Service Conditions Research Articles

Creep Lifetime Prediction of Alloy 617 Using Black Box Machine Learning Approach

Abstract This study explored the application of black box machine learning (ML) to build high throughput models that predict the creep response of Ni-based Alloy 617. Black box ML refers to highly complex machine learning algorithms that generate outputs from inputs without an interpretable internal process. The rapid implementation of suitable heat of alloys into targeted service is impeded by the extended qualification process involving chemistry-processing-structure-performance assessment. The ASME B&PV Code III outlines the requirement of 10,000 h of creep testing before each heat can be qualified for service and 30,000 h for heats that exhibit metastable phases. There is a critical need to shorten the development-to-deployment timeline for heats of an alloy at specific applications. Recent advancement in ML offers the ability to identify correlations in data which is difficult to elucidate by other approaches. To that end, black box ML is employed to expedite the HEAT qualification of Alloy 617 and predict performance from HEAT chemistry out to an unprecedented timescale. In this study, creep data for Ni-based Alloy 617—a solid solution strengthened material is gathered from a wide range of data sources. The alloy chemistry, phases, precipitates, and microstructural features are analyzed to obtain the key strengthening mechanism. Service conditions, mechanical properties, chemistry, and chemical ratios are provided as potential input features. The Pearson correlation coefficient with a 95% confidence bound is employed for input feature screening where poorly correlated inputs are eliminated to speed up the ML process and prevent under- and/or over-fitting of predictions. In the ML algorithm, the selected input features are regarded as predictors, and rupture is regarded as the response. An algorithm evaluation is performed where 20 ML algorithms are trained with the training set. The three-layered neural network (NN) was observed to be the best algorithm for the given data based on statistical rationale. The NN accurately predicts rupture across a range of isotherms and data sources. The interpolative and extrapolative predictions are in compliance with ECCC V5 guidelines. A post-audit validation exhibits neither under- nor over-fitting and confirms the applicability of NN algorithms to unseen data. The black box ML provides a pathway to predict the performance directly from chemistry and opens avenues to rapid heat qualification.

Low-temperature oxidation behaviors of Cu-15Ni-8Sn alloy in different aging conditions

Cu-15Ni-8Sn (wt%) alloy has been widely used in engineering applications due to its excellent mechanical strength, wear, and corrosion resistance. However, low-temperature oxidation (LTO) poses a significant challenge, restricting its further application and longevity. In this study, the low-temperature oxidation behavior of the Cu-15Ni-8Sn alloy under different aging condition was investigated using multi-scale characterization by SEM and TEM. The characterization results reveal the oxide films of the aged sample contain tri-layers at low temperature, the outermost layer is dominated by CuO and Cu2O, the middle oxide layer contains of Cu2O, NiO, SnO2 and SnO, and the inner oxide layer is composed of NiO and SnO2. In addition, the as-quenched sample has superior oxidation resistance compared to the aged samples, and the oxidation resistance of the aged samples decreases with the increase of aging time. That is, there is a trade-off between hardness and oxidation resistance in aged Cu-15Ni-8Sn alloys. Therefore, these findings provided a way to adjust the aging parameter based on specific service conditions.

Data-driven AI for the automated classification of the isothermal heat-treated thermal barrier coatings using pulsed infrared thermography

Abstract Thermal barrier coating (TBC) is a multilayer coating applied to metallic structures exposed to high temperatures, such as aero-engine parts and gas turbine blades. These thermally insulating materials are used to extend the component life by minimizing the high-temperature exposure of structural components. As a result of the high-temperature oxidation tests, a thermally grown oxide (TGO) layer formed at the interface between the ceramic topcoat layer and the bond coat layer. The primary cause of TBC performance decline and structure failure is the growth of the oxide layer. Hence, it is crucial to regularly monitor the formation of the oxide layer and the degradation of coatings. This can be achieved by developing coating-life prediction models, which are vital for quality control, health monitoring of TBCs, maintenance decision-making, and the prevention of catastrophic failures. Existing methods for life estimation, which are dependent on empirical methods and microstructural analysis, often fail due to various material compositions and service conditions. This study proposes an automated classification of iso-thermal heat-treated TBC samples using temperature data, which helps in predicting the TBC life and monitoring the TBC degradation. TBC-coated samples are isothermal heat-treated at 1000 °C, and the initial growth of TGO is monitored using a non-destructive thermal imaging technique. Identifying distinctive features in each class corresponding to layer thickness measurement and service hours is challenging and time-consuming. So, we integrate data-driven AI models and feature extraction techniques to interpret complex thermal patterns. The performance of deep learning models and classical machine learning models demonstrates that our method can achieve maximum classification accuracy and F1-score of 89.2% and 89.0%, respectively. This automated classification framework promises a powerful tool for predictive maintenance and remaining useful lifetime estimation of hot section components reliant on TBCs.

