Simulation and validation of residual deformations in additive manufacturing of metal parts

Selective laser melting (SLM) is gaining increasing relevance in industry. Residual deformations and internal stresses caused by the repeated layerwise melting of the metal powder and transient cooling of the solidified layers still presents a significant challenge to the profitability and quality of the process. Excessive distortions or cracking may lead to expensive rejects. In practice, critical additively manufactured parts are either iteratively pre-compensated or redesigned based on production experience. To satisfy the need for improved understanding of this complex manufacturing process, CAE software providers have recently developed solutions to simulate the SLM process. This study focuses on the evaluation of two solutions by ANSYS, i.e. ANSYS Additive Print and ANSYS Additive Suite. ANSYS Additive Print (AAP), a user-oriented software, and ANSYS Additive Suite (AAS), a software requiring advanced experience with Finite Element Methods (FEM), are investigated and validated with regard to residual deformations. For the evaluation of the two programs, calibration and validation geometries were printed by SLM in Ti–6Al–4V and residual deformations have been measured by 3D scanning. The results have been used for the calibration of isotropic and anisotropic strain scaling factors in AAP, and for sensitivity analyses on the effect of basic model parameters in AAS. The actual validation of the programs is performed on the basis of different sample geometries with varying wall thickness and deformation characteristic. While both simulation approaches, AAP and AAS, are capable of predicting the qualitative characteristics of the residual deformations sufficiently well, accurate quantitative results are difficult to obtain. AAP is more accessible and yields accurate results within the calibrated regime. Extrapolation to other geometries introduces uncertainties, however. AAS, on the other hand, features a sounder physical basis and therefore allows for a more robust extrapolation. Numerical efforts and modelling uncertainties as well as requirements for an extensive set of material parameters reduce its practicality, however. More appropriate calibration geometries, continuing extension of a more reliable material database, improved user guidelines and increased numerical efficiency are key in the future establishment of the process simulation approaches in the industrial practice

Hacia el fomento de las TIC en el sector educativo en Colombia

El actual contexto tecnológico y la apertura de nuevos escenarios digitales de comunicación y acceso al conocimiento ha generado cambios sustanciales en el sector educativo a nivel global. Los maestros y las instituciones educativas no pueden limitarse solo a proporcionar las clásicas estrategias de seguimiento y apoyo al aprendizaje del alumno; por el contrario, deben basarse en el fomento y promoción de mecanismos más sistemáticos y continuados al momento de llevar a cabo dicha labor, con el fin de promover mayor autonomía en los procesos de enseñanza-aprendizaje que lideran. Precisamente, para conocer a fondo cómo se encuentra el sector educativo en Colombia respecto a los retos que marcan las nuevas formas de enseñanza-aprendizaje en la sociedad del conocimiento, se presenta esta obra que contiene los resultados del proyecto Medición del impacto de las actividades orientadas al fomento de las TIC en el sector educativo en la región Caribe Colombiana. Caso Barranquilla y Cartagena, ejecutado por el Observatorio de Educación de la Uninorte, la Universidad Tecnológica de Bolívar y la Corporación Colombia Digital, con el apoyo de asesores internacionales de la Universidad de Murcia y del Instituto Torre del Palau de España, y el financiamiento de COLCIENCIAS y el Ministerio de Educación Nacional de Colombia. Los autores realizan una revisión crítica al actual escenario tecnológico del sector educativo en el país y presentan una propuesta orientada al fortalecimiento del fomento de las TIC. Los resultados aquí compilados serán de gran utilidad para la generación de nuevas medidas orientadas a optimizar el proceso de inclusión digital en Colombia y la replicación de este propuesta en otros escenarios de América Latina

A transversal pilot study of oropharyngeal carriage of Kingella kingae in healthy children younger than 6 months

The aim of this pilot study was to investigate the extent of oropharyngeal Kingella kingae carriage during the first 6 months of life

Accelerating detection of lung pathologies with explainable ultrasound image analysis

Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of 0.90±0.08 and a specificity of 0.96±0.04. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity 0.806, specificity 0.962). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound

Erratum: Born et al. Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis. <i>Appl. Sci.</i> <b>2021</b>, <i>11</i>, 672

The authors wish to make the following corrections to this paper [...