Unexpected Event Prediction in Wire Electrical Discharge Machining Using Deep Learning Techniques

Theoretical models of manufacturing processes provide a valuable insight into physical phenomena but their application to practical industrial situations is sometimes difficult. In the context of Industry 4.0, artificial intelligence techniques can provide efficient solutions to actual manufacturing problems when big data are available. Within the field of artificial intelligence, the use of deep learning is growing exponentially in solving many problems related to information and communication technologies (ICTs) but it still remains scarce or even rare in the field of manufacturing. In this work, deep learning is used to efficiently predict unexpected events in wire electrical discharge machining (WEDM), an advanced machining process largely used for aerospace components. The occurrence of an unexpected event, namely the change of thickness of the machined part, can be effectively predicted by recognizing hidden patterns from process signals. Based on WEDM experiments, different deep learning architectures were tested. By using a combination of a convolutional layer with gated recurrent units, thickness variation in the machined component could be predicted in 97.4% of cases, at least 2 mm in advance, which is extremely fast, acting before the process has degraded. New possibilities of deep learning for high-performance machine tools must be examined in the near future.The authors gratefully acknowledge the funding support received from the Spanish Ministry of Economy and Competitiveness and the FEDER operation program for funding the project "Scientific models and machine-tool advanced sensing techniques for efficient machining of precision components of Low Pressure Turbines" (DPI2017-82239-P) and UPV/EHU (UFI 11/29). The authors would also like to thank Euskampus and ONA-EDM for their support in this project

A RE Methodology to achieve Accurate Polygon Models and NURBS Surfaces by Applying Different Data Processing Techniques

The scope of this work is to present a reverse engineering (RE) methodology to achieve accurate polygon models for 3D printing or additive manufacturing (AM) applications, as well as NURBS (Non-Uniform Rational B-Splines) surfaces for advanced machining processes. The accuracy of the 3D models generated by this RE process depends on the data acquisition system, the scanning conditions and the data processing techniques. To carry out this study, workpieces of different material and geometry were selected, using X-ray computed tomography (XRCT) and a Laser Scanner (LS) as data acquisition systems for scanning purposes. Once this is done, this work focuses on the data processing step in order to assess the accuracy of applying different processing techniques. Special attention is given to the XRCT data processing step. For that reason, the models generated from the LS point clouds processing step were utilized as a reference to perform the deviation analysis. Nonetheless, the proposed methodology could be applied for both data inputs: 2D cross-sectional images and point clouds. Finally, the target outputs of this data processing chain were evaluated due to their own reverse engineering applications, highlighting the promising future of the proposed methodology.This research was funded by the he Department of Economic Development, Sustainability and Environment of the Basque Government for funding the KK-2020/00094 (INSPECTA) research project and the Spanish Ministry of Science and Innovation for funding the ALASURF project (PID2019-109220RB-I00)

Enhancement of ceramic tool behaviour with textured grooves during machining of Inconel® 718

Inconel® 718, known for its excellent mechanical properties in extreme conditions, presents machining challenges due to its low machinability. The chip formation process, influenced by its high ductility and low thermal conductivity, leads to material adhesion and high cutting forces. Ceramic tools have been proposed to mitigate these inconveniences. Textured cutting tools have emerged as a promising solution, aiming to optimise tool-chip contact and, with it, the tribological conditions and the cutting forces. This study investigates the influence of textured grooves on ceramic tools when turning Inconel® 718. Two groove inclinations, 0° and − 25° relative to the cutting edge, were tested. Texturing was performed using a laser station. Experimental results showed improved tool wear characteristics with textured tools, indicating favourable chip extraction and reduced material adhesion. Cutting forces were notably lower with textured tools compared to the reference tool, attributed to reduced notch wear and altered chip flow. Chip morphology analysis revealed differences in chip shape and thermal stability between the reference and textured tools. In conclusion, textured tools, particularly those with − 25° inclined grooves, demonstrated enhanced performance in machining Inconel® 718.Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. The authors are grateful to the Basque government group IT IT1337-19, to project CPP2021-008932 funded by the Spanish Ministry of Science MCIN/AEI/https://doi.org/10.13039/501100011033 and European Union “NextGenerationEU”/PRTR, and the UPV/EHU itself for the financial aid for the pre-doctoral grant PIF 19/96

