Neural Network Modelling of Track Profile in Cold Spray Additive Manufacturing.

Cold spray additive manufacturing is an emerging technology that offers the ability to deposit oxygen-sensitive materials and to manufacture large components in the solid state. For further development of the technology, the geometric control of cold sprayed components is fundamental but not yet fully matured. This study presents a neural network predictive modelling of a single-track profile in cold spray additive manufacturing to address the problem. In contrast to previous studies focusing only on key geometric feature predictions, the neural network model was employed to demonstrate its capability of predicting complete track profiles at both normal and off-normal spray angles, resulting in a mean absolute error of 8.3%. We also compared the track profile modelling results against the previously proposed Gaussian model and showed that the neural network model provided comparable predictive accuracy, even outperforming in the predictions at cold spray profile edges. The results indicate that a neural network modelling approach is well suited to cold spray profile prediction and may be used to improve geometric control during additive manufacturing with an appropriate process planning algorithm.This research was funded by CSIRO’s Active Integrated Matter Future Science Platform (AIM FSP)under the testbed number: TB10_WB04

Diferencias en la distribución de las variantes de hemoglobina según las regiones geográficas en una población colombiana

Introducción Colombia ha estado Introducción Colombia ha estado sometida a intensas corrientes genéticas y culturales debido a su ubicación geográfica. Las hemoglobinopatías son las enfermedades recesivas más frecuentes en todo el mundo y representan un importante problema de salud pública, según la región y la ascendencia de cada país. Objetivos Evaluar la frecuencia de variantes de hemoglobina según la región geográfica en un grupo poblacional ajustado por sexo y edad en Colombia. Métodos Fue un estudio descriptivo retrospectivo de variantes de hemoglobina realizado por electroforesis en pacientes atendidos y/o remitidos a instituciones de atención especializada en Bogotá, Colombia entre enero de 2009 y diciembre de 2020. Resultados Se analizaron 2.224 resultados, 48,4% hombres y 51,5% mujeres; 63,3% de los pacientes fueron sin alteraciones, 14,3% presentaron talasemia, 17,3%, HbS, 2,3%, HbS/C, 1,8%, HbC, 0,5%, HbE y 0,5% HbF persistente, siendo la HbS más prevalente en hombres (p = 0,005). Al evaluar las regiones geográficas de Colombia, se encontró una mayor prevalencia de HbS en las regiones Pacífica (p = 0,005) y Caribe, mientras que la Talasemia y la HbS fueron más prevalentes en las regiones Andina y Orinoquia, y fue raro encontrar alguna hemoglobinopatía (p = 0,0001) en la región Amazónica. Conclusiones Las principales hemoglobinopatías encontradas en Colombia son la HbS, predominantemente en varones, y la Talasemia. La distribución de las hemoglobinopatías en las diferentes regiones geográficas de Colombia está influenciada por la ancestralidad.Sujeta a intensas corrientes genéticas y culturales debido a su ubicación geográfica. Las hemoglobinopatías son las enfermedades recesivas más frecuentes a nivel mundial y representan un importante problema de salud pública, según la región y la ancestralidad de cada país. Objetivos Evaluar la frecuencia de variantes de hemoglobina según la región geográfica en un grupo poblacional ajustado por sexo y edad en Colombia. Métodos Fue un estudio descriptivo retrospectivo de variantes de hemoglobina realizado por electroforesis en pacientes atendidos y/o remitidos a instituciones de atención especializada en Bogotá, Colombia entre enero de 2009 y diciembre de 2020. Resultados Se analizaron 2.224 resultados, 48,4% hombres y 51,5% mujeres; 63,3% de los pacientes fueron sin alteraciones, 14,3% presentaron talasemia, 17,3%, HbS, 2,3%, HbS/C, 1,8%, HbC, 0,5%, HbE y 0,5% HbF persistente, siendo la HbS más prevalente en hombres (p = 0,005). Al evaluar las regiones geográficas de Colombia, se encontró una mayor prevalencia de HbS en las regiones Pacífica (p = 0,005) y Caribe, mientras que la Talasemia y la HbS fueron más prevalentes en las regiones Andina y Orinoquia, y fue raro encontrar alguna hemoglobinopatía (p = 0,0001) en la región Amazónica. Conclusiones Las principales hemoglobinopatías encontradas en Colombia son la HbS, predominantemente en varones, y la Talasemia. La distribución de las hemoglobinopatías en las diferentes regiones geográficas de Colombia está influenciada por la ascendencia.Introduction Colombia has been Introduction Colombia has been subject to intense genetic and cultural currents due to its geographical location. Hemoglobinopathies are the most common recessive diseases found worldwide and represent an important public health problem, according to the region and ancestry of each country. Objectives To evaluate the frequency of hemoglobin variants according to the geographical region in a population group adjusted to sex and age in Colombia. Methods This was a descriptive retrospective study of hemoglobin variants performed by electrophoresis in patients treated at and/or referred to specialized care institutions in Bogota, Colombia between January 2009 and December 2020. Results A total of 2,224 results were analyzed, 48.4% male and 51.5% female; 63.3% of patients were without alterations, 14.3% presented with thalassemia, 17.3%, HbS, 2.3%, HbS/C, 1.8%, HbC, 0.5%, HbE and 0.5% persistent HbF, with HbS being more prevalent in males (p = 0.005). When assessing the geographical regions of Colombia, a higher prevalence of HbS was found in the Pacific (p = 0.005) and Caribbean regions, while Thalassemia and HbS were more prevalent in the Andean and Orinoquia regions, and it was rare to find any hemoglobinopathies (p = 0.0001) in the Amazonian region. Conclusions The main hemoglobinopathies found in Colombia are HbS, predominantly in males, and Thalassemia. The distribution of hemoglobinopathies in different geographical regions of Colombia is influenced by ancestry.subject to intense genetic and cultural currents due to its geographical location. Hemoglobinopathies are the most common recessive diseases found worldwide and represent an important public health problem, according to the region and ancestry of each country. Objectives To evaluate the frequency of hemoglobin variants according to the geographical region in a population group adjusted to sex and age in Colombia. Methods This was a descriptive retrospective study of hemoglobin variants performed by electrophoresis in patients treated at and/or referred to specialized care institutions in Bogota, Colombia between January 2009 and December 2020. Results A total of 2,224 results were analyzed, 48.4% male and 51.5% female; 63.3% of patients were without alterations, 14.3% presented with thalassemia, 17.3%, HbS, 2.3%, HbS/C, 1.8%, HbC, 0.5%, HbE and 0.5% persistent HbF, with HbS being more prevalent in males (p = 0.005). When assessing the geographical regions of Colombia, a higher prevalence of HbS was found in the Pacific (p = 0.005) and Caribbean regions, while Thalassemia and HbS were more prevalent in the Andean and Orinoquia regions, and it was rare to find any hemoglobinopathies (p = 0.0001) in the Amazonian region. Conclusions The main hemoglobinopathies found in Colombia are HbS, predominantly in males, and Thalassemia. The distribution of hemoglobinopathies in different geographical regions of Colombia is influenced by ancestry

