Comparison between the machinability of different titanium alloys (Ti-6Al-4V and Ti-6Al-7Nb) employing the multi-objective optimization

Titanium and its alloys are amongst the most important metallic materials used by many industries, such as those pertaining to the aerospace, automotive, and biomedical sectors. This is due to the reliability and functionality of titanium components, in addition to their high strength-to-weight ratio and corrosion resistance. Thus, titanium and its alloys are of great importance to the challenging operations of these sectors. The manufacturing of titanium requires great accuracy to ensure that resulting products meet quality requirements, due to its difficult machinability. In this study, the cutting forces and surface roughness of the turning were analysed to compare different titanium alloys, Ti–6Al–4V and Ti–6Al–7Nb, with CVD-coated and uncoated inserts. The effect of control factors on the response variables was measured using ANOVA. Response surface methodology was applied to the creation of a model of responses and to a bi-objective optimization process via the normalized normal constraint method. The Pareto-optimal sets of both alloys were achieved, which may be applied to practical situations to achieve optimal results for these responses. The models and optimization results confirmed the similarity of machinability values between the Ti–6Al–4 V and Ti–6Al–7Nb alloys. The uncoated inserts yielded the best surface roughness and cutting force results when used with both titanium alloys.publishe

Multivariate robust modelling and optimization of cutting forces of the helical milling process of the aluminum alloy Al 7075

Helical milling is an advanced hole-making process and different approaches considering controllable variables have been presented addressing modelling and optimization of machining forces in helical milling. None of them considers the importance of the noise variables and the fact that machining forces components are usually correlated. Exploring this issue, this paper presents a multivariate robust modelling and optimization of cutting forces of the helical milling of the aluminum alloy Al 7075. For the study, the tool overhang length was defined as noise variable since in cavities machining there are specific workpiece geometries that constrain this variable; the controllable variables were axial feed per tooth, tangential feed per tooth and cutting speed. The cutting forces in the workpiece coordinate system were measured and the components in the tool coordinate system, i.e., the axial and radial forces, were evaluated. Since these two outcomes are correlated, the weighted principal component analysis was performed together with the robust parameter design to allow the multivariate robust modelling of the mean and variance equations. The normal boundary intersection method was used to obtain a set of Pareto robust optimal solutions related to the mean and variance equations of the weighted principal component. The optimization of the weighted principal component through the normal boundary intersection method was performed and the results evaluated in the axial and radial cutting forces components. Confirmation runs were carried out and it was possible to conclude that the models presented good fit with experimental data and that the Pareto optimal point chosen for performing the confirmation runs is robust to the tool overhang length variation. Finally, the cutting force models were also presented for mean and variance in the workpiece coordinate system in the time domain, presenting low error regarding the experimental test, endorsing the results.publishe

Steatite/Epoxy Composites for Restoration Works Through a Statistical Mixture Design Methodology

Currently many works of art made of soapstone and recognized as cultural patrimony of humanity are in an advanced stage of degradation. Hence, it is necessary to interrupt this process and recover the deteriorated parts. Composite materials consisted of steatite particles and epoxy polymer are designed and characterised for their application in the repair of sculptures made of soapstone. The material applied in restorations should provide coloration and texture similar to soapstone besides structural requirements. The degree of similarity of the artificial material to the rock is enhanced by the proper selection of the particle size range and the increase of steatite incorporation in the composites. A statistical methodology based on the mixture design is used to optimize the relative amount of three particle size fractions of steatite particles in order to maximise the proportion of the dispersed phase in the composites. The maximum particle packing density (1.50 g/cm³) is obtained for a ternary mixture, composed of 62% of coarse particles (1.18 mm - 0.60 mm), 6% of medium sized particles (0.60 mm - 0.30 mm) and 32% of fine particles (0.30 mm - 0.15 mm). In this manner, the fabrication of composites based on an epoxy polymer matrix with 70wt% of incorporated steatite particles has been possible, increasing the maximum amount by 10% as used in previous works

