Innovation Plasturgie Composites

Abstract Two modern trends in insurance are data-intensive underwriting and behaviour-based insurance. Data-intensive underwriting means that insurers analyse more data for estimating the claim cost of a consumer and for determining the premium based on that estimation. Insurers also offer behaviour-based insurance. For example, some car insurers use artificial intelligence (AI) to follow the driving behaviour of an individual consumer in real time and decide whether to offer that consumer a discount. In this paper, we report on a survey of the Dutch population ( N = 999) in which we asked people’s opinions about examples of data-intensive underwriting and behaviour-based insurance. The main results include: (i) If survey respondents find an insurance practice unfair, they also find the practice unacceptable. (ii) Respondents find almost all modern insurance practices that we described unfair. (iii) Respondents find practices for which they can influence the premium fairer. (iv) If respondents find a certain consumer characteristic illogical for basing the premium on, then respondents find using the characteristic unfair. (v) Respondents find it unfair if an insurer offers an insurance product only to a specific group. (vi) Respondents find it unfair if an insurance practice leads to the poor paying more. We also reflect on the policy implications of the findings.

Abstract Conformal cooling channels for injection moulds play a vital role in improving the efficiency and quality of the moulding processes by enabling more uniform heat removal. This study develops an integrated framework for optimising such cooling channels by topology optimisation, addressing key challenges in heat transfer under turbulent flow conditions. A scaling strategy is proposed to approximate high velocity turbulent heat transfer using a lower velocity laminar flow model. The scaling strategy overcomes the limitations of traditional Darcy potential flow models by incorporating inertial effects, which are critical for designing complex cooling layouts. Verification against high-fidelity Reynolds-Averaged Navier–Stokes simulations confirms the effectiveness of the scaling model in delivering reliable and physically accurate optimisation results. The optimisation process explores multiple performance and manufacturability constraints. Pressure drop constraints reveal the trade-offs between cooling efficiency and fluid resistance. Temperature uniformity constraints significantly reduce surface temperature deviations, particularly in critical regions such as corners and protrusions. Length scale constraints ensure manufacturability by regulating feature sizes, balancing structural simplicity with thermal performance. By combining these innovations, the proposed methodology establishes a robust foundation for designing advanced conformal cooling channels, offering practical solutions for thermal management in industrial applications.

Many composite manufacturing processes employ the consolidation of pre-impregnated preforms. However, in order to obtain adequate performance of the formed part, intimate contact and molecular diffusion across the different composites’ preform layers must be ensured. The latter takes place as soon as the intimate contact occurs and the temperature remains high enough during the molecular reptation characteristic time. The former, in turn, depends on the applied compression force, the temperature and the composite rheology, which, during the processing, induce the flow of asperities, promoting the intimate contact. Thus, the initial roughness and its evolution during the process, become critical factors in the composite consolidation. Processing optimization and control are needed for an adequate model, enabling it to infer the consolidation degree from the material and process features. The parameters associated with the process are easily identifiable and measurable (e.g., temperature, compression force, process time, ⋯). The ones concerning the materials are also accessible; however, describing the surface roughness remains an issue. Usual statistical descriptors are too poor and, moreover, they are too far from the involved physics. The present paper focuses on the use of advanced descriptors out-performing usual statistical descriptors, in particular those based on the use of homology persistence (at the heart of the so-called topological data analysis—TDA), and their connection with fractional Brownian surfaces. The latter constitutes a performance surface generator able to represent the surface evolution all along the consolidation process, as the present paper emphasizes.

Additive manufacturing (AM) is widely used for tooling applications: Complex conformal cooling channels are designed to increase and homogenize the cooling rate of a mold insert. The list of available steel grades in laser powder bed fusion is currently very limited and does not satisfy highly mechanically demanding applications; hard stainless steel metals printable without cracking are lacking. Herein, the influence of the particle size distribution and atomization process on the printability of a new tooling steel tailored for AM (L40) is observed on an EOS M290 printer based on microstructural observations and mechanical testing. Several batches with different powder size distributions of either gas‐ or water‐atomized powder are processed and studied. The aim of using water‐atomized powder is to reduce both the environmental footprint and the cost of the feed material. Dense built parts (other 99% density) are achieved using water‐ and gas‐atomized powders. The hardness is in all cases superior to 50 HRC, which is above the standard requirement for injection molding applications. The ductility of gas‐atomized parts is four times higher than that measured on heat‐treated maraging steel. Water‐atomized samples reach a ductility similar to that of maraging steel.

