Business Center

Business Center (Tutoring)

Liberal Arts, Room 010

Welcome to the Business Center!

Face to Face and Online tutoring are now available.

The Business Center has tutors available for the following courses:

Click on "SCHEDULE APPOINTMENT" to see availability.

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Additional Services:

In addition to individual tutoring, the following services are also available to eligible students through a federal Student Support Services Grant.

Personal Mentors
Facilitated Study Groups
Study Partners
Grad School Applications Assistance
Needs Assessments
Cultural Activities

Please call the Business Center if you can not attend an appointment. Students with 5 missed appointments will be automatically blocked from making additional appointments.

If you have a Max Missed block on your account, you are required to meet with the Center Director before the block is removed.

Business Center

508.999.8710

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Events

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

May

3:00PM

Mechanical Engineering MS Project Presentation by Mr. Noah Whitney

Mechanical Engineering MS Project Presentation by Mr. Noah Whitney DATE: May 3, 2024 TIME: 3:00 P.M. - 5:00 P.M. LOCATION: Zoom link: https://umassd.zoom.us/j/93989772725?pwd=Ulk5ME9KYnpvMGhBU2toZ1dKeHE3dz09 (Contact scunha@umassd.edu for Meeting ID# and Passcode) TOPIC: Ion Beam Figuring Prototype for Guiding the Polishing Process of Optical Substrates ABSTRACT: For high efficiency optical components, substrate surface smoothness is critical. This project aims to create a polishing method for substrates that can be performed at Plymouth Grating Laboratory, for the purpose of creating ultra-high efficiency diffraction gratings. Traditional polishing methods, such as pitch polishing and lap polishing, use an abrasive slurry to mechanically smooth substrate surfaces. This method can be quite expensive and often fails for large scale optics. Therefore, a non-mechanical polishing technique called Ion Beam Figuring is proposed as an alternative method for substrate polishing. This method can be achieved on site at Plymouth Grating Laboratory while simultaneously reducing cost and increasing the likelihood of a successful polish. To achieve this method of polishing, a framework is developed for experimentally profiling a radio frequency ion source, via broadband spectroscopy. Next, a prototype 1-dimensional Ion Beam Figuring method is created using a custom MATLAB program. This program yields a dwell time map, which guides the figuring process by determining the position and time for which the ion beam etches the substrate. This prototype Ion Beam Figuring program will be based on a Fourier transform deconvolution method. Once this program was created, initial validations were performed using test case surface profiles to ensure the program properly computes parameters used in the IBF process. This project provides a foundation to produce large polished substrates with higher reliability and a significantly decreased cost. This is a key step in creating high efficiency meter class diffraction gratings to be used in some of the highest power laser systems in the world. These laser systems can be used in fusion, biomedical, defense, and semiconductor industries. ADVISOR: - Dr. Jun Li, Assistant Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBERS: - Dr. Wenzhen Huang, Professor, Department of Mechanical Engineering, UMass Dartmouth - Dr. Alfa Heryudono, Associate Professor, Department of Mathematics, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Jun Li (jun.li@umassd.edu).

