison, and support reproducible research. This paper presents a meta-optimization framework to design multi-phase algorithms for this purpose. The approach combines initial parameter estimation techniques from statistical algorithms, the global search properties of soft computing, and the rapid convergence of numerical methods. Designs that exhibit the best balance between runtime performance and accuracy are identified. The approach is illustrated through nonhomogeneous Poisson process and covariate software reliability growth models, including a cross-validation step on data sets not used to identify designs. The results indicate the nonhomogeneous Poisson process model considered is too simple to benefit from soft computing because it incurs additional runtime with no increase in accuracy attained. However, a multi-phase design for the covariate software reliability growth model consisting of the bat algorithm followed by a numerical method achieves better performance and converges consistently, compared to a numerical method only. The implementation of a framework-designed algorithm into a software reliability tool is demonstrated. The proposed approach also supports higher-dimensional covariate software reliability growth model fitting suitable for implementation in further tools. Co-Advisor(s): Dr. Lance Fiondella, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth Committee Members: Dr. Hong Liu, Commonwealth Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth; Dr. Ruolin Zhou, Associate Professor, Department of Electrical & Computer Engineering, UMASS Dartmouth NOTE: All ECE Graduate Students are ENCOURAGED to attend. All interested parties are invited to attend. Open to the public. *For further information, please contact Dr. Lance Fiondella via email at lfiondella@umassd.edu

Claire T. Carney Library, Room 314

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Last modified: Tue, Mar 12, 2019, 11:41 by Christine Allen

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