Multivariate GR&R through factor analysis

Several measurement tasks present multivariate nature. In the cases with quality characteristics highly correlated within groups, but with a relatively small correlation between groups, the available multivariate GR&R methods are not suitable to provide a correct interpretation of the results. The present work presents a new multivariate GR&R approach through factor analysis. Factor analysis is a multivariate statistical method which focuses on the explanation of the covariance structure of the data. Through orthogonal rotation of the factors a suitable structure can be achieved with loadings easy to relate the variables to the factors. The proposed multivariate GR&R method through factor analysis is described and applied in the quality evaluation of holes obtained through helical milling process of AISI H13 hardened steel. The method succeeded in achieving a simple structure, with one factor related to the roughness outcomes and other related to the roundness ones, simplifying the gage capability evaluation.publishe

Tool wear in dry helical milling for hole-making in AISI H13 hardened steel

Helical milling is a hole-making process which can be applied to achieve high-quality finished boreholes in hardened steels. Due to the drilling process limitations, which are intensified when applied in hardened steels, the helical milling process can be applied on hole-making tasks in moulds and dies industry, since milling have been widely applied in moulds and dies machining to replace high-cost operations like grinding and electrical discharge machining. However, to succeed in achieving high-quality boreholes in hardened parts, which presents high added value due to previous operations, tool wear in the helical milling of hardened steels should be more investigated. In the present study, dry helical milling tool life tests were conducted in AISI H13 hardened steel parts, varying the cutting velocity. The flank wear on frontal cutting edges was progressively measured through optical microscopy, and SEM/EDS was performed in frontal and peripheral worn cutting edges. The wear occurred progressively in the flank of the frontal cutting edges with adhesion and oxidation as main wear mechanisms. In the peripheral edges, coating loss, and adhesion of workpiece material in the tool clearance surface were observed, besides fracture in the tool nose flank with the highest cutting velocity. A nested ANOVA was performed to evaluate the burr height in the borehole exit. The tool life stage was statistically significant in the burr height.publishe

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART): Study protocol for a randomized controlled trial

Background: Acute respiratory distress syndrome (ARDS) is associated with high in-hospital mortality. Alveolar recruitment followed by ventilation at optimal titrated PEEP may reduce ventilator-induced lung injury and improve oxygenation in patients with ARDS, but the effects on mortality and other clinical outcomes remain unknown. This article reports the rationale, study design, and analysis plan of the Alveolar Recruitment for ARDS Trial (ART). Methods/Design: ART is a pragmatic, multicenter, randomized (concealed), controlled trial, which aims to determine if maximum stepwise alveolar recruitment associated with PEEP titration is able to increase 28-day survival in patients with ARDS compared to conventional treatment (ARDSNet strategy). We will enroll adult patients with ARDS of less than 72 h duration. The intervention group will receive an alveolar recruitment maneuver, with stepwise increases of PEEP achieving 45 cmH(2)O and peak pressure of 60 cmH2O, followed by ventilation with optimal PEEP titrated according to the static compliance of the respiratory system. In the control group, mechanical ventilation will follow a conventional protocol (ARDSNet). In both groups, we will use controlled volume mode with low tidal volumes (4 to 6 mL/kg of predicted body weight) and targeting plateau pressure <= 30 cmH2O. The primary outcome is 28-day survival, and the secondary outcomes are: length of ICU stay; length of hospital stay; pneumothorax requiring chest tube during first 7 days; barotrauma during first 7 days; mechanical ventilation-free days from days 1 to 28; ICU, in-hospital, and 6-month survival. ART is an event-guided trial planned to last until 520 events (deaths within 28 days) are observed. These events allow detection of a hazard ratio of 0.75, with 90% power and two-tailed type I error of 5%. All analysis will follow the intention-to-treat principle. Discussion: If the ART strategy with maximum recruitment and PEEP titration improves 28-day survival, this will represent a notable advance to the care of ARDS patients. Conversely, if the ART strategy is similar or inferior to the current evidence-based strategy (ARDSNet), this should also change current practice as many institutions routinely employ recruitment maneuvers and set PEEP levels according to some titration method.Hospital do Coracao (HCor) as part of the Program 'Hospitais de Excelencia a Servico do SUS (PROADI-SUS)'Brazilian Ministry of Healt