Experimental Investigations of Using Aluminum Oxide (Al2O3) and Nano-Graphene Powder in the Electrical Discharge Machining of Titanium Alloy

In the present study, a comprehensive parametric analysis was carried out using the electrical discharge machining of Ti6Al4V, using pulse-on time, current, and pulse-off time as input factors with output measures of surface roughness and material removal rate. The present study also used two different nanopowders, namely alumina and nano-graphene, to analyze their effect on output measures and surface defects. All the experimental runs were performed using Taguchi’s array at three levels. Analysis of variance was employed to study the statistical significance. Empirical relations were generated through Minitab. The regression model term was observed to be significant for both the output responses, which suggested that the generated regressions were adequate. Among the input factors, pulse-off time and current were found to have a vital role in the change in material removal rate, while pulse-on time was observed as a vital input parameter. For surface quality, pulse-on time and pulse-off time were recognized to be influential parameters, while current was observed to be an insignificant factor. Teaching–learning-based optimization was used for the optimization of output responses. The influence of alumina and nano-graphene powder was investigated at optimal process parameters. The machining performance was significantly improved by using both powder-mixed electrical discharge machining as compared to the conventional method. Due to the higher conductivity of nano-graphene powder, it showed a larger improvement as compared to alumina powder. Lastly, scanning electron microscopy was operated to investigate the impact of alumina and graphene powder on surface morphology. The machined surface obtained for the conventional process depicted more surface defects than the powder-mixed process, which is key in aeronautical applications.This research received some help from the Basque government through University research groups, grant IT1573-22. Authors work in cooperation under a common agreement in the field of EDM

Characteristics of Induced-Sputum Inflammatory Phenotypes in Adults with Asthma : Predictors of Bronchial Eosinophilia

The objectives of this study were, for patients attending a specialist asthma clinic at a tertiary care hospital, to determine, from sputum induction (SI), proportions of bronchial inflammatory phenotypes, demographic, clinical and functional characteristics of each phenotype, and the most accessible non-invasive inflammatory marker that best discriminates between phenotypes. Included were 96 patients with asthma, attending a specialist asthma clinic at a tertiary care hospital, who underwent testing as follows: SI, spirometry, fractional exhaled nitric oxide (FeNO), blood eosinophilia, total immunoglobulin E (IgE), and a skin prick test. SI phenotypes were 46.9% eosinophilic, 33.3% paucigranulocytic, 15.6% neutrophilic, and 4.2% mixed. No significantly different clinical or functional characteristics were observed between the phenotypes. A positive correlation was observed between SI eosinophilia and both emergency visits in the last 12 months (p = 0.041; r = 0.214) and FeNO values (p = 0.000; r = 0.368). Blood eosinophilia correlated with SI eosinophilia (p = 0.001; r = 0.362) and was the best predictor of bronchial eosinophilia, followed by FeNO, and total blood IgE (area under the receiver operating characteristic curve (AUC-ROC) 72%, 65%, and 53%, respectively), although precision was only fair. In consultations for severe asthma, the most frequent phenotype was eosinophilic. Peripheral blood eosinophilia is a reliable marker for discriminating between different bronchial inflammatory phenotypes, is useful in enabling doctors to select a suitable biologic treatment and so prevent asthma exacerbation, and is a better predictor of bronchial eosinophilia than FeNO and IgE values

Model d’atenció a la salut de les persones trans*

Persones trans; Atenció a la salut; Identitat de gènerePersonas trans; Atención a la salud; Identidad de géneroTrans people; Health care; Gender identityL'objectiu d'aquest document és l'ordenació de la cartera de serveis de l’atenció a la salut de les persones trans en el seu procés de transició en la identitat de gènere sentida a càrrec del sistema sanitari públic de Catalunya a partir d’un model consensuat i d’un protocol clínic marc per a l’atenció de la salut