Development of the sheared edge in the trimming of steel and light metal sheet, part 1 - experimental observations

Trimming experiments were conducted on sheet metals including two drawing steels, an aluminum alloy and a magnesium alloy, using a specially designed die in a mechanical press. The punch-die clearance was varied and data obtained on the rollover and burr height as a function of the clearance. Samples were also partially trimmed to examine crack initiation, the generation of the fracture surface profile and mechanism of burr formation. The results showed that while the burr height and rollover depth generally increased with increasing clearance for all examined materials, there were differences in the fracture surface profile shape, the burr shape, and the mechanism of burr formation, between the two steels and the two light alloys. The major cause of these differences appeared to be the rate of crack propagation through the sheet material.

Development of the sheared edge in the trimming of steel and light metal sheet, part 2 - mechanisms and modelling

The shearing behavior of a drawing-steel and aluminum alloy were investigated using hardness contours of partially deformed samples and a finite element model of the trimming process. Results showed that the stress and strain distributions within the work-piece were more strongly dependent on the punch penetration than the material properties of the work-piece. Differences in the final fracture surface profile and burr formation of the drawing-steel and aluminum alloy were a consequence of the shape of the stress and strain distribution when the crack in the sample became unstable, not when it was initiated. Results and existing literature suggest that a correlation may exist between the strain-rate sensitivity of the work-piece material and the burr mechanism and fracture surface profile of the trimmed part.

Sheared edge quality in the trimming of sheet metals

Examines the sheared edge quality of different automotive sheet metals resulting from the trimming process using experimental tooling and finite element modelling. The significant differences in burr, sliver and edge profile formation have been explained in terms of the strain distribution and tensile properties of the materials.

Genome sequence of Ensifer medicae strain WSM1115, an acid-tolerant Medicago-nodulating microsymbiont from Samothraki, Greece

Ensifer medicae (syn. Sinorhizobium medicae) strain WSM1115 forms effective nitrogen fixing symbioses with a range of annual Medicago species and is used in commercial inoculants in Australia. WSM1115 is an aerobic, motile, Gram-negative, non-spore-forming rod. It was isolated from a nodule recovered from the root of burr medic (Medicago polymorpha) collected on the Greek Island of Samothraki. WSM1115 has a broad host range for nodulation and N2 fixation capacity within the genus Medicago, although this does not extend to all medic species. WSM1115 is considered saprophytically competent in moderately acid soils (pHCaCl2 5.0) however, has failed to persist at field sites where soil salinity exceeded 10 ECe (dS/m). Here we describe the features of E. medicae strain WSM1115, together with genome sequence information and its annotation. The 6,861,065 bp high-quality-draft genome is arranged into 7 scaffolds of 28 contigs, contains 6,789 protein-coding genes and 83 RNA-only encoding genes, and is one of 100 rhizobial genomes sequenced as part of the DOE Joint Genome Institute 2010 Genomic Encyclopedia for Bacteria and Archaea-Root Nodule Bacteria (GEBA-RNB) project.

Visionnaire 2013

Visionnaire: a screening of graduate and masters short films: Wolf, Shaw, Mawrgan. Treasure, Horton, Kyle. Descent, Coughlin, Michael. Cut, Hendriks, Frouwke. A Silent Waltz, Lefoe, Kelsey; Elliott, Isaac; Nanayakkara, Ashini; Tillett, Michael; Teychenne, Danielle; Axiaq, Christopher. Siren, Brander, Dale; Heath, Katherine, Stielow, Rebecca; Bellamy, Edward; Boldeman, Emma; Dong, Jing. The Man Who Cared Too Much, Milner, Robert; Di Quinzio, Carl; Lancaster, Tabitha; Hennequin, Blake; Pinnock, Alicia; Hay, Sarah. Perspective, Hampson, Matt; Gubieski, Alex; Lam, Yen; Jokomo, Nyasha; Carew, Keanan; Burr, Harrison. A Cut Above, Giebler, Heather; Blythe, Carolyn, Carell, Eden; Lai, Cyrus; Rattenbury, Todd; Gatsios, Isaac. Pumpkins, Davies, Brooke; Scharf, Harrison; Jayawardena, Kevin; Lee, Sebastian; Haidari, Ali; Morris, Travis. The Runner, Mitchinson, Liz; Kelly, Rhin; Fomin, Nina; Luxa, Haili; Williams-Pate, Robert; Karoutsos, Antonios. Doors, Sadeghi, Sam; Ward, Ben; Thomson, Alana; Brown, Sam; Earl, Richard; Watterson, James. Daddy’s Little Girl, Dahlenburg, Zoe; Atalla, Joanna; McConnell, Adam; Perri, Tim; Turnbull, Sophie; Leszczynski, Dale. Hopscotch, Egenes, Silge Vikor; Burns, Matt; Fuller, Kelly; Wurm, Christopher; Parker, Stephen; Carson, Brooke Aung.

Process capability analysis in non normal linear regression profiles

When the distribution of a process characterized by a profile is non normal, process capability analysis using normal assumption often leads to erroneous interpretations of the process performance. Profile monitoring is a relatively new set of techniques in quality control that is used in situations where the state of product or process is represented by a function of two or more quality characteristics. Such profiles can be modeled using linear or nonlinear regression models. In some applications, it is assumed that the quality characteristics follow a normal distribution; however, in certain applications this assumption may fail to hold and may yield misleading results. In this article, we consider process capability analysis of non normal linear profiles. We investigate and compare five methods to estimate non normal process capability index (PCI) in profiles. In three of the methods, an estimation of the cumulative distribution function (cdf) of the process is required to analyze process capability in profiles. In order to estimate cdf of the process, we use a Burr XII distribution as well as empirical distributions. However, the resulted PCI with estimating cdf of the process is sometimes far from its true value. So, here we apply artificial neural network with supervised learning which allows the estimation of PCIs in profiles without the need to estimate cdf of the process. Box-Cox transformation technique is also developed to deal with non normal situations. Finally, a comparison study is performed through the simulation of Gamma, Weibull, Lognormal, Beta and student-t data.