Predicting Geometric Errors and Failures in Additive Manufacturing

Additive manufacturing is a process that has facilitated the cost effective production of complicated designs. Objects fabricated via additive manufacturing technologies often suffer from dimensional accuracy issues and other part specific problems such as thin part robustness, overhang geometries that may collapse, support structures that cannot be removed, engraved and embossed details that are indistinguishable. In this work we present an approach to predict the dimensional accuracy per vertex and per part. Furthermore, we provide a framework for estimating the probability that a model is fabricated correctly via an additive manufacturing technology for a specific application. This framework can be applied to several 3D printing technologies and applications. In the context of this paper, a thorough experimental evaluation is presented for binder jetting technology and applications.Comment: This version has been published in the Rapid Prototyping Journal (2023

Feature-based 3D Morphing based on Geometrically Constrained Sphere Mapping Optimization

Current trends in free form editing suggest the development of a new novel editing paradigm for CAD models beyond traditional CAD editing of mechanical parts. To this end we wish to develop accurate, robust and efficient 3D mesh deformation techniques such as 3D structural morphing. In this paper, we present a feature-based approach to 3D morphing of arbitrary genus-0 polyhedral objects that is appropriate for CAD editing. The technique is based on a sphere mapping process built on an optimization technique that uses a target function to maintain the correspondence among the initial polygons and the mapped ones while preserving topology and connectivity through a system of geometric constraints. Finally, we introduce a fully automated feature-based technique that matches surface areas (feature regions) with similar morphological characteristics between the two morphed objects and performs morphing according to this feature correspondence list. Alignment is obtained without user intervention and is based on pattern matching between the feature graphs of the two morphed objects

Bloodstream Infections in a COVID-19 Non-ICU Department: Microbial Epidemiology, Resistance Profiles and Comparative Analysis of Risk Factors and Patients’ Outcome

Background: Bloodstream infections (BSI) caused by highly resistant pathogens in non-ICU COVID-19 departments pose important challenges. Methods: We performed a comparative analysis of incidence and microbial epidemiology of BSI in COVID-19 vs. non-COVID-19, non-ICU departments between 1 September 2020-31 October 2021. Risk factors for BSI and its impact on outcome were evaluated by a case-control study which included COVID-19 patients with/without BSI. Results: Forty out of 1985 COVID-19 patients developed BSI. The mean monthly incidence/100 admissions was 2.015 in COVID-19 and 1.742 in non-COVID-19 departments. Enterococcus and Candida isolates predominated in the COVID-19 group (p < 0.001 and p = 0.018, respectively). All Acinetobacter baumannii isolates were carbapenem-resistant (CR). In the COVID-19 group, 33.3% of Klebsiella pneumoniae was CR, 50% of Escherichia coli produced ESBL and 19% of Enterococcus spp. were VRE vs. 74.5%, 26.1% and 8.8% in the non-COVID-19 group, respectively. BSI was associated with prior hospitalization (p = 0.003), >2 comorbidities (p < 0.001), central venous catheter (p = 0.015), severe SARS-CoV-2 pneumonia and lack of COVID-19 vaccination (p < 0.001). In the multivariate regression model also including age and multiple comorbidities, only BSI was significantly associated with adverse in-hospital outcome [OR (CI95%): 21.47 (3.86–119.21), p < 0.001]. Conclusions: BSI complicates unvaccinated patients with severe SARS-CoV-2 pneumonia and increases mortality. BSI pathogens and resistance profiles differ among COVID-19/non-COVID-19 departments, suggesting various routes of pathogen acquisition