Optimization of the Thermokinetic Method for the Control of Weld Decay in AISI 304L and AISI 316L Stainless Steel Weldment

This study investigates the control of weld decay in AISI 304L/316L alloy weldments for liquefied natural gas (LNG) and cryogenic environments using the Tungsten Inert Gas (TIG) welding technique. Weldment samples were thermokinetically treated at 1050, 1100, and 1150°C and cooled in five mediums: Water, Salt, Natural Air, Salt with annealing, and Water with annealing, to retain carbon and chromium in solid solution at approximately 30°C. Furthermore, evaluation methods based on metallography i.e. optical microscopy (OM), Scanning Electron Microscopy (SEM) and Energy Dispersive X-Ray Spectroscopy (EDX), Wear Test (i.e. wear rate and wear track) and electrochemical corrosion potential measurements were adopted. An experimental design table was developed using the design expert software 13.0. This helped develop a predictive mathematical model for ascertaining the optimum operating service conditions of the material. From the results, the optical microscopy analysis revealed that the control sample exhibited a more irregular pattern than others. Results showed that the control sample had a more irregular structure, while air-cooled samples exhibited smoother surfaces, indicating better bonding. SEM revealed a coarse surface with uneven particle distribution post-heat application. The predominant elements were Iron (Fe), Chromium (Cr), and Nickel (Ni). Corrosion potential varied between -0.5 to 0.15 V, demonstrating wave-like behaviour over time. Wear analysis indicated that lower coefficients of friction correlate with better wear resistance. Finally, response surface methodology (RSM) revealed that increasing temperature proportionally increased yield and corrosion rates. While the identified optimal values for Temperature are (1112.68°C), Material (316L), and Quenching Medium (SQ+SA), resulting in specific values such as Wear Rate (5.76896E-06), Coefficient of friction (0.224254), Corrosion Rate (0.395566), and Weight of Heat-Treated Sample (14.7797). The study enhances understanding of the mechanisms affecting welding and contributes to optimizing welding procedures for improved mechanical properties and corrosion resistance.

Evolution of Microstructural and Mechanical Properties of Alloy 617B During Service on a Key-Component Test Platform at 700 °C

The evolution of the microstructural and mechanical properties of alloy 617B during long-term service on a key-component test platform at 700 °C was systematically investigated. The precipitation behavior and size changes of the M23C6 and γ′ phases were characterized by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The results showed that carbide M23C6 precipitated in the form of discontinuous particles, plates, or needles at grain boundaries and within grains, while the γ′ phase had a spherical shape and was distributed in a dispersed manner. With prolonged service time, both the M23C6 and γ′ phases gradually coarsened. After 24,000 h of service, the yield strength, tensile strength, and Brinell hardness of alloy 617B significantly increased; however, the impact toughness decreased, accompanied by intergranular embrittlement. The increase in precipitate volume fraction and its contribution to the strength of the alloy were evaluated by a precipitation strengthening model. The coarsening of M23C6 was identified as the main cause of embrittlement. The findings of this study provide important experimental data and theoretical support for the stability of 617B alloys under long-term high-temperature service conditions.

Characterization of physical metallurgy of quenching and partitioning steel in pulsed resistance spot welding: A simulation-aided study

This study mainly focuses on the microstructure evolution of QP980 steel during resistance spot welding and its influence on the mechanical performance of resistance spot welds which is a critical influencing factor on the quality of body-in-white at the service condition. It is observed that the thermomechanically engineered microstructure of QP980 steel changes to form metastable phases such as martensite in the fusion zone and the heat affected zone due to rapid cooling induced by the thermal cycle of the welding. A finite element modeling of the welding process was used to predict the weldment heat distribution, thermal history and microstructure evolution in different welding zones. The modeled thermal history of the weldments shows that the peak temperature in the four-pulse resistance spot welding is delayed because of pulsed welding conditions and holding times between the welding pulses. This heat management approach in pulsed welding prevents void formation. The modeled thermal history and rapid heating, and cooling conditions are discussed here to predict the microstructure evolution and transformation in the fusion and reheated zones. The modeled results were helpful in the prediction of the microstructure at different weld zones. Then the strategic links between the microstructure and mechanical performance of the welded alloy are discussed thoroughly. The microhardness profile of the weld is discussed from a microstructural point of view to disclose the physical metallurgy of the welds. Softening phenomena were not observed in the sub-critical heat affected zone.

