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Deep drawing and redrawing of solution-treated Cu-Cr-Zr-Ti alloy sheets and post-forming characterization of the redrawn cup wall

Behera Dibya Ranjan, Katiyar Bhupesh Singh, Das Sayan, Panda Sushanta Kumar, Kar Sujoy Kumar, Murty S. V. S. Narayana

Archives of Civil and Mechanical Engineering

2023

Vol. 23, no. 3

art. no. e217, 2023

Recently, space research organizations are interested in investigating multistage deep drawing of Cu-Cr-Zr-Ti alloy sheets for the fabrication of large-depth thrust chamber liners used in cryogenic engine of satellite launch vehicles. Hence, an attempt was made for the first time to design and develop a laboratory scale two-stage deep drawing setup to successfully draw cylindrical cups of solution-treated Cu-0.5Cr-0.05Zr-0.05Ti (wt. %) sheets of 1.7 mm thickness. The finite element (FE) model with Marciniak–Kuczynski forming limit diagram (MK-FLD) was implemented to design the above two-stage deep drawing setup, and two different anisotropic models, namely Hill48 and Barlat89, along with solid and shell element formulations were used to capture the deformation behavior. After setup design, the two-stage deep drawing experiments were conducted, and successful redrawn cups with overall drawing ratio of 2.94 with maximum cup depth of 58.5 mm was achieved. The strain evolution during deformation was analyzed in polar effective plastic strain (PEPS) locus, and it was also observed that the surface roughness of cup wall and corner was significantly increased to 2.73 μm and 3.16 μm, respectively, due to accumulation of plastic strain and evolution of texture. Further, the orientation gradient inside the grains at both cup wall and corner regions was observed, and evolution of Copper {112} < 111> and Y{111} <112> texture components were identified in the cup wall. However, the marginal increase in roughness of cup corner as compared to that of the cup wall might be due to the development of Brass {110} < 112 > texture. Finally, the aging behavior of redrawn cup wall was analyzed, and it was found that the peak aging occurred at 500 °C for 2 h. with a hardness of 98 ± 4 VHN due to the formation of fine Cr-rich precipitates.

A soft-computing based approach towards automatic detection of pulmonary nodule

Mukherjee Jhilam, Kar Madhuchanda, Chakrabarti Amlan, Das Sayan

Biocybernetics and Biomedical Engineering

2020

Vol. 40, no. 3

1036--1051

Early detection of lung cancer is the major challenge for physicians to treat and control this deadly disease whose primary step is to detect pulmonary nodule from thoracic computed tomography (CT) images. In view of increasing the accuracy of the pulmonary nodule detection methodology, this paper proposes a novel technique that can aid early diagnosis of the patients. The study has considered high resolution computed tomography (HRCT) images from two public datasets LIDC and Lung-TIME and an independent dataset, created in collaboration between Peerless Hospital Kolkata and University of Calcutta. The key feature of the test dataset is that the class features are imbalanced in nature. The structures associated with lung parenchyma are segmented using parameterized multi-level thresholding technique, grayscale morphology, and rolling ball algorithm. Then random under sampling is implemented to overcome the imbalance class problem, followed by a feature selection methodology using binary particle swarm optimization (BPSO). The nodule and non-nodule classification are performed by implementing ensemble stacking. Indeed, it has been observed that there exists insufficient published literature that has been considered similar looking pulmonary abnormalities as non-nodule objects as well as imbalance class problem and feature selection algorithm to design an automated, accurate and robust model for automated detection of the pulmonary nodule. In reference to the LIDC dataset, the false positive, false negative detection rates and sensitivity are 1.01/scan, 0.56/scan and 99.01% respectively, which is an improvement in terms of accuracy as compared to the existing state-of-the-art research works.