Determination of machinable volume for finish cuts in CAPP

Identification of machinable volume for finish cut is a complex task as it involves the details not only of the final product but also the intermediate part obtained from rough machining of the blank. A feature recognition technique that adopts a rule-based methodology is required for calculating this small, complex shaped finish cut volume. This paper presents the feature recognition module in a CAPP system that calculates the intermediate finish cut volume by adopting a rule based syntactic pattern recognition approach. In this module, the interfacer uses STEP AP203/214, a CAD neutral format, to trace the coordinate point information and to calculate the machinable volume. Two illustrative examples are given to explain the proposed syntactic pattern approach for prismatic parts

Toolpath algorithm for free form irregular contoured walls / surfaces with internal deflecting connections.

This paper presents a toolpath generation method to efficiently machine free form irregular contoured walls / surfaces (FIWS) containing internal deflecting connections (IDC’s). The toolpath generation method is based on a series of identifications and calculations, where initially a ‘Main Computable Zone (MCZ)’ in the Machinable Areas (Ma’s) of FIWS is identified based on the Tool track dimensions (Td). Then the MCZ’s are divided into Split Computable Zones (SCZ’s) and Split Computable Zones for Internal Connections (SCZI’s) which are subsequently sub divided as ‘Categorized Computable Zones’ (CCZ) with simple-medium-high complexity. The identification of CCZ’s is based on the 10 different types of FIWS representations developed for this study. From the CCZ’s categorization of complexity, they are further split into smaller ‘Machinable Zones (MZ’s)’ using a 4-step algorithm. In the algorithm, the first step calculates a common plane (CP) to cut the steep areas in the CCZ’s where the tool cannot have full access for machining. Once the CP is identified, the second step is to extend it by moving them along the CCZ’s and calculate the necessary ‘Machinable Zones (MZ’s)’ in the next stage. This is done by finding the intersection of CP with the FIWS through a point to point / line plane intersection concept. After this step, the MZ’s are re-iterated by including the open and closed surface criteria and is analyzed for the IDC’s to be combined in the fourth stage. This is achieved by adding up the IDC’s with the existing MZ’s computed by the algorithm. At every stage, the algorithm considers tool collision avoidance and tool rubbing in the CCZ’s and MZ’s . This is by an automatic computation based on the height to fixture clearance for safer neck length which avoids collision and rubbings in the final toolpaths. Finally, a combined tool path is generated for all the MZ’s and has been verified / tested for a sample part and impeller containing similar shapes using UG NX / STEP –NC software

STEP based Finish Machining CAPP system

This research paper presents various methodologies developed in a STEP based Computer Aided Process Planning (CAPP) system named "Finish Machining – CAPP" (FM-CAPP). It is developed to generate automatic process plans for finish machining prismatic parts. It is designed in a modular fashion consisting of three main modules, namely (i) Feature Recognition module (FRM) (ii) Machining Planning Module (MPM) and (iii) Setup Planning Module (SPM). The FRM Module analyses the geometrical and topological information of the inputted part in STEP AP 203/AP214 formats, and generates a text file with full dimensional details of features and machinable volumes. It is then passed on to the MPM for the selection of best suited machining process. Here, the selection is based on a 7 stage elimination strategy considering major manufacturing factors. After machining planning, the task of selecting the best suited setup is implemented in the SPM module. When these tasks are completed, the system generates the process-planning sheet containing the details of feature, finish cut machinable volume, machining processes with the cutting tool/ media, process parameters and the setup required for machining

Automated design and STEP-NC machining of impellers

This paper presents the four stage approach followed for automated design and STEP-NC based machining of impellers. In the first stage, the design calculations are performed to construct the 'Meridional representation' of the radial impeller. Then 3D curves are projected from the 'Meridional representation' and 3D model is generated using UG-NX software. In the second stage, the process planning activities including tooling & setup plan are completed. Here, ball end mill cutters with suitable diameter and length are selected and appropriate process parameters as suited to 5 axis milling are considered. In the third stage, the tool path data based on contour area milling is generated and verified in the UG NX software. Finally, in the fourth stage, the model with the complete data is imported to STEP-NC software and the AP-238 format is generated. In this article the design procedure adopted for construction of 'Meridional Section' of a radial turbine is discussed with the general methdology to automate the process planning and tool path generation. A test case of radial impeller is presented with the results obtained by adopting STEP-NC format

