Browse
Submit Data
Databases
API
Help

Dataset Information

5 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

A high-fidelity simulation of double-sided incremental forming: Improving the accuracy by incorporating the effects of machine compliance.

ABSTRACT: Double-Sided Incremental Forming (DSIF) is a technology for the rapid, flexible manufacturing of sheet metal parts. DSIF is highly nonlinear, requiring the use of complex finite element (FE) models to optimize and control the process in order to meet geometric accuracy and sheet thinning design criteria. Current FE models do not properly take into account the effects of machine compliance, which reduces their accuracy and hinders their use for optimization and control. The aim of this work is to create a greatly improved FE model of DSIF by taking a novel approach of modeling the aggregate effects of machine and tool compliance. The accuracy of the new model was extensively validated using the local geometry, thickness distribution, principal strains, and forming forces from a funnel experiment. The validated model was used to accurately predict the spatial distribution and time-histories of the equivalent plastic strain, von Mises equivalent stress, stress triaxiality, and Lode angle parameter across and along the sheet metal. The stress state was found to rapidly change through the sheet thickness, from highly compressive between the tools and the sheet, to a mixture of generalized shear and plane strain elsewhere. Moreover, the compressive regions between the two DSIF tools created a constrained deformation zone, which likely aids in prolonging the onset of excessive thinning. This improved FE model can now be used to quantitatively characterize the nonlinear local deformation mechanisms inherent to the DSIF process, thereby providing a solid foundation for future advances in process control.

SUBMITTER: Moser N

PROVIDER: S-EPMC8647678 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

Hybrid simulation for knee arthrocentesis: improving fidelity in procedures training.

Project description:BackgroundProcedures form a core competency for internists, yet many do not master these skills during residency. Simulation can help fill this gap, but many curricula focus on technical skills, and overlook communication skills necessary to perform procedures proficiently. Hybrid simulation (HS) is a novel way to teach and assess procedural skills in an integrated, contextually-based way.AimTo create a HS model for teaching arthrocentesis to internal medicine residents.SettingInternal medicine residency program at the University of Toronto.ParticipantsTwenty four second-year internal medicine residents.Program descriptionResidents were introduced to HS, given practice time with feedback from standardized patients (SPs) and faculty, and assessed individually using a different scenario and SP. Physicians scored overall performance using a 6-point procedural skills measure, and both physicians and SPs scored communication using a 5-point communication skills measure.Program evaluationRealism was highly rated by residents (4.13/5.00), SPs (4.00) and physicians (4.33), and was perceived to enhance learning. Residents' procedural skills were rated as 4.21/6.00 (3.00 - 5.00; ICC = 0.77, [0.53 - 0.92]), comparable to an experienced post-graduate year (PGY) 2-3; and all but one resident was considered competent.DiscussionHS facilitates simultaneous acquisition of technical and communication skills. Future research should examine whether HS improves transfer of skills to the clinical setting.

| S-EPMC3631077 | biostudies-literature

Improving the accuracy of medical diagnosis with causal machine learning.

Project description:Machine learning promises to revolutionize clinical decision making and diagnosis. In medical diagnosis a doctor aims to explain a patient's symptoms by determining the diseases causing them. However, existing machine learning approaches to diagnosis are purely associative, identifying diseases that are strongly correlated with a patients symptoms. We show that this inability to disentangle correlation from causation can result in sub-optimal or dangerous diagnoses. To overcome this, we reformulate diagnosis as a counterfactual inference task and derive counterfactual diagnostic algorithms. We compare our counterfactual algorithms to the standard associative algorithm and 44 doctors using a test set of clinical vignettes. While the associative algorithm achieves an accuracy placing in the top 48% of doctors in our cohort, our counterfactual algorithm places in the top 25% of doctors, achieving expert clinical accuracy. Our results show that causal reasoning is a vital missing ingredient for applying machine learning to medical diagnosis.

| S-EPMC7419549 | biostudies-literature

Improving Learners' Comfort With Cesarean Sections Through the Use of High-Fidelity, Low-Cost Simulation.

