Missing data in clinical trials can have a major effect on the validity of the inferences that can be drawn from the trial. This article reviews methods for preventing missing data and, failing that, ...dealing with data that are missing.
Background
Missing data have seriously compromised inferences from clinical trials, yet the topic has received little attention in the clinical-trial community.
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Existing regulatory guidances
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on the design, conduct, and analysis of clinical trials have little specific advice on how to address the problem of missing data. A recent National Research Council (NRC) report
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on the topic seeks to address this gap, and this article summarizes some of the main findings and recommendations of that report. The authors of this article served on the panel that prepared the report.
Missing data have seriously compromised inferences from clinical trials.
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For example, . . .
Modeling of catastrophic disruption requires understanding the processes of dynamic failure and fragmentation. This paper summarizes current mechanisms and models for dynamic failure, strength, and ...fragmentation, reviewing these from a mechanics perspective and with an emphasis on making links to the developing advances in these areas in the engineering and computational mechanics communities. We describe dynamic failure processes, examine size and rate effects, articulate the scaling concepts that arise naturally from these processes, and examine the influences of these processes on effective strength and fragmentation.
•Summarizes mechanisms for dynamic failure, strength, and fragmentation, reviewing these from a mechanics perspective.•Emphasis on making links to the developing advances in these areas in the engineering and computational mechanics communities.
•The compressive strength of B4C and B4C-TiB2 were acquired across strain rates ranging from 10−4 s−1 to 1000 s−1.•Normalized ballistic efficiency was used to compare depth of penetration tests with ...AP M2. projectiles on B4C variants.•Larger impact crater in pure B4C led to lower depth of penetration.•Degraded properties of B4C-TiB2 composite attributed to silicon impurities.
A comparative study on the microstructure, rate-dependent compressive behavior, and ballistic performance of commercially available pressureless sintered boron carbide-titanium diboride (material Z) and hot-pressed boron carbide (material S) was conducted. Under quasi-static compression at rates of 1.4 to 1.6 x 10-4 s-1, the strength was found to be 3.07 ± 0.11 GPa for material Z and 4.72 ± 0.14 GPa for material S. At dynamic strain rates ranging from 185 to 1152 s-1, the compressive strength ranged from 3.56 to 4.07 GPa for material Z and 5.24 to 5.97 GPa for material S. Depth of penetration testing was performed using 7.62 mm AP M2 projectiles. The normalized ballistic efficiency of the two materials were found to be comparable at 932 m/s, while material S was superior to material Z at an impact velocity of 1078 m/s. Based on post-mortem SEM analysis of ballistic tile fragments, the inferior mechanical properties and ballistic performance of material Z are attributed to an uneven distribution of silicon impurities and a significant level of porosity.
•Ab-initio informed design of a CoCrFeNi-based HEAs with simultaneous addition of Mo and Nb.•Additive manufacturing of a HEA via cold-spraying develops recrystallized nano-grains.•Excellent ...mechanical properties under compression achieving 1745 MPa and 2622 MPa of yield and ultimate stress.
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We present a combined experimental and computational investigation of the mechanical properties of a CoCrFe0.75NiMo0.3Nb0.125 (composition in molar ratio) high-entropy alloy additively manufactured via cold spray. We find that the sprayed alloy exhibits extraordinary mechanical properties under compression, reaching yield stress of ~1745 MPa, ultimate stress of ~2622 MPa, and a maximum strain at failure of ~9%. These exceptional mechanical properties are the result of four independent hardening mechanisms. First, using ab initio simulations, we find that non-equiatomic compositions increase the enthalpy of mixing, promoting better solubility of solute Mo and Nb atoms while simultaneously preserving the electronegativity of the base alloy. The higher solubility results in solid-solution hardening and nanosized precipitate formation, promoting additional hardening. These effects are confirmed in the experimental characterization of the manufactured HEA, where nanosized precipitates of ~226 ± 65 nm in size are identified. Additional hardening effects are associated with the manufacturing process, where the high-velocity impacts of the microparticles promote dynamic recrystallization through dislocation emission and grain refinement. To understand the dynamic recrystallization of particles, high-velocity impact simulations using molecular dynamics are performed. We find that when particles reach a critical impact velocity ( ~600–800 m ⋅ s−1), the dislocation density reaches a maximum, and grain refinement is maximized. The decaying wave pressures developed during the impact generate gradual refinement levels, leading to heterogeneous microstructures combining nano and micro grains, which was later confirmed experimentally using electron backscatter diffraction. These subtle atomic and microstructural features result in outstanding experimentally evaluated yield and ultimate stresses compared to other high-entropy alloys with similar compositions.
•Rate-dependent behavior of a Mg-Li-Al alloy with high concentrations of Li (~ 35 wt%) and Al (~ 20 wt%) is investigated.•The alloy exhibits superior micro-hardness (1.63 ± 0.08 GPa), which is> 1.5x ...higher than all reported Mg-Li-based alloys.•Uniaxial compression tests show rate-insensitivity, high peak strength (699.4±4.0MPa), and quasi-brittle material failure.•Peeling failure mechanism resulting from a dendritic “fishbone” microstructure promotes a macro-scale shear-mode fracture.