Research on the wheel wear and rolling contact fatigue of metro vehicle induced by the rail corrugation

The rail corrugation is a common disease on the track, which exacerbates the wheel-rail interaction and accelerates wheel wear and crack propagation. This study focuses on the wheel wear and rolling contact fatigue of metro vehicle under the rail corrugation. Firstly, the characteristics of the rail corrugation under service conditions are determined by the statistical analysis of the field data, and the rail corrugation is applied to the established vehicle-track coupled dynamics model. Then, the wear index and the work of friction force as indicators of wheel wear are studied. Finally, based on the Shakedown Map and the criterion of maximum amplitude of shear stress, the simulation analysis of wheel-rolling contact fatigue under the rail corrugation is carried out. The results show that the increases in superficial fatigue index and accumulated fatigue are 1.52 and 6.16 times in the wavelength range of 30-90 mm larger than those in the range of 90-210 mm, respectively. Furthermore, the cracks more easily occur 2-4 mm from the outer surface of wheel, and expand along the wheel tread.

Improving Collaborative Interactions Between Humans and Artificial Intelligence to Achieve Optimal Patient Outcomes in the Healthcare Industry

The introduction of Artificial Intelligence (AI) into the healthcare industry holds immense promise for improving patient outcomes. However, the interaction between healthcare professionals and AI systems is critical to fully realize the potential benefits. Artificial intelligence (AI) applications have transformed healthcare. This study is based on a general literature review uncovering the role of Al in healthcare and focuses on the following key aspects: medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation and other administrative applications. The impact of AI is observed in detecting clinical conditions in medical imaging and diagnostic services, controlling the outbreak of coronavirus disease 2019 (COVID-19) with early diagnosis, providing virtual patient care using Ai-powered tools, managing electronic health records, augmenting patient engagement and compliance with the treatment plan, reducing the administrative workload of healthcare professionals (HCPs), discovering new drugs and vaccines, spotting medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. Nevertheless, this science pitch meets several technical, ethical, and social challenges, including privacy, safety, the right to decide and try, costs, information and consent, access, and efficacy, while integrating AI into healthcare. The governance of AI applications is crucial for patient safety and accountability and for raising HCPs’ belief in enhancing acceptance and boosting significant health precisely address regulatory, ethical, and trust issues while advancing the acceptance and implementation of AI. Since COVID-19 hit the global health system, the concept of AI has created a revolution in healthcare, and such an uprising could be another step forward to meet future healthcare needs.

THE IMPERATIVE OF INSTITUTING A MILITARY OMBUDSMAN AND ITS POTENTIAL FRAMEWORKS

The Secretariat of the Human Rights Commissioner of the Verkhovna Rada of Ukraine includes the Department for Monitoring the Observance of Rights in the Defense Sector and the Rights of Veterans and Servicemen, Prisoners of War and Their Family Members. The Department’s main task is to ensure that the Parliamentary Commissioner for the Observance of the Constitutional Rights and Freedoms of Servicemen exercises his or her powers. During 2022, the Human Rights Commissioner recorded a more than twenty-fold increase in appeals by servicemen regarding the violation of their rights compared to the previous year. In addition, the range of issues on which servicemen and their family members are appealing has expanded. In 2020–2021, the majority of appeals and complaints were related to military service conditions, timely payments, including adequate financial support for the families of fallen servicemen, pension recalculations, and non-compliance by the Pension Fund of Ukraine and other state bodies with court decisions restoring the rights of military personnel, military pensioners and members of their families. During 2022–2023, appeals also included the search for missing persons and the return of captives. The latter account for 72 % of all appeals. At the same time, the percentage of responses to these appeals is extremely low. The published data indicate that the Department for Monitoring the Observance of Rights in the Defense Sector and the Rights of Veterans and Servicemen, Prisoners of War and Their Family Members might not fully cover social and legal protection of these groups. The low response rate compared to the volume of complaints about human rights violations in the security and defense sector may be due to the lack of funding and the lack of qualified specialists. Given the ongoing war between the Russian Federation and Ukraine, the number of appeals will increase, and the volume of unresolved issues will most likely accumulate. Therefore, taking current realities into account, the increase in the number of servicemen and other defense sector employees, it is advisable to follow best practices worldwide and separate the institute of the military ombudsman from the institute of the Parliamentary Human Rights Commissioner. This can be done by introducing an appropriate legislative act and establishing an independent structure with expanded powers regarding veterans, prisoners of war and civilian hostages with its own financing and staff.