Project description:IntroductionSeveral studies have demonstrated effective simulation-based training for laparoscopic procedures in OB/GYN, but limited simulation curricula exist for abdominal procedures, particularly cesarean sections (CSs).MethodsWe developed a high-fidelity modification of an existing CS model costing about $25 and incorporated it into a 90-minute teaching simulation event for medical students and OB/GYN residents in a single academic program. The simulation included a structured curriculum, pre-/postsimulation surveys, a surgical instrument review, a mannequin with the CS model containing a fetus in breech position, and live video streaming. Our surveys assessed participants' comfort with the procedure and its related components on a 5-point scale, and we used a paired t test to analyze our data.ResultsTwenty-two learners (eight third-year medical students, one fourth-year medical student, three first-year residents, four second-year residents, one third-year resident, four fourth-year residents, and one unknown level) participated in this simulation. We found a statistically significant improvement in perceived CS instrument knowledge, suturing skills, and satisfaction with the model among all participants. Only third-year medical students had a statistically significant increase in comfort level in performing a CS after the simulation. Video streaming engaged a wider audience, but poor lighting and audio limited its efficacy.DiscussionUsing this simulation model at the end of medical school or early in residency may have the greatest positive effect on resident comfort with CSs. This low-cost and versatile model can be used across educational settings, including OB/GYN interest group activities, intern boot camp, and interprofessional emergency drills.

| S-EPMC7062555 | biostudies-literature

Analysis of Incremental Sheet Forming of Aluminum Alloy.

Project description:Recent developments in incremental sheet forming have resulted in the creation of novel manufacturing processes that are highly adaptable and could bring significant economic benefits for advanced technologies and low-volume production. In this manuscript, the following variables were examined: the variation in the deformation forces for a part with a pyramidal trunk shape; the variation in the deformations and thinning of the Al 3003 material during the incremental forming process; and the variation in the accuracy of the incrementally formed part and the quality of the surfaces (surface roughness). The components of the forces in the incremental forming have increasing values from the beginning of the process to the maximum value due to the hardening process. The TiN-coated tool ensures lower values of the forming components. Due to the kinematics of the forming process, deviations, especially in shape, from the part in the drawing are observed, which are shown by the radius of curvature of the side wall of the part, the appearance of a radius of connection between the wall and the bottom of the part, as well as dimensional deviations that are expressed by the variation in the forming depth. Concerning the smoothness of the surfaces, it was observed that the best roughness results were obtained in the case of the TiN-coated tool.

| S-EPMC10573285 | biostudies-literature

Incorporating genome-wide association into eco-physiological simulation to identify markers for improving rice yields.

Project description:We explored the use of the eco-physiological crop model GECROS to identify markers for improved rice yield under well-watered (control) and water deficit conditions. Eight model parameters were measured from the control in one season for 267 indica genotypes. The model accounted for 58% of yield variation among genotypes under control and 40% under water deficit conditions. Using 213 randomly selected genotypes as the training set, 90 single nucleotide polymorphism (SNP) loci were identified using a genome-wide association study (GWAS), explaining 42-77% of crop model parameter variation. SNP-based parameter values estimated from the additive loci effects were fed into the model. For the training set, the SNP-based model accounted for 37% (control) and 29% (water deficit) of yield variation, less than the 78% explained by a statistical genomic prediction (GP) model for the control treatment. Both models failed in predicting yields of the 54 testing genotypes. However, compared with the GP model, the SNP-based crop model was advantageous when simulating yields under either control or water stress conditions in an independent season. Crop model sensitivity analysis ranked the SNP loci for their relative importance in accounting for yield variation, and the rank differed greatly between control and water deficit environments. Crop models have the potential to use single-environment information for predicting phenotypes under different environments.

| S-EPMC6487590 | biostudies-literature

Author Correction: Improving the accuracy of medical diagnosis with causal machine learning.

