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Mathematical modelling and optimization of parameters for Al-Si-Fe aluminum alloy reinforced by Al 2 O 3 and graphite particles using Taguchi method

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Materials Today: Proceedings 2021年 44卷 979-986页

作者： Robale Dinsa Daba Devendra Kumar Sinha Habtamu Beri Satyam Shivam Gautam Gulam Mohammed Sayeed Ahmed Ram Sewak Singh Program of Mechanical Design and Manufacturing Engineering School of Mechanical Chemical and Materials Engineering ASTU ADAMA Ethiopia Department of Mechanical Engineering North Eastern Regional Institute of Science and Technology Itanagar India Department of Electronics and Communication School of Electrical Engineering and Computing ASTU ADAMA Ethiopia

In the present scenario the aluminum and its alloys are preferred due to requirement of lightweight high strength material. The present research article interrogates experimentally the study of actual density and porosity using powder metallurgy method. The experimental design is done using Taguchi L 16 orthogonal array. Actual density and porosity has been analyzed based on ‘smaller is best’ basis. Actual density and porosity are calculated using Archimedes principle. The leverage of four levels and five process parameters such as weight percentage of Al 2 O 3 , weight percentage of graphite, compaction pressure, compaction time and milling time are considered. Results reveal that Al 2 O 3 weight percentage has the highest leverage on density followed by compaction time and graphite weight percentage.

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Influence of Crystallographic Orientation on the Deformation of Ag Nanoparticles During High-Speed Impact

SSRN

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SSRN 2022年

作者： Becker, Michael F. Kovar, Desiderio Chitrakar, T.V. The University of Texas at Austin AustinTX78712 United States Materials Science and Engineering Program Texas Materials Institute United States Department of Electrical and Computer Engineering United States Department of Mechanical Engineering Center for Additive Manufacturing and Design Innovation

Several related aerosol processes utilize high velocity impact of solid nanoparticles to produce nanograined films including the aerosol deposition method, jet molding, vacuum kinetic spraying, and micro cold spray. Although the influence of important variables such as particle size and particle impact velocity, on particle deformation and film formation have been previously explored, other variables have not been systematically explored. Because particles impact with a full range of orientations, an understanding of the effect of particle orientation is required to predict film microstructures. Molecular dynamics simulations were conducted to determine the influence of particle crystallographic orientation on the particle deformation upon impact and the resulting microstructure of the deposit. We show that the orientations that produce the largest overall particle deformation are not correlated to orientations where initiation of plastic deformation is easiest. Rather, the deformation is heterogenous and depends on the mechanisms responsible for deformation. Two deformation mechanisms are identified: 1) dislocation plasticity and 2) disordering followed by viscous flow. The fraction of the atoms in the impacting particle that experience permanent deformation by each mechanism is quantified as a function of particle orientation. The implications of the effects of particle crystallographic orientation on film microstructures are also discussed. © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

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Correction: Materials laboratories of the future for alloys, amorphous, and composite materials (MRS Bulletin, (2025), 50, 2, (190-207), 10.1557/s43577-024-00846-y)

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MRS Bulletin 2025年 1-2页

作者： Banerjee, Sarbajit Meng, Y. Shirley Minor, Andrew M. Zhang, Minghao Zaluzec, Nestor J. Chan, Maria K.Y. Seidler, Gerald McComb, David W. Agar, Joshua Mukherjee, Partha P. Melot, Brent Chapman, Karena Guiton, Beth S. Klie, Robert F. McCue, Ian D. Voyles, Paul M. Robertson, Ian Li, Ling Chi, Miaofang Destino, Joel F. Devaraj, Arun Marquis, Emmanuelle A. Segre, Carlo U. Liu, Huinan H. Yang, Judith C. Momeni, Kasra Misra, Amit Abdolrahim, Niaz Medvedeva, Julia E. Cai, Wenjun Sehirlioglu, Alp Dizbay-Onat, Melike Mehta, Apurva Graham-Brady, Lori Maruyama, Benji Rajan, Krishna Warner, Jamie H. Taheri, Mitra L. Kalinin, Sergei V. Reeja-Jayan, B. Schwarz, Udo D. Simon, Sindee L. Brown, Craig M. Department of Chemistry and Department of Materials Science & Engineering Texas A & M University College Station United States Pritzker School of Molecular Engineering The University of Chicago Chicago United States Department of Materials Science & Engineering University of California Berkeley Berkeley United States National Center for Electron Microscopy Molecular Foundry Lawrence Berkeley National Laboratory Berkeley United States Center for Nanoscale Materials Argonne National Laboratory Lemont United States Department of Physics University of Washington Seattle United States Department of Materials Science & Engineering The Ohio State University Columbus United States Department of Mechanical Engineering and Mechanics Drexel University Philadelphia United States School of Mechanical Engineering Purdue University West Lafayette United States Department of Chemistry University of Southern California Los Angeles United States Department of Chemistry Stony Brook University The State University of New York Stony Brook United States Department of Chemistry University of Kentucky Lexington United States Department of Physics University of Illinois at Chicago Chicago United States Department of Materials Science & Engineering Northwestern University Evanston Bangladesh Department of Materials Science & Engineering University of Wisconsin–Madison Madison United States Department of Materials Science and Engineering University of Pennsylvania Philadelphia United States Department of Mechanical Engineering and Materials Science Duke University Durham United States Department of Chemistry Creighton University Omaha United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory Richland United States Department of Materials Science & Engineering University of Michigan–Ann Arbor Ann Arbor United States Department of Physics Illinois Institute of Technology Chicago United States Department of Bioengineering and the Mater

