Neonatal intestinal obstruction is the most common surgical emergency in a newborn. Although, large numbers of newborns are operated in our country, limited published literature is available on advances in diagnosis, and management of this problem with outcome analysis in newborns. We report a premature (32 weeks) newborn who developed acute onset symptoms of small bowel obstruction in 3rdweek of life, and discuss the approach to diagnosis and management with the minimal access surgery and successful outcome.
3D printing is a growing technology being incorporated into almost every industry. Although it has obvious advantages, such as precision and less fabrication time, it has many shortcomings. Although several attempts were made to monitor the errors, many have not been able to thoroughly address them, like stringing, over-extrusion, layer shifting, and overheating. This paper proposes a study using machine learning to identify the optimal process parameters such as infill structure and density, material (ABS, PLA, Nylon, PVA, and PETG), wall and layer thickness, count, and temperature. The result thus obtained was used to train a machine learning algorithm. Four different network architectures (CNN, Resnet152, MobileNet, and Inception V3) were used to build the algorithm. The algorithm was able to predict the parameters for a given requirement. It was also able to detect any errors. The algorithm was trained to pause the print immediately in case of a mistake. Upon comparison, it was found that the algorithm built with Inception V3 achieved the best accuracy of 97%. The applications include saving the material from being wasted due to print time errors in the manufacturing industry.
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