Rasgo Drepanocítico, Manifestaciones Clínicas y Resultados: Un Estudio Transversal en Colombia: Tasa creciente de sujetos sintomáticos que viven a gran altitud

Resumen.Antecedentes:El rasgo drepanocítico (TCE) es un trastorno sanguíneo autosómico recesivo en el que los pacientes son portadores heterocigotos de hemoglobina S (HbAS) y suelen ser asintomáticos. Realizamos un análisis descriptivo de las manifestaciones clínicas y los resultados asociados al TCS. Métodos: Este fue un estudio descriptivo, transversal que incluyó pacientes con SCT desde 2014 hasta 2020 en el Hospital Militar Central, el centro de referencia de las Fuerzas Militares en Bogotá, Colombia. Resultados: De 647 electroforesis de hemoglobina analizadas, se identificaron 51 pacientes con SCT, incluyendo 43 hombres (84,3%) y ocho mujeres (15,7%), con una mediana de edad de 22 años (IQR 15-36 años). De ellos, 28 (54,8%) eran afrocolombianos, 23 (45,1%) mestizos colombianos y 31/51 (60,8%) de los pacientes eran militares activos. Veinticuatro pacientes (47,1%) fueron asintomáticos, y Veintisiete pacientes (52,9%) fueron sintomáticos (complicaciones sistémicas); La mayoría de los pacientes que presentaron síntomas eran militares activos de las fuerzas militares colombianas. Las complicaciones esplénicas fueron las más importantes (85,2%), p=0,0005, y hubo un amplio espectro de complicaciones esplénicas. Además, se encontraron elevaciones significativas de leucocitos, bilirrubina, LDH y PCR. Dieciocho pacientes (66,7%) recibieron tratamiento médico, cinco (18,5%) requirieron esplenectomía y sólo el 5,9% de los pacientes fueron enviados a consejo genético.Conclusiones: El personal militar es una población con alto riesgo de desarrollar síntomas, y las complicaciones esplénicas fueron las más relevantes en los pacientes sintomáticos. La mayoría de los pacientes recibieron tratamiento médico, y el 18,5% de los pacientes requirieron esplenectomía. Nuestros resultados reflejan la ausencia de redirección de estos pacientes a consejo genéticoAbstract.Background:Sickle cell trait (SCT) is an autosomal recessive blood disorder in which patients are heterozygous carriers for hemoglobin S (HbAS) and are usually asymptomatic. We performed a descriptive analysis of clinical manifestations and outcomes associated with SCT. Methods: This was a descriptive, cross-sectional study that included patients with SCT from 2014 to 2020 at Hospital Militar Central, the reference center of the Military forces in Bogota, Colombia. Results: Of 647 hemoglobin electrophoresis analyzed, we identified 51 patients with SCT, including 43 males (84.3%) and eight females (15.7%), with a median age of 22 years (IQR 15–36 years). Of these, 28 (54.8%) were Afro-Colombian, 23 (45.1%) were Colombian mestizos, and 31/51 (60.8%) of patients were active military members. Twenty-four patients (47.1%) were asymptomatic, and Twenty-seven patients (52.9%) were symptomatic (systemic complications); Most of the patients who presented symptoms were active military members of the Colombian military forces. Splenic complications were the most important (85.2%), p=0.0005, and there was a wide spectrum of splenic complications. In addition, we found significant elevations in leukocytes, bilirubin, LDH, and CRP. Eighteen patients (66.7%) received medical management, five (18.5%) required splenectomy, and only 5.9% of patients were sent for genetic counseling.Conclusions: Military Personnel isa population with a high risk of developing symptoms, and splenic complications were the most relevant in symptomatic patients. Most patients received medical treatment, and 18.5% of patients required splenectomy. Our results reflect the absence of redirection of these patients to genetic counseling

