Finite element analysis of micro end mill and simulation of burr formation in machining al6061-t6

The recent technological progressions in industries have offered ascent to the continually growing requests for microstructures, sensors, and parts. Micro-milling is a promising method to create these scaled down structures, sensors, and parts. Yet, micromilling still confronts some significant difficulties, tormenting further provision of this innovation. The most noticeable around them is micro burr formation. Burrs created along the completed edges and surfaces in micro-milling operation have huge effect on the surface quality and performance of the completed parts and microstructures. In any case, deburring of micro-parts is not conceivable because of bad accessibility and tight tolerances in micro segments. One of the methods to minimize micro burr formation in micro milling is by enhancing the geometry of the device. As minimization of micro burrs still remains a key test in micro machining, not many researchers have worked in this field. The main aim of the research work is to present finite element analysis of flat end mill micro cutters used in micro milling by varying geometry of the tools. Apart from this, study has been done in detail on burr formation in micro milling and what factors affect it. Burr formation simulation has been carried out while varying the tool geometry. The outcome of the research will be a static finite element analysis of micro burrs formed during micro-milling which can help in determining tool life and a detailed dynamic analysis of micro burrs formed during micro-milling operation in Al6061-T6 which can benefit the aerospace industry in various ways. The results obtained during the analysis may be used for further research for burr minimization through tool optimization and process control

Neutrino phenomenology and scalar Dark Matter with A4 flavor symmetry in Inverse and type II seesaw

AbstractWe present a TeV scale seesaw mechanism for exploring the dark matter and neutrino phenomenology in the light of recent neutrino and cosmology data. A different realization of the Inverse seesaw (ISS) mechanism with A4 flavor symmetry is being implemented as a leading contribution to the light neutrino mass matrix which usually gives rise to vanishing reactor mixing angle θ13. Using a non-diagonal form of Dirac neutrino mass matrix and 3σ values of mass square differences we parameterize the neutrino mass matrix in terms of Dirac Yukawa coupling “y”. We then use type II seesaw as a perturbation which turns out to be active to have a non-vanishing reactor mixing angle without much disturbing the other neutrino oscillation parameters. Then we constrain a common parameter space satisfying the non-zero θ13, Yukawa coupling and the relic abundance of dark matter. Contributions of neutrinoless double beta decay are also included for standard as well as non-standard interaction. This study may have relevance in future neutrino and Dark Matter experiments

An Empirical Analysis of Women Empowerment within Muslim Community in Murshidabad District of West Bengal, India

Women empowerment is a contemporary issue for developing countries like India. The rates of women empowerment are in a vulnerable condition within the largest Muslim minority community of India. In this paper, an attempt has been taken to present an empirical analysis of Muslim women empowerment within purposively selected Murshidabad district of West Bengal regarding the highest concentration of Muslim people (63.67%) all over the country. For showing the multidimensional aspects of women empowerment, a Cumulative Empowerment Index (CEI) has been constructed using 22 key indicators that act as explained variables covering four dimension of women empowerment, i.e. control over economic resources, control over household decision making, women’s mobility and political awareness. Nine explanatory (independent) variables have also been selected as determinants of women empowerment (CEI). Based on the multiple regression results the study finds statistically significant impact of accessing any type of media, family structure, family headship, household income, paid work and duration of marital life on Cumulative Empowerment Index of Muslim women at the study area. It concludes that active participation of GO’s and local NGO’s in bringing change of traditional beliefs of Muslim family and gaining awareness about women’s rights and practices can accelerate the women empowerment process within Muslim community of Murshidabad district. Keywords: Women empowerment, Cumulative Empowerment Index, Muslim community, Multiple regressio

A music context for teaching introductory computing

We describe myro.chuck, a Python module for controlling music synthesis, and its applications to teaching introductory computer science. The module was built within the Myro framework using the ChucK programming language, and was used in an introductory computer science course combining robots, graphics and music. The results supported the value of music in engaging students and broadening their view of computer science

Llamas Know What GPTs Don't Show: Surrogate Models for Confidence Estimation

To maintain user trust, large language models (LLMs) should signal low confidence on examples where they are incorrect, instead of misleading the user. The standard approach of estimating confidence is to use the softmax probabilities of these models, but as of November 2023, state-of-the-art LLMs such as GPT-4 and Claude-v1.3 do not provide access to these probabilities. We first study eliciting confidence linguistically -- asking an LLM for its confidence in its answer -- which performs reasonably (80.5% AUC on GPT-4 averaged across 12 question-answering datasets -- 7% above a random baseline) but leaves room for improvement. We then explore using a surrogate confidence model -- using a model where we do have probabilities to evaluate the original model's confidence in a given question. Surprisingly, even though these probabilities come from a different and often weaker model, this method leads to higher AUC than linguistic confidences on 9 out of 12 datasets. Our best method composing linguistic confidences and surrogate model probabilities gives state-of-the-art confidence estimates on all 12 datasets (84.6% average AUC on GPT-4)