Mapping of Compositional Diversity and Chronological Ages of Lunar Farside Multiring Mare Moscoviense Basin: Implications to the Middle Imbrian Mare Basalts

The Mare Moscoviense is an astonishing rare flatland multi-ring basin and one of the recognizable mare regions on the Moon’s farside. The mineralogical, chronological, topographical and morphological studies of the maria surface of the Moon provide a primary understanding of the origin and evolution of the mare provinces. In this study, the Chandrayaan-1 M3 data have been employed to prepare optical maturity index, FeO and TiO2 concentration, and standard band ratio map to detect the mafic indexes like olivine and pyroxene minerals. The crater size frequency distribution method has been applied to LROC WAC data to obtain the absolute model ages of the Moscoviense basin. The four geological unit ages were observed as 3.57 Ga (U-2), 3.65 Ga (U-1), 3.8 Ga (U-3) and 3.92 Ga (U-4), which could have been formed between the Imbrian and Nectarian epochs. The M3 imaging and reflectance spectral parameters were used to reveal the minerals like pyroxene, olivine, ilmenite, plagioclase, orthopyroxene-olivine-spinel lithology, and olivine-pyroxene mixtures present in the gabbroic basalt, anorthositic and massive ilmenite rocks, and validated with the existing database. The results show that the Moscoviense basin is dominated by intermediate TiO2 basalts that derived from olivine-ilmenite-pyroxene cumulate depths ranging from 200 to 500 km between 3.5 Ga and 3.6 Ga

Identification of potential serum biomarkers of glioblastoma: serum osteopontin levels correlate with poor prognosis

Background: The aim of this study is to identify serum biomarkers with classification and prognosis utility for astrocytoma, in particular glioblastoma (GBM). Methods: Our previous glioma microarray database was mined to identify genes that encode secreted or membrane-localized proteins. Subsequent analysis was done using significant analysis of microarrays, followed by reverse transcription-quantitative PCR (RT-qPCR) and immunohistochemical validation in tumor tissues, ELISA and Western blot validation in sera, and correlation with survival of GBM patients. Results: Significant analysis of microarrays identified 31 upregulated and 3 downregulated genes specifically in GBMs. RT-qPCR validation on an independent set of samples confirmed the GBM-specific differential expression of several genes, including three upregulated (CALU, CXCL9, and TIMP1) and two downregulated (GPX3 and TIMP3) novel genes. With respect to osteopontin (OPN), we show the GBM-specific upregulation by RT-qPCR and immunohistochemical staining of tumor tissues. Elevated serum OPN levels in GBM patients were also shown by ELISA and Western blot. GBM patients with high serum OPN levels had poorer survival than those with low serum OPN levels (median survival 9 versus 22 months respectively; P = 0.0001). Further, we also show high serum TIMP1 levels in GBM patients compared with grade II/III patients by ELISA and downregulation of serum GPX3 and TIMP3 proteins in GBMs compared with normal control by Western blot analysis. Conclusions: Several novel potential serum biomarkers of GBM are identified and validated. High serum OPN level is found as a poor prognostic indicator in GBMs. Impact: Identified serum biomarkers may have potential utility in astrocytoma classification and GBM prognosis

Texture Segregation By Visual Cortex: Perceptual Grouping, Attention, and Learning