This article was updated to correct Minghao Zhang’s affiliation from "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA" to "Pritzker School of Molecular engineering, The University of Chicago, Chicago, USA;Argonne National Laboratory, Lemont, USA." Argonne National Laboratory was left off the original publication. © The Author(s) 2025.

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Advancing electrochemical impedance analysis through innovations in the distribution of relaxation times method

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Joule 2024年第7期8卷 1958-1981页

作者： Maradesa, Adeleke Py, Baptiste Huang, Jake Lu, Yang Iurilli, Pietro Mrozinski, Aleksander Law, Ho Mei Wang, Yuhao Wang, Zilong Li, Jingwei Xu, Shengjun Meyer, Quentin Liu, Jiapeng Brivio, Claudio Gavrilyuk, Alexander Kobayashi, Kiyoshi Bertei, Antonio Williams, Nicholas J. Zhao, Chuan Danzer, Michael Zic, Mark Wu, Phillip Yrjänä, Ville Pereverzyev, Sergei Chen, Yuhui Weber, André Kalinin, Sergei V. Schmidt, Jan Philipp Tsur, Yoed Boukamp, Bernard A. Zhang, Qiang Gaberšček, Miran O'Hayre, Ryan Ciucci, Francesco Department of Mechanical and Aerospace Engineering The Hong Kong University of Science and Technology Hong Kong Metallurgical and Materials Engineering Colorado School of Mines Golden80401 United States Beijing Key Laboratory of Green Chemical Reaction Engineering and Technology Department of Chemical Engineering Tsinghua University Beijing China Green Energy Storage Trento38123 Italy Department of Manufacturing and Production Engineering Faculty of Mechanical Engineering and Ship Technology Institute of Machine and Materials Technology Gdańsk University of Technology Gdańsk Poland Electrode Design for Electrochemical Energy Systems University of Bayreuth Bayreuth Germany School of Chemistry University of New South Wales Sydney Australia School of Advanced Energy Sun Yat-Sen University Shenzhen China Sustainable Energy Center CSEM Neuchâtel2002 Switzerland Interdisciplinary Faculty of Science and Engineering Shimane University Matsue Japan Centre for Electronic and Optical Materials National Institute for Materials Science Tsukuba Japan Department of Civil and Industrial Engineering University of Pisa Pisa Italy Department of Materials Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Department of Chemical Engineering Massachusetts Institute of Technology Cambridge02139 United States Electrical Energy Systems University of Bayreuth Bayreuth Germany Ruder Boskovic Institute Zagreb Croatia Department of Materials and Minerals Resources Engineering National Taipei University Taipei Taiwan Finland Johann Radon Institute for Computational and Applied Mathematics Linz Austria School of Science and Engineering Nanjing Tech. University Nanjing China Karlsruhe Germany Department of Materials Science and Engineering University of Tennessee KnoxvilleTN37996 United States Physical Science Division Pacific Northwest National Laboratory RichlandWA99354 United States Systems Engineering for Electrical Energy Storage University of B

