195,619 research outputs found
Modeling Shape Distortion of 3-D Printed Aluminum Oxide Parts During Sintering
No full textCeramic parts have become increasingly valuable in many different engineering applications due
to their high melting point and other desirable mechanical properties. When manufacturing
ceramic parts with complicated geometries, 3-D printing has become a very useful production
method. When creating 3-D printed ceramic parts, producing the desired properties are based on
achieving the proper microstructure. After printing, the density of the part is low and the resulting
microstructure is inadequate for many engineering applications. To increase the density of the part,
and create the desired mechanical properties the part must be heat treated. This process is known
as sintering. Sintering entails heating a part, which has already been formed, so that densification
occurs [1]. During the sintering process, distortion and non-uniform densification occurs. This is
a result of non-uniform pore distribution throughout the material resulting in inhomogeneous
material density. Friction upon shrinkage and gravity also play roles in the non-uniform sintering
[2]. The prediction of non-uniform densification and distortion of the manufactured part is
essential to efficiently manufacture ceramic parts to a predetermined dimension. The objective of
this thesis is to generate techniques for modeling 3-D printed ceramics (Aluminum Oxide) during
the sintering process to accurately predict shrinkage and distortion. A continuum mechanics
approach was taken to create a constitutive model that describes the relationship between the stress
and strain tensor during the sintering process. The constitutive model was utilized in creating a
user subroutine (UMAT) to implement into the FEA program, Abaqus
D-calibration comparison with elastic-net Cox model.
No full texta) D-calibration of survival predictions for the DJIN model. Estimated survival probabilities are expected to be uniformly distributed (dashed black line). We use Pearson’s χ2 test to assess the distribution of survival probabilities finding χ2 = 1.3 and p = 1.0 and an elastic net Cox model. (Higher p-values and smaller χ2 statistics are better). b) D-calibration of survival predictions for the elastic-net Cox model. Estimated survival probabilities are expected to be uniformly distributed (dashed black line). We use Pearson’s χ2 test to assess the distribution of survival probabilities finding χ2 = 2.1 and p = 1.0. Error bars show the standard deviation. (TIF)</p
1ST MEASUREMENT OF GAMMA(D(S)(+)-]MU+NU)/GAMMA(D(S)(+)-]PHI-PI+)
No full textComplete Author List:
ACOSTA D, ATHANAS M, MASEK G, PAAR H, BEAN A, GRONBERG J, KUTSCHKE R, MENARY S, MORRISON RJ, NAKANISHI S, NELSON HN, NELSON TK, RICHMAN JD, RYD A, TAJIMA H, SCHMIDT D, SPERKA D, WITHERELL MS, PROCARIO M, YANG S, BALEST R, CHO K, DAOUDI M, FORD WT, JOHNSON DR, LINGEL K, LOHNER M, RANKIN P, SMITH JG, ALEXANDER JP, BEBEK C, BERKELMAN K, BESSON D, BROWDER TE, CASSEL DG, CHO HA, COFFMAN DM, DRELL PS, EHRLICH R, GALIK RS, GARCIASCIVERES M, GEISER B, GITTELMAN B, GRAY SW, HARTILL DL, HELTSLEY BK, JONES CD, JONES SL, KANDASWAMY J, KATAYAMA N, KIM PC, KREINICK DL, LUDWIG GS, MASUI J, MEVISSEN J, MISTRY NB, NG CR, NORDBERG E, OGG M, PATTERSON JR, PETERSON D, RILEY D, SALMAN S, SAPPER M, WORDEN H, WURTHWEIN F, AVERY P, FREYBERGER A, RODRIGUEZ J, STEPHENS R, YELTON J, CINABRO D, HENDERSON S, KINOSHITA K, LIU T, SAULNIER M, SHEN F, WILSON R, YAMAMOTO H, ONG B, SELEN M, SADOFF AJ, AMMAR R, BALL S, BARINGER P, COPPAGE D, COPTY N, DAVIS R, HANCOCK N, KELLY M, KWAK N, LAM H, KUBOTA Y, LATTERY M, NELSON JK, PATTON S, PERTICONE D, POLING R, SAVINOV V, SCHRENK S, WANG R, ALAM MS, KIM IJ, NEMATI B, ONEILL JJ, SEVERINI H, SUN CR, ZOELLER MM, CRAWFORD G, DAUBENMIER CM, FULTON R, FUJINO D, GAN KK, HONSCHEID K, KAGAN H, KASS R, LEE J, MALCHOW R, MORROW F, SKOVPEN Y, SUNG M, WHITE C, WHITMORE J, WILSON P, BUTLER F, FU X, KALBFLEISCH G, LAMBRECHT M, ROSS WR, SKUBIC P, SNOW J, WANG PL, WOOD M, BORTOLETTO D, BROWN DN, FAST J, MCILWAIN RL, MIAO T, MILLER DH, MODESITT M, SCHAFFNER SF, SHIBATA EI, SHIPSEY IPJ, WANG PN, BATTLE M, ERNST J, KROHA H, ROBERTS S, SPARKS K, THORNDIKE EH, WANG CH, DOMINICK J, SANGHERA S, SHELKOV V, SKWARNICKI T, STROYNOWSKI R, VOLOBOUEV I, ZADOROZHNY P, ARTUSO M, HE D, GOLDBERG M, HORWITZ N, KENNETT R, MONETI GC, MUHEIM F, MUKHIN Y, PLAYFER S, ROZEN Y, STONE S, THULASIDAS M, VASSEUR G, ZHU G, BARTELT J, CSORNA SE, EGYED Z, JAIN V, SHELDON P, AKERIB DS, BARISH B, CHADHA M, CHAN S, COWEN DF, EIGEN G, MILLER JS, OGRADY C, URHEIM J, WEINSTEIN A
Jacob D. Cox portrait
No full textCarte de visite portrait of Jacob Dolson Cox (1828-1900) in military uniform, taken by Warner & Elliott of Columbus, Ohio. Originally from Montreal, Canada, Cox emigrated to the United States, and attended Oberlin College. After several pre-war political positions in Ohio, he joined the Ohio Volunteer Infantry. Cox fought at Antietam, South Mountain, and Atlanta, and eventually became military supervisor for the District of Ohio and the District of Michigan. He went on to become the 28th governor of Ohio from 1866 to 1868, and served as Secretary of the Interior from 1869 through 1870 in the presidential cabinet of fellow Ohioan Ulysses S. Grant
Productivity growth and the returns from public investment in R&D in Australian broadacre agriculture
No full textInvestment in R&D has long been regarded as an important source of productivity growth in Australian agriculture. Perhaps because research lags are long, current investment in R&D is monitored closely. Investment in R&D has been flat while productivity growth has remained strong, relative both to other sectors of the Australian economy and to the agricultural sectors of other countries. Such productivity growth, at a time when the decline in terms of trade facing Australian farmers has slowed, may have enhanced the competitiveness of Australian agriculture. The econometric results presented here suggest no evidence of a decline in the returns from research from the 15 to 40 per cent per annum range estimated by Mullen and Cox. In fact the marginal impact of research increases with research over the range of investment levels experienced from 1953 to 2000, a finding which lends support to the view that there is underinvestment in agricultural research. These results were obtained from econometric models which maintain strong assumptions about how investments in research and extension translate into changes in TFP. Hence some caution in interpreting the results is warranted.productivity, research and development, research evaluation, Productivity Analysis, Research and Development/Tech Change/Emerging Technologies,
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