Concept, content and structure of the phenomenon of professional adaptation of military servicemen to military service through physical training and sports

The article is devoted to the analysis of the concept, content and structure of professional adaptation of military personnel to military service using means of physical training and sports. It was determined that the professional adaptation of military personnel is a complex process that includes psychophysiological, social and professional components. Special attention is paid to the role of physical training and sports in ensuring the effective adaptation of servicemen to the conditions of service, in particular in the context of the formation of the necessary physical, moral and willpower qualities and professional skills. On the basis of a systemic approach, the main elements and stages of the adaptation process, as well as their interrelationship and influence on the general level of combat readiness of servicemen, are revealed. Means of physical training and sports play an important role in the professional adaptation of military personnel. They contribute to increasing physical endurance, strengthening health, developing mental stability and forming teamwork skills. Thus, the professional adaptation of military personnel is the result of the complex influence of physical, psychological and social factors. Means of physical training and sports are important tools that contribute to a successful adaptation process, providing recruits with not only physical strength, but also psychological readiness for service, as well as integration into the military team.

Influence of social media and the digital environment on international migration of health workforce from low- and middle-income countries post COVID-19 pandemic: a scoping review protocol

IntroductionMigration of the health workforce from low- and middle-income countries (LMCIs) is increasingly becoming a phenomenon of interest within migration governance systems. The COVID-19 pandemic aggravated health workforce shortages that...

Grain disintegration and dynamic recrystallization during impact tests of additively manufactured nickel-based alloy 718

The high-temperature dynamic mechanical response of Alloy 718 produced via laser-powder bed fusion (LPBF) was investigated through compressive Split-Hopkinson Pressure Bar (SHPB) tests. Simulating the typical service conditions of Alloy 718, the tests were conducted at temperatures ranging from 298 K to 773 K and at strain rates of 1000 s−1 to 1500 s−1. Phenomenological material constitutive models, such as the modified versions of Johnson-Cook and Hensel-Spittel models, were developed based on the SHPB test results. Analysis of microstructural evolution under impact conditions highlighted that columnar grains with high Schmid factors tend to undergo preferential activation and dislocation pile-up. This process leads to the formation of adiabatic shear bands, grain disintegration, and intense lattice rotation, particularly at higher strain rates. Furthermore, increasing the dynamic deformation temperature facilitates the activation of discontinuous dynamic recrystallization (DRX), with strain accumulation promoting localized grain nucleation along heavily dislocated dendritic boundaries. Recognizing the limitations of phenomenological material constitutive models in accurately representing the underlying microstructural evolution, an artificial neural network (ANN)-based constitutive model employing a three-layer backpropagation learning algorithm was implemented, reducing the Average Absolute Relative Error (AARE) to 0.17%.

On the Possibilities and Significance of Unmanned Aerial Vehicles for the Pre- Hospital Stage of Medical Care

Unmanned aerial vehicles are an important force in search and rescue operations. They help reduce the time needed to search for and provide assistance to the wounded, sick and injured who are located at a large territorial distance and in hard-to-reach places. With the help of computer “vision” and sensors such as noise sensing, binary sensing, vibration and thermal sensing, drones are able to search for living patients not only in the sea, high in the mountains and in mines, but also buried under the rubble of buildings and structures. Such devices demonstrate advantages in emergency and urgent delivery of medical resuscitation and other medical equipment, medicines, blood products and organs for transplantation to patients, especially those in remote locations. With the help of drones, it is possible to effectively sort patients in case of mass sanitary losses, carry out disinfection and remotely monitor the health status of patients with highly contagious infectious diseases and other pathological conditions, as well as reduce the time for providing other medical and humanitarian services to the population. It is obvious that the use of drones requires further study of their promising capabilities, especially in the actual conditions of emergency medical services.