Arginase is involved in a wide range of pathologies including cardiovascular diseases and infectious diseases whilst it is also a promising target to improve cancer immunotherapy. To date, only a limited number of inhibitors of arginase have been reported. Natural polyphenols, among them piceatannol, are moderate inhibitors of arginase. Herein, we report our efforts to investigate catechol binding by quantum chemistry and generate analogues of piceatannol. In this work, we synthesized a novel series of amino-polyphenols which were then evaluated as arginase inhibitors. Their structure-activity relationships were elucidated by deep quantum chemistry modelling. 4-((3,4-Dihydroxybenzyl)amino)benzene-1,2-diol 3t displays a mixed inhibition activity on bovine and human arginase I with IC50 (K i) values of 76 (82) μM and 89 μM, respectively.

Omni-directional inductive wireless power transfer to a movable target inside a half-meter cubic emitter box is studied. The innovative point is to use 3D Molded Interconnect Device (3D-MID) coils at the surface of the target with a 3D shape. Transmission of energy at 6.78 MHz is performed experimentally and characterized in terms of power and efficiency. The 3D design of the coils allows maintaining power when the receiver target is translated and rotated inside the emitter box (that is to say allows compensating to a certain extent the misalignment between the emitters and the receivers). A comparison with a simplified model is performed. Lastly, the potential of this technology is discussed.

Sleep quality in people with cystic fibrosis (CF) is known to be poor, whereas participating in regular physical activity is associated with less decline in lung function (forced expiratory volume in 1 sec [FEV1]). The relationship between sleep quality and physical activity in people with CF is unknown. Secondary analysis of sleep and activity data collected via actigraphy. Adults with CF in stable health, participating in a study of physical activity (including assessment of exercise capacity), completed 7 d of activity and sleep assessment (SenseWear Armband [SWA]; BodyMedia). Sleep characteristics were derived from accelerometer positional data and registration of sleep state by the SWA, determined by energy expenditure. Sleep and activity data were available for 47 participants [n = 28 male; mean ± standard deviation age = 29 ± 8 yr; median (IQR) FEV1 = 60 (50, 82) % predicted]. More fragmented sleep was associated with poorer exercise capacity (rs = -0.303, P = .04), less time spent in moderate-vigorous physical activity (rs = -0.337, P = .020), and poorer FEV1 (rs = -0.344, P = .018). Regression analysis showed that less fragmented sleep was an independent predictor of more total daily activity time (β = -1.0, standard error [SE] of β = .4, P = .02) and trended toward significance for more moderate-vigorous physical activity (β = -.3, SE of β = -.26, P = .08). Greater total sleep time and sleep efficiency were related to better exercise capacity and lung function (P < .05). This secondary analysis demonstrated a modest relationship between sleep parameters and physical activity and exercise capacity in adults with CF. Future studies of interventions to promote physical activity participation in this group should consider the relationship between sleep and activity performance.

Geometrical and appearance quality requirements set the limits of the current\nindustrial performance in injection molding. To guarantee the product's\nquality, it is necessary to adjust the process settings in a closed loop. Those\nadjustments cannot rely on the final quality because a part takes days to be\ngeometrically stable. Thus, the final part geometry must be predicted from\nmeasurements on hot parts. In this paper, we use recent success of Generative\nAdversarial Networks (GAN) with the pix2pix network architecture to predict the\nfinal part geometry, using only hot parts thermographic images, measured right\nafter production. Our dataset is really small, and the GAN learns to translate\nthermography to geometry. We firstly study prediction performances using\ndifferent image similarity comparison algorithms. Moreover, we introduce the\ninnovative use of Discrete Modal Decomposition (DMD) to analyze network\npredictions. The DMD is a geometrical parameterization technique using a modal\nspace projection to geometrically describe surfaces. We study GAN performances\nto retrieve geometrical parameterization of surfaces.\n