Data-Efficient Neural Network for Track Profile Modelling in Cold Spray Additive Manufacturing

Cold spray is emerging as an additive manufacturing technique, particularly advantageous when high production rate and large build sizes are in demand. To further accelerate technology’s industrial maturity, the problem of geometric control must be improved, and a neural network model has emerged to predict additively manufactured geometry. However, limited data on the effect of deposition conditions on geometry growth is often problematic. Therefore, this study presents data-efficient neural network modelling of a single-track profile in cold spray additive manufacturing. Two modelling techniques harnessing prior knowledge or existing model were proposed, and both were found to be effective in achieving the data-efficient development of a neural network model. We also showed that the proposed data-efficient neural network model provided better predictive performance than the previously proposed Gaussian function model and purely data-driven neural network. The results indicate that a neural network model can outperform a widely used mathematical model with data-efficient modelling techniques and be better suited to improving geometric control in cold spray additive manufacturing

Data-Driven Overlapping-Track Profile Modeling in Cold Spray Additive Manufacturing

Acknowledgements: This work was supported by CSIRO’s Active Integrated Matter Future Science Platform (AIM-FSP). Daiki Ikeuchi received Research Training Program (International) Scholarship from the Department of Education, Skills and Employment, Australia. The authors thank CSIRO Characterization for access to LEXT OLS4000 conformal laser scanning microscope.Funder: Commonwealth Scientific and Industrial Research Organisation (CSIRO)AbstractCold spray additive manufacturing is an emerging solid-state deposition process that enables large-scale components to be manufactured at high-production rates. Control over geometry is important for reducing the development and growth of defects during the 3D build process and improving the final dimensional accuracy and quality of components. To this end, a machine learning approach has recently gained interest in modeling additively manufactured geometry; however, such a data-driven modeling framework lacks the explicit consideration of a depositing surface and domain knowledge in cold spray additive manufacturing. Therefore, this study presents surface-aware data-driven modeling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modeling framework explicitly incorporated two relevant geometric features (i.e., surface type and polar length from the nozzle exit to the surface) and a widely adopted Gaussian superposing model as prior domain knowledge in the form of an explicit mean function. It was shown that the proposed model could provide better predictive performance than the Gaussian superposing model alone and the purely data-driven Gaussian Process model, providing consistent overlapping-track profile predictions at all overlapping ratios. By combining accurate prediction of track geometry with toolpath planning, it is anticipated that improved geometric control and product quality can be achieved in cold spray additive manufacturing.</jats:p

Data-Driven Overlapping-Track Profile Modeling in Cold Spray Additive Manufacturing

Cold spray additive manufacturing is an emerging solid-state deposition process that enables large-scale components to be manufactured at high-production rates. Control over geometry is important for reducing the development and growth of defects during the 3D build process and improving the final dimensional accuracy and quality of components. To this end, a machine learning approach has recently gained interest in modeling additively manufactured geometry; however, such a data-driven modeling framework lacks the explicit consideration of a depositing surface and domain knowledge in cold spray additive manufacturing. Therefore, this study presents surface-aware data-driven modeling of an overlapping-track profile using a Gaussian Process Regression model. The proposed Gaussian Process modeling framework explicitly incorporated two relevant geometric features (i.e., surface type and polar length from the nozzle exit to the surface) and a widely adopted Gaussian superposing model as prior domain knowledge in the form of an explicit mean function. It was shown that the proposed model could provide better predictive performance than the Gaussian superposing model alone and the purely data-driven Gaussian Process model, providing consistent overlapping-track profile predictions at all overlapping ratios. By combining accurate prediction of track geometry with toolpath planning, it is anticipated that improved geometric control and product quality can be achieved in cold spray additive manufacturing