The effects of different casting techniques on the hardness, energy absorbance and impact strength of sand-cast Pb-Sb-Cu alloys

The effect of casting technique on the impact strength, energy absorbance and hardness of sand cast Pb-Sb-Cu alloys was studied following casting of the alloys using three techniques. Cooling of the alloys after casting was carried out in water, air and furnace in order to vary the micro-structure of the alloys produced. Copper addition to the base alloy was by dispersion of the Cu powder within the Pb-Sb matrix using the three casting techniques. The results showed that Technique A, which involved simultaneous addition of Cu powder and pouring of the molten Pb-Sb into the mould conferred higher impact strength and better energy absorbance on the Pb-Sb-Cu alloys produced, compared to alloys from Techniques B (involving addition of Cu powder intermittently as pouring of Pb-Sb into the mould was going on) and C (involving pouring a stirred mixture of heated Pb-Sb alloy and powdered Cu into the mould). Hardness of the Pb-Sb-Cu alloys was independent of the casting techniques used, but highly dependent on the cooling rate imposed by the cooling medium. Irrespective of the casting technique used, cooling the Pb-Sb-Cu alloys in water conferred higher hardness on the alloys, while higher impact strength and energy absorbance were conferred on the alloys following furnace cooling. Increased Cu addition (up to a maximum of 8.26%) to the Pb-Sb alloy increased correspondingly the impact strength, energy absorbance and hardness of Pb-Sb-Cu alloys so produced.Keywords: Casting technique, sand casting, Pb-Sb-Cu alloy, copper powderInternational Journal of Natural and Applied Sciences, 5(3): 195-202, 200

Model for empirical evaluation and predictive analysis of the effect of temperature on sinter production rate

A model has been derived for empirical evaluation and predictive analysis of the effect of temperature on the sinter production rate. The established model; ã=32.095-0.0067á showed that the sinter production rate was dependent on the sintering temperature within the range (864-11000C). The validity of the model was rooted in the expression 32.095-y =0.0067á, where both sides of the expression were approximately almost equal. The model-predicted and experimental data proximately agreed that beyond a temperature of 9630C, the sinter production rate decreased with increase in the operation temperature. Sinter production rates per unit rise in the operation temperature as obtained from experiment and derived model were evaluated as -0.0064 and -0.0067m2/h respectively. The maximum deviation of mode-predicted data from the corresponding experimental values was less than 15%, which is quite within the acceptable deviation limit of experimental results.Keywords: Model, Temperature, Sinter mix, Sinter production rateInternational Journal of Natural and Applied Sciences, 7(1): 1 - 7, 2011 ISSN: 0794 – 471

Model for the evaluation of overall volume shrinkage in molded clay products from initial air-drying stage to completion of firing

No Abstract.International Journal of Natural and Applied Sciences Vol. 4 (3) 2008: pp. 234-23

CholecTriplet2021: A benchmark challenge for surgical action triplet recognition

Context-aware decision support in the operating room can foster surgical safety and efficiency by leveraging real-time feedback from surgical workflow analysis. Most existing works recognize surgical activities at a coarse-grained level, such as phases, steps or events, leaving out fine-grained interaction details about the surgical activity; yet those are needed for more helpful AI assistance in the operating room. Recognizing surgical actions as triplets of ‹instrument, verb, target› combination delivers more comprehensive details about the activities taking place in surgical videos. This paper presents CholecTriplet2021: an endoscopic vision challenge organized at MICCAI 2021 for the recognition of surgical action triplets in laparoscopic videos. The challenge granted private access to the large-scale CholecT50 dataset, which is annotated with action triplet information. In this paper, we present the challenge setup and the assessment of the state-of-the-art deep learning methods proposed by the participants during the challenge. A total of 4 baseline methods from the challenge organizers and 19 new deep learning algorithms from the competing teams are presented to recognize surgical action triplets directly from surgical videos, achieving mean average precision (mAP) ranging from 4.2% to 38.1%. This study also analyzes the significance of the results obtained by the presented approaches, performs a thorough methodological comparison between them, in-depth result analysis, and proposes a novel ensemble method for enhanced recognition. Our analysis shows that surgical workflow analysis is not yet solved, and also highlights interesting directions for future research on fine-grained surgical activity recognition which is of utmost importance for the development of AI in surgery