Electrochemical impedance spectroscopy (EIS) is widely used in electrochemistry, energy sciences, biology, and beyond. Analyzing EIS data is crucial, but it often poses challenges because of the numerous possible equivalent circuit models, the need for accurate analytical models, the difficulties of nonlinear regression, and the necessity of managing large datasets within a unified framework. To overcome these challenges, non-parametric models, such as the distribution of relaxation times (DRT, also known as the distribution function of relaxation times, DFRT), have emerged as promising tools for EIS analysis. For example, the DRT can be used to generate equivalent circuit models, initialize regression parameters, provide a time-domain representation of EIS spectra, and identify electrochemical processes. However, mastering the DRT method poses challenges as it requires mathematical and programming proficiency, which may extend beyond experimentalists’ usual expertise. Post-inversion analysis of DRT data can be difficult, especially in accurately identifying electrochemical processes, leading to results that may not always meet expectations. This article examines non-parametric EIS analysis methods, outlining their strengths and limitations from theoretical, computational, and end-user perspectives, and provides guidelines for their future development. Moreover, insights from survey data emphasize the need to develop a large impedance database, akin to an impedance genome. In turn, software development should target one-click, fully automated DRT analysis for multidimensional EIS spectra interpretation, software validation, and reliability. Particularly, creating a collaborative ecosystem hinged on free software could promote innovation and catalyze the adoption of the DRT method throughout all fields that use impedance data. © 2024 Elsevier Inc.

关键词： Electrochemical impedance spectroscopy

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Evaluation of T-72 tank driver seat based on anthropometric dimensions of Ethiopian military tank drivers

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AIP Conference Proceedings 2022年第1期2648卷

作者： Aliyi Umer Dereje Engida G. M. Sayeed Ahmed U. Pranavi 1College of Engineering Defence University Bishoftu Ethiopia 2Addis Ababa Science and Technology University Addis Ababa Ethiopia 3Program of Mechanical Design and Manufacturing Engineering School of Mechanical Chemical and Materials Engineering (SoMCME) ASTU ADAMA Ethiopia.P.O.Box:1888 4Center of Excellence for Advanced Manufacturing Engineering Program of Mechanical Design Ethiopia.P.O.Box:1888 5Department of Mechanical Engineering Vardhaman College of Engineering Hyderabad-501218 India

In the world it is difficult to design in terms of average dimension. For this reason anthropometric plays a great rule in the world in design of products. In Ethiopia T-72 tanks are the most important weapons that give a strong support to the ground force in the battle field. So the question of the correct ergonomically design and analysis of the driver seat especially in terms of comfort is mandatory so due to this fact, study anthropometric of driver's seat is very important. It is necessary to define the drivers’ space and seat dimensions by considering the size of the average people according to their body dimensions. The basic questions for this research are (i) To what extent the soviet fabricated tank driver's seat is comfortable to Ethiopia army, (ii) Is there significant different between the body size dimension of soviet and Ethiopian army, and (iii) The Ethiopian military derivers have awareness of the comfort. The main goal of this paper is to conduct descriptive survey on an ergonomics assessment of driver's seat of T72 tank. Following sitting height, popliteal height, sitting to shoulder height, shoulder breadth, buttocks to popliteal length, and hip breadth seated anthropometric dimensions were measured of 45 subjects / respondents (35 males, 10 females) randomly selected from the field study. Mean, standard deviation, and T-test were calculated based on anthropometric data of subjects. Results revealed suggest a mismatch among the dimensions of current T-72 Russian fabricated tank motive force seat and the anthropometric dimensions of military staffs. This recommends that anthropometric information of the Ethiopian army tank drivers changed into not working in the design and manufacturing of the seats. This research is may be cautioned that tank driving force seat of T-72 tank and tank drivers anthropometric measurements are at variance. Therefore, the paper presents anthropometric statistics that may be utilized by the Russian manufacturers of tank

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Experimental investigation of productivity of machining process using double tool turning

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AIP Conference Proceedings 2022年第1期2648卷

作者： Kuba Defaru Nedi Moera Gutu Jiru G. M. Sayeed Ahmed Dasari Govardhan 1Department of Mechanical Design and Manufacturing Engineering Adama Science and Technology University ADAMA Ethiopia. P.O.Box:1888 2Program of Mechanical Design and Manufacturing Engineering School of Mechanical Chemical and Materials Engineering (SoMCME) ASTU ADAMA Ethiopia. P.O.Box:1888 3Department of Mechanical Engineering Joginpally B.R Engineering College Hyderabad TS-500075 India

