Institution
Southwest Wisconsin Technical College
Education•Fennimore, Wisconsin, United States•
About: Southwest Wisconsin Technical College is a education organization based out in Fennimore, Wisconsin, United States. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 240 authors who have published 219 publications receiving 2667 citations. The organization is also known as: Southwest Tech.
Topics: Microstructure, Alloy, Corrosion, Ultimate tensile strength, Coating
Papers published on a yearly basis
Papers
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TL;DR: In this paper, the deformation behavior of pure copper was studied in hot compression tests in the temperature range of 773-1173 K and strain rate interval of 0.001-1.0 s-1, the corresponding flow stress curves were plotted.
Abstract: The deformation behavior of pure copper was studied in hot compression tests in the temperature range of 773–1173 K and strain rate interval of 0.001–1.0 s–1, the corresponding flow stress curves were plotted. The new method to calculate critical and saturation stresses was devised, quantitative analysis of strain hardening and dynamic softening was presented, a three-stage constitutive model was constructed to predict the flow stress of pure copper. As predicted and measured flow stress comparison indicate, the physical constitutive model can accurately characterize hot deformation of pure copper. With dynamic recovery and/or recrystallization. Numerical simulation of an upsetting process is carried out by implementing the constitutive model into commercial software. This model can be put to practical use and be quite promising for improving efficiency of a hot forging process for pure copper components.
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01 Jan 2021TL;DR: The vibration prediction model of the key vibration source points of a vehicle platform is studied and the results show that in the frequency range of 1∼500Hz, the maximum error of root mean square of cabin I, that of cabin II and cabin III of the platform is 2.97dB.
Abstract: The vibration prediction model of the key vibration source points of a vehicle platform is studied Starting from the analysis of the factors affecting the vibration response and the correlation analysis, the key factors affecting the vibration response of the same vehicle structure are sorted out The vibration prediction model of the key vibration source points is established by using neural network, and using the measured data to verify the accuracy of the vibration prediction model of the cabin section I, cabin II and cabin III of the platform The results show that in the frequency range of 1∼500Hz, the maximum error of root mean square of cabin I is 275dB, that of cabin II is 23dB, and that of cabin II is 297dB
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01 Oct 2020TL;DR: In this paper, a finite element model of a certain type of airborne equipment installation frame is established to verify the influence of vibration endurance acceleration test spectrum solved by different fatigue damage spectrum calculation methods on its life.
Abstract: Aiming to simulate the actual level flight vibration environment of the aviation equipment during the whole life-cycle, the calculation methods of fatigue damage spectrum based on stationary broad-band hypothesis, stationary narrow-band hypothesis and rain flow counting method are studied separately, and the synthetic acceleration spectrum in frequency domain is carried out and the process of determining vibration endurance test conditions is proposed. The field measured data of three types of flight vibration environment of typical aviation equipment is selected to calculate different types of synthetic acceleration spectrum respectively, and the basic characteristics are compared and analyzed. Thirdly, the finite element model of a certain type of airborne equipment installation frame is established to verify the influence of vibration endurance acceleration test spectrum solved by different fatigue damage spectrum calculation methods on its life. The result shows that the RMS value of the acceleration spectrum calculated by the rain flow counting method is higher than others. Based on the RMS value of rain flow counting method, the relative error between Rayleigh distribution and its RMS is 0.011 while Dirlik and its RMS is 0.0347, and Wirsching’s is 0.0373. Finally, the damage results show that the fatigue damage calculated by Dirlik distribution acceleration method is closest to that by rain flow counting acceleration method, and the vibration endurance test condition based on Dirlik distribution is closest to the flight vibration environment experienced in the whole life of aviation equipment.
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TL;DR: In this article, a cyclic extrusion and closed compression (CECC) process was used for full-annealed coarse grained oxygen-free high conductivity (OFHC) copper billets with ultrafine-grained microstructure.
Abstract: The bulk Cu billets with ultrafine-grained microstructure were successfully processed from full-annealed coarse grained oxygen-free high conductivity (OFHC) Cu by the cyclic extrusion and closed compression (CECC), subsequently annealed at different temperatures. The evolution of the microstructure and mechanical properties was systematically studied. The results show that the effective strain per CECC process is ε=2.77, with further annealing treatment, a high-efficiency grain refinement is realized. After two cycles of CECC process and annealing at 350 °C for 1 h, the grain size refined to ~3 μm, the tensile strength increased to 280 MPa with a high ductility of 54%. Furthermore, a homogeneous structure and mechanical properties in the bulk copper billets for post-forging could be obtained.
Authors
Showing all 240 results
Name | H-index | Papers | Citations |
---|---|---|---|
David Butler | 52 | 324 | 9820 |
Scott Bolton | 47 | 311 | 8523 |
Zude Zhao | 21 | 37 | 1310 |
Qiang Chen | 19 | 74 | 1196 |
Dayu Shu | 16 | 39 | 969 |
Jian Shen | 15 | 69 | 755 |
Ronald L. Iman | 13 | 14 | 1292 |
Chuankai Hu | 12 | 14 | 651 |
Lunjin Lu | 11 | 77 | 552 |
Xiangsheng Xia | 11 | 19 | 525 |
Qiang Chen | 11 | 17 | 364 |
Jianxin He | 11 | 14 | 536 |
Xingde Jia | 9 | 31 | 258 |
Shuhai Huang | 9 | 27 | 412 |
Charles C. Watson | 7 | 11 | 221 |