Spatio-temporal Analysis of Thermal Profiles in Extrusion-based Additive Manufacturing

Extrusion-based Additive Manufacturing (AM) processes have recently gained increasing attention in the scientific and industrial communities because of the wide range of processible materials (from thermoplastics to composite and biomaterials), printable volumes, and industrial applications. As for many other AM processes, the actual problems with process stability and repeatability are still limiting the industrial process adoption, as these problems can significantly impact on the final part quality. In this framework, a latest research trend aims at developing in-situ monitoring solutions for inline defect detection, in a zero-waste production perspective. Among the existing in-situ sensing techniques, many studies showed that in-situ thermography represents a viable solution to describe the temperature dynamic and validate the thermal models but very few approaches have been proposed to quantitively study the temperature evolution to quickly detect process instabilities. This paper presents a new approach to quickly analyse the temporal dynamic of temperature in the printed layer while providing a spatial mapping of the temperature homogeneities. Compared with previous methods, the current one has the main novelty feature of combining both the spatial and temporal signature in a synthetic mapping that allows to detect unstable or unusual problems. In order to show the effectiveness of the proposed solution, a real case study of Big Area Additive Manufacturing (BAAM) for composite materials is considered. The study shows that the provided method can clearly enhance defect detection and represents a new solution for detecting anomalous areas where thermal profiles behave differently with respect to the surrounding areas. The same methodology underlined the thermal evolution complexity in the BAAM case study and enabled the detection of local flaws, i.e., hot and cold spots

Robust in-line qualification of lattice structures manufactured via laser powder bed fusion

The shape complexity enabled by AM would impose new part inspection systems (e.g., x-ray computed tomography), which translate into qualification time and costs that may be not affordable. However, the layerwise nature of the process potentially allows anticipating qualification tasks in-line and in-process, leading to a quick detection of defects since their onset stage. This opportunity is particularly attractive in the presence of lattice structures, whose industrial adoption has considerably increased thanks to AM. This paper presents a novel methodology to model the quality of lattice structures at unit cell level while the part is being built, using high resolutions images of the powder bed for in-line geometry reconstruction and identification of deviations from the nominal shape. The methodology is designed to translate complex 3D shapes into 1D deviation profiles that capture the “geometrical signature” of the cell together with the reconstruction uncertainty

A techno-economic approach for decision-making in metal additive manufacturing: metal extrusion versus single and multiple laser powder bed fusion

This work presents a decision-making methodology that allows the merging of quantitative and qualitative decision variables for selecting the optimal metal Additive Manufacturing (AM) technology. The approach is applied on two competing technologies in the field of metal AM industry, i.e., the metal extrusion AM process (metal FFF) and the Laser Powder Bed Fusion process (LPBF) with single and multiple lasers, which represent the benchmark solution currently on the market. A comprehensive techno-economical comparison is presented where the two processes are analysed in terms of process capabilities (quality, easiness of use, setup time, range of possible materials, etc.) and costs, considering two different production scenarios and different parts’ geometries. In the first scenario, the AM system is assumed to be dedicated to one single part production while in this second scenario, the AM system is assumed to be saturated, as devoted to producing a wide mix of part types. For each scenario, two different part types made of 17–4 PH stainless steel are considered as a reference to investigate the effect of shape complexity, part size and production times to select the best technology when metal FFF and LPBF must be considered. The first part type refers to an extrusion die, to represent typical shapes of interest in the tooling industry, while the second part type is an impeller which can be used in many different industrial sectors, ranging from oil and gas to aerospace. In order to include quantitative and qualitative criteria, a decision-making model based on Analytic Hierarchy Process (AHP) is proposed as the enabler tool for decision making. The proposed approach allows to determine the most effective solution depending on the different production configurations and part types and can be used as a guideline and extended to include other technologies in the field of metal AM. On the other side, the critical discussion of the criteria selected, and the results achieved allow to highlight the pros and cons of the competing technologies, thus defining the existing limits to define directions for future research

Preliminary tests on PEG-based thermoresponsive polymers for the production of 3D bioprinted constructs

In the last years, the growing demand for tissues and organs led to the development of novel techniques, such as 3D bioprinting. This technique proved to be promising for both patient-specific and custom-made applications when using autologous cells, and for the creation of standardized models that in the future could be used for instance for high-throughput drug screening. Within this context, the formulation of bioinks that could provide reliable, reproducible, and replicable structures with good mechanical properties and high biocompatibility is a crucial challenge. In this work, the use of a thermoresponsive PEG-based formulation was investigated as a bioink, allowing its use for 4D bioprinting applications triggered by thermal changes. First, the polymer was synthesized by reversible addition-fragmentation chain transfer polymerization (RAFT), which allows for optimal control over the final properties of the polymer. Then, the printability for extrusion-based bioprinting of this formulation was assessed through in-situ imaging. Finally, the use of this polymer as bioink was tested by encapsulation of endothelial cells and evaluating cell distribution within the construct

Fast hierarchical fusion model based on least squares B-splines approximation

With manufacturing shifting from traditional products to high value products, the complexity and accuracy of the products are increasing in order to reduce energy costs, create friendly environment and better health care. Structured surfaces, freeform surfaces, and other functional engineering surfaces are becoming the core part of high value manufacturing products. However, measurement of these surfaces is becoming very difficult due to instrumental limitations including measurement range, speed, resolution and accuracy. Multi-instruments/sensors measurement are now being developed for freeform and structured surface assessment, which requires the fusion of the data into a unified system to achieve larger dynamic measurements with greater reliability. This paper discusses the process of combining data from several information sources (instruments/sensors) into a common representational format and the surface topography can be reconstructed using Gaussian processes and B-spline techniques. In this paper the Gaussian process model is extended in order to take into account the uncertainty propagation and a new data fusion model based on least squares B-splines that drastically reduce the computational time are presented. The results are validated by two for freeform surface measurements

