Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Evaluation of extremely steep residual stress gradients based on a combined approach using laboratory-scale equipment

Gefördert im Rahmen des Projekts DEA

A Novel Approach to Robustly Determine Residual Stress in Additively Manufactured Microstructures Using Synchrotron Radiation

Gefördert im Rahmen des Projekts DEA

A Novel Approach to Robustly Determine Residual Stress in Additively Manufactured Microstructures Using Synchrotron Radiation

In recent decades additive manufacturing (AM) for years has been in focus of academia and industry as its underlying production principle allows for the realization of designs of unprecedented geometrical complexity. However, often such structures are not realized due to the lack of understanding of structural and mechanical properties, this fact amongst others related to the unique microstructures established by the related processes. In this context, residual stresses, highly affected by the scan strategy and process parameters used, play an essential role. Generally, various methods and approaches can be used to determine residual stress states experimentally. However, especially in case of the unique microstructures formed by AM, most standard procedures cannot be applied reliably. Commonly used methods based on X-ray diffraction rely on laboratory X-ray sources and synchrotron radiation. In present work, a novel method is proposed for robustly calculating residual stresses based on the linear regression method (similar to the sin2 ψ approach in reflection mode). Data obtained by use of synchrotron radiation in transmission mode are applied. To assess the reliability of the novel procedure, results are validated using simulations and in situ tensile tests. For these tests the well-known Ni-base alloy INCONEL 718 processed by laser powder bed fusion (LPBF), being characterized by a complex microstructure, and a conventionally manufactured 100Cr6 steel sample are used

Experimental Analysis of the Stability of Retained Austenite in a Low-Alloy 42CrSi Steel after Different Quenching and Partitioning Heat Treatments

Gefördert im Rahmen des Projekts DEA

Experimental Analysis of the Stability of Retained Austenite in a Low‐Alloy 42CrSi Steel after Different Quenching and Partitioning Heat Treatments

Quenching and partitioning (Q&P) steels are characterized by an excellent combination of strength and ductility, opening up great potentials for advanced lightweight components. The Q&P treatment results in microstructures with a martensitic matrix being responsible for increased strength whereas interstitially enriched metastable retained austenite (RA) contributes to excellent ductility. Herein, a comprehensive experimental characterization of microstructure evolution and austenite stability is carried out on a 42CrSi steel being subjected to different Q&P treatments. The microstructure of both conditions is characterized by scanning electron microscopy as well as X-ray diffraction (XRD) phase analysis. Besides macroscopic standard tensile tests, RA evolution under tensile loading is investigated by in situ XRD using synchrotron and laboratory methods. As a result of different quenching temperatures, the two conditions considered are characterized by different RA contents and morphologies, resulting in different strain hardening behaviors as well as strength and ductility values under tensile loading. In situ synchrotron measurements show differences in the transformation kinetics being rationalized by the different morphologies of the RA. Eventually, the evolution of the phase specific stresses can be explained by the well-known Masing model

Sliding wear resistance and residual stresses of parts repaired by laser metal deposition

International audienceLarge temperature gradients inherent to additive manufacturing (AM) processes induce large residual stress (RS) in the final part. Because RS can influence the tribological properties, this study focuses on the relationship between wear sliding properties and RS in IN718 coatings. Such coatings were deposited with a Laser Metal Deposition (LMD) machine using to two different scanning strategies. The wear resistance and RS state of two types of samples were investigated after surface milling. RS were measured before and after tests on a reciprocating sliding test apparatus. Two different X-ray diffraction techniques were employed to measure the surface and subsurface state RS: Laboratory Energy Dispersive X-ray Diffraction (LEDXD) and Synchrotron X-ray Energy Dispersive Diffraction (SXEDD). Due to the milling process, coatings show similar depth distributions of RS from 22 µm to 92 µm depth, but exhibit different magnitudes depending on the scanning strategy used. Reciprocating sliding wear tests induced high compressive residual stresses that erased the initial RS state. However, a similar wear behavior was observed in the two samples, which possess similar texture and microstructure. This demonstrates that the influence of RS on wear resistance is a second-order effect. Nevertheless, it was observed that RS can still impact the wear performance at the early testing stages of the repaired parts

Predicting Flow Stress Behavior of an AA7075 Alloy Using Machine Learning Methods

The present work focuses on the prediction of the hot deformation behavior of thermo-mechanically processed precipitation hardenable aluminum alloy AA7075. The data considered focus on a novel hot forming process at different tool temperatures ranging from 24∘C to 350∘C to set different cooling rates after solution heat-treatment. Isothermal uniaxial tensile tests in the temperature range of 200∘C to 400∘C and at strain rates ranging from 0.001 s−1 to 0.1 s−1 were carried out on four different material conditions. The present paper mainly focuses on a comparative study of modeling techniques based on Machine Learning (ML) and the Zerilli–Armstrong model (Z–A) as reference. Related work focuses on predicting single data points of the curves that the model was trained on. Due to the way data were split with respect to training and testing data, it is possible to predict entire stress–strain curves. The model allows to decrease the number of required laboratory experiments, eventually saving costs and time in future experiments. While all investigated ML methods showed a higher performance than the Z–A model, the extreme Gradient Boosting model (XGB) showed superior results, i.e., the highest error reduction of 91% with respect to the Mean Squared Error