Machine learning prediction method for the interface bond strength between fiber reinforced polymer bars and concrete based on multi-feature driven analysis

In severe service conditions, corrosion of reinforcement poses a significant challenge for concrete structures. Fiber reinforced polymer (FRP) materials, characterized by their low weight, high strength, and superior corrosion resistance, are effective in enhancing the durability of these structures. The interfacial bond performance between FRP bars and concrete is crucial for their synergistic action and is pivotal for the design and safety evaluation of FRP-reinforced concrete structures. This paper conducts a comprehensive analysis of factors including the material and mechanical properties of FRP bars, concrete mechanical properties, and bond length. Utilizing an exhaustive search strategy, it identifies the optimal characteristic parameters for assessing the bond strength between FRP bars and concrete. Subsequently, these parameters are integrated with four machine learning (ML) algorithms—DT, SVM, RF and XGB—to train the nonlinear mapping relationship between influencing factors and bond strength, proposing a unified and interpretable prediction method for the FRP bar-concrete interface bond strength. The model's predictive accuracy is then validated using a test dataset, and a comparative analysis is performed among the predictive models generated by the four ML algorithms, as well as against empirical calculation formulas. The findings indicate that the ML models offer superior predictive performance and accuracy over empirical formulas, with the XGB algorithm demonstrating the highest overall accuracy and best performance. Moreover, to address the black-box nature of ML algorithms, the SHAP method is employed to enhance the interpretability of the bond strength prediction process. The newly developed hybrid ML model holds promise as a novel approach for accurately assessing the bond strength at the FRP bar-concrete interface.

Effect of molybdenum addition on precipitate coarsening kinetics in Inconel 740H: A phase-field study

Inconel 740H (IN740H) has emerged as an important candidate for the advanced ultra-supercritical (AUSC) steam turbines due to its superior microstructural stability and creep resistance under service conditions. In the present work, we have reparameterized a multicomponent Ni-based superalloy (IN740H) into an equivalent ternary Ni-Al-Mo superalloy, based on the partitioning coefficients of the elements, using the thermodynamic database (CALPHAD). We use this thermodynamic description to employ a quantitative phase-field model to assess the long-term stability of γ′ precipitates in IN740H utilizing GPU-based supercomputing architecture. The assessment helps us to enhance our understanding of the effect of the atomic diffusivity of Mo on coarsening kinetics of the γ′-precipitates in equivalent ternary Ni-Al-Mo superalloy. Investigation reveals that our phase-field model can accurately predict the experimentally observed coarsening kinetics in IN740H.

Fracture failure of oil-collecting steel wire reinforced thermoplastic composite pipe

A steel wire-reinforced thermoplastic composite pipe (SRTP) used as an oil connecting experienced fracture failure after 1.8 years’ service. In this paper, we conducted through on-site working condition investigation, visual physical inspection, analysis of the inner lining structure composition, physical and chemical properties analysis, thermal analysis, and mechanical analysis, and morphology analysis. The lining material of the SRTP exhibited significant swelling, which reduced its performance, and operation at high temperatures (>75°C) accelerated this process. Due to the swelling of oil and the permeation of CO2 and H2S, the oil and acid gases penetrated the interlayer of the composite pipe and accumulated in the annular space, continuously corroding and weakening the steel wire. This process gradually reduced its load-bearing capacity and pressure resistance. It is recommended that non-metallic pipes should be selected according to the service conditions to ensure that the materials are consistent with the design requirements.

Mixed Mode‐I/II interlaminar fracture toughness Characterization of woven Carbon–Kevlar interyarn hybrid textile composites

