Events
Observatory Open House For updates on weather conditions please refer to www.assne.org
Spring 2024 14-week full session classes end, last class before final exam.
Spring classes end today.
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).
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
Today is study day.
Spring 2024 Second 7-week session classes end, last class before final exam.
Spring 2024 14-week full session final exams begin.
Examinations begin today.
Spring 2024 Second 7-week session final exam day.
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).
Spring 2024 Second 7-week session grades are due, 72 hours from final exam day.