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A study on the microstructure and composition, micro-hardness and strain-rate-dependent compressive behaviors, and the associated failure mechanisms of an ultra-light-weight Mg-Li-Al alloy were conducted. X-ray diffraction and X-ray photoelectron spectroscopy showed a multi-phase material with ~35 wt% Li and ~20 wt% Al, and a dendritic “fishbone” microstructure resulted from the high percentage of both Li and Al. Micro-indentation measurements showed a superior hardness (1.63 ± 0.08 GPa) that is> 1.5x higher than other Mg-Li-Al alloys reported in the literature, with a low density (~1.68 g/cm3) comparable to Mg alloys. Strain-rate-dependent uniaxial compression experiments demonstrated no strain-rate-sensitivity in the peak strength (699.4 ± 74.0 MPa) at strain rates between 10−5 and 103 s−1. High-speed imaging revealed a shear-mode brittle fracture under both quasi-static and dynamic conditions, with an additional splitting crack mechanism observed under dynamic loading. Crack propagation speeds demonstrated a positive correlation with strain rate from ~480 m/s at ~100 s−1 to ~1000 m/s at ~2000 s−1. Post-mortem analysis showed that the “fishbone” structure with a peeling fracture mechanism appears to be the dominant site promoting shear failure across all strain rates.
Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and ...potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material’s behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes.
Abstract
This study presents a data-driven finite element-machine learning surrogate model for predicting the end-to-end full-field stress distribution and stress concentration around an ...arbitrary-shaped inclusion. This is important because the model’s capacity to handle large datasets, consider variations in size and shape, and accurately replicate stress fields makes it a valuable tool for studying how inclusion characteristics affect material performance. An automatized dataset generation method using finite element simulation is proposed, validated, and used for attaining a dataset with one thousand inclusion shapes motivated by experimental observations and their corresponding spatially-varying stress distributions. A U-Net-based convolutional neural network (CNN) is trained using the dataset, and its performance is evaluated through quantitative and qualitative comparisons. The dataset, consisting of these stress data arrays, is directly fed into the CNN model for training and evaluation. This approach bypasses the need for converting the stress data into image format, allowing for a more direct and efficient input representation for the CNN. The model was evaluated through a series of sensitivity analyses, focusing on the impact of dataset size and model resolution on accuracy and performance. The results demonstrated that increasing the dataset size significantly improved the model’s prediction accuracy, as indicated by the correlation values. Additionally, the investigation into the effect of model resolution revealed that higher resolutions led to better stress field predictions and reduced error. Overall, the surrogate model proved effective in accurately predicting the effective stress concentration in inclusions, showcasing its potential in practical applications requiring stress analysis such as structural engineering, material design, failure analysis, and multi-scale modeling.
Abstract This study investigates the application of machine learning models to predict time-evolving stress fields in complex three-dimensional structures trained with full-scale finite element ...simulation data. Two novel architectures, the multi-decoder CNN (MUDE-CNN) and the multiple encoder–decoder model with transfer learning (MTED-TL), were introduced to address the challenge of predicting the progressive and spatial evolutional of stress distributions around defects. The MUDE-CNN leveraged a shared encoder for simultaneous feature extraction and employed multiple decoders for distinct time frame predictions, while MTED-TL progressively transferred knowledge from one encoder–decoder block to another, thereby enhancing prediction accuracy through transfer learning. These models were evaluated to assess their accuracy, with a particular focus on predicting temporal stress fields around an additive manufacturing (AM)-induced isolated pore, as understanding such defects is crucial for assessing mechanical properties and structural integrity in materials and components fabricated via AM. The temporal model evaluation demonstrated MTED-TL’s consistent superiority over MUDE-CNN, owing to transfer learning’s advantageous initialization of weights and smooth loss curves. Furthermore, an autoregressive training framework was introduced to improve temporal predictions, consistently outperforming both MUDE-CNN and MTED-TL. By accurately predicting temporal stress fields around AM-induced defects, these models can enable real-time monitoring and proactive defect mitigation during the fabrication process. This capability ensures enhanced component quality and enhances the overall reliability of additively manufactured parts.
Mesh size dependency caused by strain localization is an ongoing problem in numerical simulations using the finite element method. In order to solve this problem, the concept of including the ...characteristic element length for regularization is used in the literature. The estimation of the characteristic element length is not a straightforward task since normally the characteristic element length differs from one element to another in the simulation and depends not only on element geometry but also on fracture plane orientation and material orientation. In this paper, an innovative method is proposed to estimate the characteristic element length which works on the orthogonal projection of elements on the fracture plane. The method is implemented in Abaqus/Explicit finite element solver and is verified using simple and more complex load cases such as tensile specimens, open hole specimens, and low-velocity impact. A good correlation between the numerical and experimental results in all of the studied cases was achieved and the proposed method proved to be effective in reducing mesh sensitivity. The use of the volumetric method from the literature for the simulation of open-hole tensile specimens led to more than 25% increase in the estimation of specimen strength while similar values of strength for different element aspect ratios were achieved with the proposed method.
This study compares the fatigue life of a 7475-T7351 aluminum alloy lower wall plate in an aircraft beam structure under alternating corrosion and fatigue conditions to universal fatigue life. It ...incorporates a corrosive environment and variable amplitude fatigue loads. The current study uses the “beach marking” technique and visual inspection to monitor crack propagation and evaluate the corrosive environment’s impact on fatigue life and damage tolerance. The experimental results indicate that during the fatigue crack initiation and penetration stages, the corrosion environment does not significantly impact the fatigue life of the beam structure because of the protection from uniform oxide films, epoxy primer, and sealants at joints. In the crack propagation stage, the corrosive environment speeds up crack growth compared to universal fatigue tests. Additionally, a “hysteresis effect” in alternating corrosion and fatigue tests shows the fatigue crack growth rate changing discontinuously, caused mainly by corrosion dissolving slip bands at the crack tip. Altogether, this study provides new insights into the influence of alternating corrosion and variable amplitude load on an aircraft beam structure’s fatigue life and damage tolerance.
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