Machining is a globally used manufacturing technology where some part of metal is reduced from bulk workpiece in the chip form. In this thesis work, explores the science of new techniques of machining by two single point cutting tools simultaneous engaged with workpiece material to increase efficiency of the turning operation. In order to implement double tool turning process a normal lathe machine setup was altered. In turning operation, the process factors which affect machined surface quality of component are cutting speed, feed, and depth of cut. Investigation was trailed out to the improvements of productivity by this new machining technique under the influence of metal cutting factors on cutting temperature, tool wear. The morphology of chips and machining time were also studied. A comprehensive test was examined by utilising single and double tool turning process on mild steel work material. The analytically predicted metal cutting temperatures, cutting tool wear, machining time were investigated in detail. For the selected metal cutting conditions, it was recorded that the machining time reduced by 25% surprisingly which the production with this time ranges increased. Tool wear area measured by optical Profilometer shows that total wear zone 813.11mm2 for single tool turning while it was reduced to 45.57 mm2 for double tool turning.

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A Comparative Study of Dimensional Tolerancing Capabilities and Microstructure Formation between Binder Jet Additively Manufactured Sand Molds and Olivine Green Sand Molds for Metalcasting of A356.0

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Procedia manufacturing 2020年 48卷 338-348页

作者： C.R. Hasbrouck Joseph W. Fisher Manuel Rudy Villalpando Paul C. Lynch Department of Industrial and Manufacturing Engineering Pennsylvania State University University Park PA 16802 USA Department of Mechanical Engineering Pennsylvania State University University Park PA 16802 USA Additive Manufacturing and Design Graduate Program Pennsylvania State University University Park PA 16802 USA Department of Industrial Engineering Penn State Erie The Behrend College Erie PA 16563 USA

The development of additive manufacturing in the 1980s led to a revolution in various metalcasting processes as early as the 1990s, including the use of polymer 3D printing for the manufacture of sand casting patterns. While additive manufacturing has entered many metalcasting processes, including both expendable and permanent mold processes, this paper specifically examines the sand casting process. ExOne’s development of binder jetting technology over the last 20 years has allowed designers in the sand casting field to directly 3D print sand molds for metalcasting. Use of binder jet additively manufactured sand molds allows for quicker turnaround time for testing prototypes, new gating designs, and for producing one-off parts. Additive manufacturing of sand molds for metalcasting may implement any of the same foundry sands as green sand processes, but uses a furan binder instead of the traditional mixture of bentonite clay and water. The use of a chemical binder had led to questions about the resulting dimensional capabilities and mechanical properties of the castings produced by the mold. This study investigated the dimensional tolerancing capabilities, surface finish, mechanical properties, microstructure, and defects present in identical castings made from both a traditional olivine green sand molding process and a binder jet additively manufactured silica sand molding process. It was concluded that binder jet additively manufactured sand molds are capable of either equal or better dimensional accuracy and tolerance capabilities than traditional olivine green sand molds. The olivine green sand parts had an average of approximately 1 μm better surface finish than the binder jet sand molds; however, it is likely that both the addition of sea coal to the green sand and the difference in final part color significantly affected this result. The mean hardness of the binder jet parts was 58.9 HBW with a standard deviation of 5.6 HBW, compared to the mean of the green

关键词： Hybrid manufacturing Additive manufacturing Metalcasting Binder Jetting Metallurgy Tolerancing Microstructure Cooling Rate

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Investigating the gap between research and practice in additive manufacturing 30

Investigating the gap between research and practice in addit...

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30th Annual International Solid Freeform Fabrication Symposium - An Additive manufacturing Conference, SFF 2019

作者： Bracken, Jennifer Bentley, Zachary Meyer, James Miller, Erik Jablokow, Kathryn W. Simpson, Timothy W. Meisel, Nicholas A. Department of Mechanical Engineering Penn State University ParkPA16802 United States Additive Manufacturing and Design Program Penn State University ParkPA16802 United States School of Engineering Design Technology and Professional Programs Penn State University ParkPA16802 United States Department of Mechanical Engineering and Industrial Engineering Penn State University ParkPA16802 United States

Additive manufacturing (AM) provides opportunities to design objects differently than traditional manufacturing methods allow, but only if designers understand the possibilities AM presents. In this study, we examined whether an AM workshop combined with an idea generation session could inspire engineering professionals to use AM solutions to solve current technical problems they face. All subjects were employees at an organization that will be referred to as Company X, a multinational commercial organization based in North America. During the study, we collected ideas for 24 projects generated before and after a training workshop focused on design for AM. In the workshop, we provided three hours of instruction about design for two metal-based AM processes. The participants’ ideas were assessed using four specific metrics: (1) cost, (2) time, (3) completeness of solution, and (4) quality, which was a function of feasibility, usefulness, and novelty. Using these data, we explored whether the workshop was effective in inspiring the participants to use AM methods and techniques from AM research in their concept generation and whether participants’ AM solutions showed improvement in cost, implementation time, and quality over non-AM designs generated before the workshop. © Solid Freeform Fabrication 2019: Proceedings of the 30th Annual International Solid Freeform Fabrication Symposium - An Additive manufacturing Conference, SFF 2019. All rights reserved.