AbstractIn actual service conditions, the composite structures are subjected to the combined effect of Mode‐I and Mode‐II loadings. It has great significance in the aerospace, defense, military, automobile, and marine sectors. This study aims to determine the Mixed Mode‐I/II interlaminar fracture toughness (ILFT) of plain and twill woven carbon, Kevlar monolithic, and interyarn hybrid textile composites under several hybridization schemes. The tailored properties are obtained using the interyarn hybridization of high stiff carbon yarn and high tough Kevlar yarn. The effect of individual yarn in the length direction in different hybrid composite laminates is carefully investigated. The Mixed Mode‐I/II ILFT characterization is experimentally performed using a mixed mode bending (MMB) test specimen on the Instron 8862 system under 45%, 55%, and 65% Mixed Mode ratios (MMRs). The increase in mode mixture percentage decreases the lever length, increases the load‐bearing capacity, and increases the Mixed Mode‐I/II ILFT of the MMB test specimen. Plain woven composites exhibited a higher ILFT than twill woven textile composites. The plain woven carbon–Kevlar hybrid composite laminate with carbon yarn‐in‐length direction shows the highest Mixed Mode‐I/II ILFT after CP laminates. Hybridization significantly increases the Mixed Mode‐I/II ILFT compared to monolithic Kevlar fabric‐reinforced composites. Moreover, the failure mechanisms are investigated using scanning electron microscopy.Highlights Mixed Mode‐I/II ILFT of carbon–Kevlar interyarn hybrid textile composites. ILFT at nonlinearity (NL), VIS, and 5%/Max load points for 45%, 55%, and 65% MMR. Advantage of placing carbon yarn‐in‐length direction in hybrid configurations. Effect of carbon–Kevlar interyarn hybridization on Mixed Mode‐I/II ILFT. Fractography of tested specimens to investigate several failure mechanisms.

Free vibration and buckling behavior of porous orthotropic doubly-curved shallow shells subjected to non-uniform edge compression using higher-order shear deformation theory

Due to the continuous enhancement of manufacturing technology for porous materials, it is expected to be increasingly used in aerospace, aviation, and other engineering applications, where the necessity for lightweight structures is paramount. Also, the non-uniform edge loads caused by service conditions, complex adjacent structures, and external loads lead to localized stress concentrations within the shells. Localized stress concentrations result in a loss of load-carrying capacity in specific regions, causing step-by-step failure of the shell. This study investigates the buckling and free vibration behavior of porous orthotropic doubly-curved shallow shells with four different porosity distribution patterns subjected to non-uniform edge compressive loads. The equations of motion of doubly-curved shallow shells are established by using higher-order shear deformation theory and Hamilton’s principle. Then, the pre-buckling in-plane stress distributions of doubly-curved shallow shells are determined and appended to the equations of motion. These fundamental partial differential equations are solved by Galerkin’s method, and the critical buckling load and natural frequency formulations are achieved. By comparing with the results in published literature, the feasibility and accuracy of present formulations are validated. Finally, systematic parametric studies are carried out to discuss the effects of different non-uniform edge compression patterns, porosity distribution patterns, porosity coefficients, aspect ratios, arc length-to-thickness ratios, radius-to-arc length ratios, orthotropy ratios, and shell types on the buckling and free vibration responses of porous orthotropic doubly-curved shallow shells. Parametric studies indicate that the reduction or increment in critical buckling loads (CBLs) and fundamental natural frequencies (FNFs) of spherical shells (SSs) are more sensitive than those of hyperbolic paraboloidal shells (HPSs). The CBLs and FNFs of SSs are larger than those of HPSs. The maximum and minimum CBLs are achieved for the triangular and uniform edge compression, respectively. The porosity effect is independent of edge compression pattern types.

Aircraft joint structure lightweight design

PurposeThe purpose of this article is to carry out a lightweight design of the joint structure according to the service condition of the joint.Design/methodology/approachThe finite element analysis techniques are used along with the variable density structure topology optimization method, the multi-island genetic algorithm for structural dimension optimization and the fatigue life analysis method.FindingsUtilizing the topology optimization method and size optimization method, the mass of the optimized model for the A100 material model is 9.67 kg. Compared to the pre-optimized model, the mass decreases by 8.23 kg, representing a weight reduction of 46.0% in the optimized model; the fatigue life of the aircraft joint is predicted to be a maximum of 1,460,017 cycles.Originality/valueThe originality of this study is that it provides new design ideas for the lightweight design of aircraft load-bearing structures.

COMMON MENTAL DISORDERS IN PERSONS WITH CHRONIC CONDITION: Carthografic analysis of a case in the primary care service

The psychological consequences of chronic diseases still poorly explored, but there are data, pointing to their importance. The study aims analyze the presence of common mental disorders in users with chronic conditions in Primary Care services in a city in the metropolitan region of Porto Alegre, RS, Brazil. The research used cartography as a methodology. Interviews were conducted with users of the Network and focus groups with the responsible teams. From the interviews, a case was highlighted for discussing. Two results are pointed out: the relationship between emotional factors and the chronic condition perceived by the user and the care centered on physical health performed by the responsible professionals. The association between anxiety / depression and chronic condition is discussed based on the concepts of catastrophist thoughts and internalized stigma and presents their respective coping strategies, aided by person-centered care.