In-situ monitoring of defects in extrusion-based bioprinting processes using visible light imaging

Tissue engineering techniques are central for the development of biomedical scaffolds, which are primarily employed in the biofabrication of various artificial human tissue and organ models. Bioprinting is a new technique of creating tissue constructs that can sustain cell proliferation. The development of printing techniques proceeds together with the development of the biomaterials to be printed, which is why studying the printability of these specific biomaterials must be explored. An appropriate hydrogel used as bioink should have numerous rheological, mechanical, and biological properties for producing appropriate tissue constructs. However, reaching the right trade-off between a desirable bioactivity and high printability is challenging, and despite numerous optimization studies for different materials, printing defects often occur during printing. Herein, methods are proposed to automatically identify these drifting processes in commonly used geometries and how they affected subsequent layers, as well as printing defects within each layer. Several structures were printed with standard commercial bioink as proof of concept. The constructs were analyzed using optical images from a coaxial camera. The images were then digitally processed to get geometrical data from which patterns of defectology to be monitored were derived. This automation should decrease the time in post-processing characterization of constructs and should provide a standardized tool to compare different bioinks

From Profile to Surface Monitoring: SPC for Cylindrical Surfaces Via Gaussian Processes

Quality of machined products is often related to the shapes of surfaces that are constrained by geometric tolerances. In this case, statistical quality monitoring should be used to quickly detect unwanted deviations from the nominal pattern. The majority of the literature has focused on statistical profile monitoring, while there is little research on surface monitoring. This paper faces the challenging task of moving from profile to surface monitoring. To this aim, different parametric approaches and control-charting procedures are presented and compared with reference to a real case study dealing with cylindrical surfaces obtained by lathe turning. In particular, a novel method presented in this paper consists of modeling the manufactured surface via Gaussian processes models and monitoring the deviations of the actual surface from the target pattern estimated in phase I. Regardless of the specific case study in this paper, the proposed approach is general and can be extended to deal with different kinds of surfaces or profiles

Improving blood pressure control through pharmacist interventions: a meta-analysis of randomized controlled trials.

BACKGROUND: Control of blood pressure (BP) remains a major challenge in primary care. Innovative interventions to improve BP control are therefore needed. By updating and combining data from 2 previous systematic reviews, we assess the effect of pharmacist interventions on BP and identify potential determinants of heterogeneity. METHODS AND RESULTS: Randomized controlled trials (RCTs) assessing the effect of pharmacist interventions on BP among outpatients with or without diabetes were identified from MEDLINE, EMBASE, CINAHL, and CENTRAL databases. Weighted mean differences in BP were estimated using random effect models. Prediction intervals (PI) were computed to better express uncertainties in the effect estimates. Thirty-nine RCTs were included with 14 224 patients. Pharmacist interventions mainly included patient education, feedback to physician, and medication management. Compared with usual care, pharmacist interventions showed greater reduction in systolic BP (-7.6 mm Hg, 95% CI: -9.0 to -6.3; I(2)=67%) and diastolic BP (-3.9 mm Hg, 95% CI: -5.1 to -2.8; I(2)=83%). The 95% PI ranged from -13.9 to -1.4 mm Hg for systolic BP and from -9.9 to +2.0 mm Hg for diastolic BP. The effect tended to be larger if the intervention was led by the pharmacist and was done at least monthly. CONCLUSIONS: Pharmacist interventions - alone or in collaboration with other healthcare professionals - improved BP management. Nevertheless, pharmacist interventions had differential effects on BP, from very large to modest or no effect; and determinants of heterogeneity could not be identified. Determining the most efficient, cost-effective, and least time-consuming intervention should be addressed with further research

new morphologic variants of the hand motor cortex as seen with mr imaging in a large study population

BACKGROUND AND PURPOSE: The hand motor cortex (HMC) has been classically described as having an omega or epsilon shape in axial-plane images obtained with CT and MR imaging. The aim of this study was to use MR imaging and Talairach normalization in a large sample population that was homogeneous for age and handedness to evaluate in a sex model a new classification with 5 morphologic variants of the HMC in the axial plane (omega, medially asymmetric epsilon, epsilon, laterally asymmetric epsilon, and null). MATERIALS AND METHODS: Structural brain MR images were obtained from 257 right-handed healthy subjects (143 men and 114 women; mean age, 23.1 ± 1.1 years) via a Talairach space transformed 3D magnetization-prepared rapid acquisition of gradient echo sequence. The frequencies of the different HMC variants were reported for hemisphere and sex. RESULTS: The new variants of the HMC (medially asymmetric epsilon, laterally asymmetric epsilon, and null) were observed in 2.9%, 7.0%, and 1.8% of the hemispheres, respectively. Statistically significant sex differences were observed: The epsilon variant was twice as frequent in men, and an interhemispheric concordance for morphologic variants was observed only for women. CONCLUSION: The large study population permitted the description of a new morphologic classification that included 3 new variants of the HMC. This new morphologic classification should facilitate the identification of the precentral gyrus in subsequent studies and in everyday practice