关键词： 3D printers

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Feasibility study of selecting soft components of body armor 8th

Feasibility study of selecting soft components of body armor

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8th International Conference on Key engineering Materials, ICKEM 2018

作者： Yaiphuak, Thawatchai Wanchat, Sujin Chantarapanich, Nattapon Mechanical and Design Engineering Graduate Program Faculty of Engineering at Sriracha Kasetsart University Thailand Digital Industrial Design and Manufacturing Research Group Department of Mechanical Engineering Faculty of Engineering at Sriracha Kasetsart University Thailand

ISBN: (纸本)9783035713046

A body armor is vital for users in combat filed. Normally, the body armor have two components: soft and hard ones. This paper proposes feasibility assessment technique to evaluate contemporary materials: Kevlar, natural spider silk, and human hair, for making soft component of the body armor. There are four criteria: technical, economic, legal, and operational feasibilities to generate the feasibility assessment matrix. The optimal material in question is human hair which has highest rank at 82%. © 2018 Trans Tech Publications, Switzerland.

关键词： Silk

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aflow++: A C++ framework for autonomous materials design

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Computational Materials Science 2023年 217卷

作者： Oses, Corey Esters, Marco Hicks, David Divilov, Simon Eckert, Hagen Friedrich, Rico Mehl, Michael J. Smolyanyuk, Andriy Campilongo, Xiomara van de Walle, Axel Schroers, Jan Kusne, A. Gilad Takeuchi, Ichiro Zurek, Eva Nardelli, Marco Buongiorno Fornari, Marco Lederer, Yoav Levy, Ohad Toher, Cormac Curtarolo, Stefano Department of Mechanical Engineering and Materials Science Duke University DurhamNC27708 United States Center for Autonomous Materials Design Duke University DurhamNC27708 United States LIFT American Lightweight Materials Manufacturing Innovation Institute DetroitMI48216 United States Institute of Ion Beam Physics and Materials Research Helmholtz-Zentrum Dresden-Rossendorf Dresden01328 Germany Theoretical Chemistry Technische Universität Dresden Dresden01062 Germany Institute of Solid State Physics Technische Universität Wien WienA-1040 Austria School of Engineering Brown University ProvidenceRI02912 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06511 United States Materials Measurement Science Division National Institute of Standards and Technology GaithersburgMD20899 United States Department of Materials Science and Engineering University of Maryland College Park MD20742 United States Department of Chemistry State University of New York at Buffalo BuffaloNY14260 United States Department of Physics and Department of Chemistry University of North Texas DentonTX76203 United States Santa Fe Institute Santa FeNM87501 United States Department of Physics and Science of Advanced Materials Program Central Michigan University Mount PleasantMI48859 United States Department of Physics NRCN Beer-Sheva84190 Israel Department of Materials Science and Engineering and Department of Chemistry and Biochemistry University of Texas at Dallas RichardsonTX75080 United States

The realization of novel technological opportunities given by computational and autonomous materials design requires efficient and effective frameworks. For more than two decades, aflow++ (Automatic-Flow Framework for Materials Discovery) has provided an interconnected collection of algorithms and workflows to address this challenge. This article contains an overview of the software and some of its most heavily-used functionalities, including algorithmic details, standards, and examples. Key thrusts are highlighted: the calculation of structural, electronic, thermodynamic, and thermomechanical properties in addition to the modeling of complex materials, such as high-entropy ceramics and bulk metallic glasses. The aflow++ software prioritizes interoperability, minimizing the number of independent parameters and tolerances. It ensures consistency of results across property sets — facilitating machine learning studies. The software also features various validation schemes, offering real-time quality assurance for data generated in a high-throughput fashion. Altogether, these considerations contribute to the development of large and reliable materials databases that can ultimately deliver future materials systems. © 2022

关键词： Machine learning

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