A neural model is proposed of how laminar interactions in the visual cortex may learn and recognize object texture and form boundaries. The model brings together five interacting processes: region-based texture classification, contour-based boundary grouping, surface filling-in, spatial attention, and object attention. The model shows how form boundaries can determine regions in which surface filling-in occurs; how surface filling-in interacts with spatial attention to generate a form-fitting distribution of spatial attention, or attentional shroud; how the strongest shroud can inhibit weaker shrouds; and how the winning shroud regulates learning of texture categories, and thus the allocation of object attention. The model can discriminate abutted textures with blurred boundaries and is sensitive to texture boundary attributes like discontinuities in orientation and texture flow curvature as well as to relative orientations of texture elements. The model quantitatively fits a large set of human psychophysical data on orientation-based textures. Object boundar output of the model is compared to computer vision algorithms using a set of human segmented photographic images. The model classifies textures and suppresses noise using a multiple scale oriented filterbank and a distributed Adaptive Resonance Theory (dART) classifier. The matched signal between the bottom-up texture inputs and top-down learned texture categories is utilized by oriented competitive and cooperative grouping processes to generate texture boundaries that control surface filling-in and spatial attention. Topdown modulatory attentional feedback from boundary and surface representations to early filtering stages results in enhanced texture boundaries and more efficient learning of texture within attended surface regions. Surface-based attention also provides a self-supervising training signal for learning new textures. Importance of the surface-based attentional feedback in texture learning and classification is tested using a set of textured images from the Brodatz micro-texture album. Benchmark studies vary from 95.1% to 98.6% with attention, and from 90.6% to 93.2% without attention.Air Force Office of Scientific Research (F49620-01-1-0397, F49620-01-1-0423); National Science Foundation (SBE-0354378); Office of Naval Research (N00014-01-1-0624

Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance

In the brain, enlarged perivascular spaces (PVS) relate to cerebral small vessel disease (SVD), poor cognition, inflammation and hypertension. We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high. We assess the performance of three different types of descriptors extracted from the BG region in T2-weighted MRI images: (i) statistics obtained from Wavelet transform’s coefficients, (ii) local binary patterns and (iii) bag of visual words (BoW) based descriptors characterizing local keypoints obtained from a dense grid with the scale-invariant feature transform (SIFT) characteristics. When the latter were used, the SVM classifier achieved the best accuracy (81.16%). The output from the classifier using the BoW descriptors was compared with visual ratings done by an experienced neuroradiologist (Observer 1) and by a trained image analyst (Observer 2). The agreement and cross-correlation between the classifier and Observer 2 (κ = 0.67 (0.58–0.76)) were slightly higher than between the classifier and Observer 1 (κ = 0.62 (0.53–0.72)) and comparable between both the observers (κ = 0.68 (0.61–0.75)). Finally, three logistic regression models using clinical variables as independent variable and each of the PVS ratings as dependent variable were built to assess how clinically meaningful were the predictions of the classifier. The goodness-of-fit of the model for the classifier was good (area under the curve (AUC) values: 0.93 (model 1), 0.90 (model 2) and 0.92 (model 3)) and slightly better (i.e. AUC values: 0.02 units higher) than that of the model for Observer 2. These results suggest that, although it can be improved, an automatic classifier to assess PVS burden from brain MRI can provide clinically meaningful results close to those from a trained observer

Silencing by nuclear matrix attachment distinguishes cell-type specificity: association with increased proliferation capacity

DNA loop organization by nuclear scaffold/matrix attachment is a key regulator of gene expression that may provide a means to modulate phenotype. We have previously shown that attachment of genes to the NaCl-isolated nuclear matrix correlates with their silencing in HeLa cells. In contrast, expressed genes were associated with the lithium 3,5-diiodosalicylate (LIS)-isolated nuclear scaffold. To define their role in determining phenotype matrix attached regions (MARs) on human chromosomes 14–18 were identified as a function of expression in a primary cell line. The locations of MARs in aortic adventitial fibroblast (AoAF) cells were very stable (r = 0.909) and 96% of genes attached at MARs are silent (P < 0.001). Approximately one-third of the genes uniquely expressed in AoAF cells were associated with the HeLa cell nuclear matrix and silenced. Comparatively, 81% were associated with the AoAF cell nuclear scaffold (P < 0.001) and expressed. This suggests that nuclear scaffold/matrix association mediates a portion of cell type-specific gene expression thereby modulating phenotype. Interestingly, nuclear matrix attachment and thus silencing of specific genes that regulate proliferation and maintain the integrity of the HeLa cell genome suggests that transformation may at least in part be achieved through aberrant nuclear matrix attachment