Project description: Not available

| S-EPMC8012648 | biostudies-literature

KAML: improving genomic prediction accuracy of complex traits using machine learning determined parameters.

Project description:Advances in high-throughput sequencing technologies have reduced the cost of genotyping dramatically and led to genomic prediction being widely used in animal and plant breeding, and increasingly in human genetics. Inspired by the efficient computing of linear mixed model and the accurate prediction of Bayesian methods, we propose a machine learning-based method incorporating cross-validation, multiple regression, grid search, and bisection algorithms named KAML that aims to combine the advantages of prediction accuracy with computing efficiency. KAML exhibits higher prediction accuracy than existing methods, and it is available at https://github.com/YinLiLin/KAML.

| S-EPMC7386246 | biostudies-literature

Improving succinylation prediction accuracy by incorporating the secondary structure via helix, strand and coil, and evolutionary information from profile bigrams.

Project description:Post-translational modification refers to the biological mechanism involved in the enzymatic modification of proteins after being translated in the ribosome. This mechanism comprises a wide range of structural modifications, which bring dramatic variations to the biological function of proteins. One of the recently discovered modifications is succinylation. Although succinylation can be detected through mass spectrometry, its current experimental detection turns out to be a timely process unable to meet the exponential growth of sequenced proteins. Therefore, the implementation of fast and accurate computational methods has emerged as a feasible solution. This paper proposes a novel classification approach, which effectively incorporates the secondary structure and evolutionary information of proteins through profile bigrams for succinylation prediction. The proposed predictor, abbreviated as SSEvol-Suc, made use of the above features for training an AdaBoost classifier and consequently predicting succinylated lysine residues. When SSEvol-Suc was compared with four benchmark predictors, it outperformed them in metrics such as sensitivity (0.909), accuracy (0.875) and Matthews correlation coefficient (0.75).

| S-EPMC5809022 | biostudies-literature

Improving children's and their visitors' hand hygiene compliance.

Project description:BackgroundNumerous interventions have tried to improve healthcare workers' hand hygiene compliance. However, little attention has been paid to children's and their visitors' compliance.AimTo test whether interactive educational interventions increase children's and visitors' compliance with hand hygiene.MethodsThis was a cluster randomised study of hand hygiene compliance before and after the introduction of educational interventions. Observations were compared for different moments of hygiene and times of the day. Qualitative data in the form of questionnaire-based structured interviews were obtained.FindingsHand hygiene compliance increased by 24.4% (P < 0.001) following the educational interventions, with children's compliance reaching 40.8% and visitors' being 50.8%. Compliance varied depending on which of the five moments of hygiene was observed (P < 0.001), with the highest compliance being 'after body fluid exposure' (72.7%). Responses from questionnaires showed educational interventions raised awareness of the importance of hand hygiene (69%, 57%) compared to those who had not experienced the educational intervention (50%).ConclusionEducational interventions may result in a significant increase in children's and visitors' hand hygiene (P < 0.001).

| S-EPMC7754805 | biostudies-literature

Improving compliance with requirements on junior doctors' hours.

Project description:ProblemCompliance with UK regulations on junior doctors' working hours cannot be achieved by manipulating rotas that maintain existing tiers of cover and work practices. More radical solutions are needed.DesignAudit of change.SettingPaediatric night rota in large children's hospital.Key measures for improvementCompliance with regulations on working hours assessed by diary cards; workload assessed by staff attendance on wards; patient safety assessed through critical incident reports.Strategies for changeDevelopment of new staff roles, followed by change from a partial shift rota comprising 11 doctors and one senior nurse, to a full shift night team comprising three middle grade doctors and two senior nurses.Effects of changeCompliance with regulations on working hours increased from 33% to 77%. Workload changed little and was well within the capacity of the new night team. The effect on patient care and on medical staff requires further evaluation.Lessons learntReduction of junior doctors' working hours requires changes to roles, processes, and practices throughout the organisation.

| S-EPMC1126659 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data