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May

1:00PM

Mechanical Engineering MS Thesis Defense by Ms. Stephanie DeCarvalho

Mechanical Engineering MS Thesis Defense by Ms. Stephanie DeCarvalho DATE: May 10, 2024 TIME: 1:00 P.M. - 3:00 P.M. LOCATION: ZOOM link: https://umassd.zoom.us/j/91399406281?pwd=aUNpWS8ybFQ5eFNIWlVmRGNjbmlaZz09 (Contact scunha@umassd.edu for Meeting ID and PassCode) TOPIC: Computational Modeling of Materials and Structures for Biomedical Applications: from 3D Printed Implants to Tissue Growth ABSTRACT: Computational modeling has been increasingly used to aid and improve engineering design, fabrication, and manufacturing. In the biomedical field, scientists and clinicians could use computational models to better understand biological phenomena and develop more precise treatment strategies. This thesis employs computational modeling of materials and structures to study two examples in biomedical applications: the development of a patient-specific additively manufactured knee implant and the prediction of an embryonic chick in its first stages of growth. Additive manufacturing (AM) has emerged as an innovative way of manufacturing products of complicated and customized geometries. Bioengineering has a special interest in AM as the possibility of creating patient specific implants that can help increase satisfaction and comfort with procedures. This study explores the use of patient data in combination with finite element modeling and analysis to evaluate the performance of an AM knee implant. The approach is demonstrated on a distal femur replacement for a 50-year-old male patient from the open-access Natural Knee Data. The performance of the implant is influenced by the printing process parameters that are used to print the part. The results show that build orientations have a significant impact on both shape distortions and residual stresses. Understanding the developmental growth from a single cell into a more complex multicellular structure contributes to topics such as tissue engineering and growth defects as well as developing individual treatments. In combination with experimental results, computational analysis can increase the understanding of the behavior of organisms. Morphomechanics are used to create a computational model to simulate the tissue growth in the embryonic chick during the early stages of its development. As the chick embryo develops, the behavior and positioning of the embryo is affected by the membrane in which it develops. The effects of the growth and its surroundings result in a series of coupled bending and twisting of the embryonic body. Using computational modeling in personalized medical implants and developmental biology, this study contributes to the goals of advancing precision health initiatives. Patient-specific implants that are created to be a perfect fit would increase the probability of more patients recovering with diminished pain, increased mobility, and an improvement in their quality of life. The deformation and behavior of biological development supports the research of quantifying health conditions that may result from environmental, developmental, or genetic influences. Understanding these factors supports the advancement of preventative medical research to preserve the health of patients. ADVISOR: - Dr. Jun Li, Assistant Professor of Mechanical Engineering, College of Engineering, UMass Dartmouth COMMITTEE MEMBERS: - Dr. Wenzhen Huang, Professor of Mechanical Engineering, College of Engineering, UMass Dartmouth - Dr. Alfa Heryudono, Associate Professor of Department of Mathematics, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Jun Li (jun.li@umassd.edu).

See description for location

May

10:00AM

Mechanical Engineering MS Thesis Defense by Mr. Anthony Encarnacion

Mechanical Engineering MS Thesis Defense by Mr. Anthony Encarnacion DATE: May 14, 2024 TIME: 10:00 a.m. - 12:00 p.m. LOCATION: Science & Engineering (SENG), Room 110 Zoom link: https://us05web.zoom.us/j/88265834821?pwd=PTi4awzgy5dIWiXFI179B9jUP0ype5.1 (contact scunha@umassd.edu or aencarnacion for Meeting ID# and PassCode) TOPIC: The Development and Implementation of a MATLAB Based Model Representing the Power-Take Off Unit of a Wave Energy Converter ABSTRACT: This thesis presents the development and implementation of a MATLAB-based model designed to represent the Power-Take Off (PTO) unit of the Maximal Asymmetric Wave Energy Converter (MADWEC) device. The objective was to create a model based on empirical data and mechanical principles to accurately represent a table-top prototype of the MADWEC PTO. This model will serve as a predictive tool, analyzing the performance of the PTO unit under various wave conditions and enabling the selection of optimal configurations based on the deployment location or power requirements. The computational model incorporates the PTOs components, including a dual-dispensing reel, counterweight rewind mechanism, slip clutch, one-way clutch, gearbox, and generator. Leveraging the computational resources of MATLAB and its Simulink environment, the model was developed with an overall error of 1.36% compared to empirical data. This research details the development process of the model, including empirical data acquisition, analysis, and model optimization techniques. A performance estimation for the Nantucket Sound area indicated the potential power generation capabilities of the device, estimated at approximately 0.19 kilowatts an hour or 1.664 megawatts annually for that location. The study showcases a robust approach to predicting the efficiency and power output of MADWEC's PTO unit, providing a valuable tool for researchers and engineers in the field of renewable energy. It contributes to the understanding of WEC operations and supports the advancement of marine renewable energy systems by aiding in the design and optimization of WEC prototypes. ADVISORS: - Dr. Daniel MacDonald, Professor, Department of Civil & Environmental Engineering, UMass Dartmouth - Dr. Mehdi Raessi, Professor, Department of Mechanical Engineering, UMass Dartmouth COMMITTEE MEMBER: - Dr. Kihan Park, Assistant Professor, Department of Mechanical Engineering, UMass Dartmouth Open to the public. All MNE students are encouraged to attend. For more information, please contact Dr. Daniel MacDonald (dmacdonald@umassd.edu).

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