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Parts fabricated via additive manufacturing (AM) methods are prone to experiencing high temperature gradients during manufacture resulting in internal residual stress formation. In the current study, a numerical model for predicting the temperature distribution and residual stress in Directed Energy Deposited (DED) Ti–6Al–4V parts is utilized for determining a relationship between local part temperature gradients with generated residual stress. Effects of time interval between successive layer deposits, as well as layer deposition itself, on the temperature gradient vector for the first and each layer is investigated. The numerical model is validated using thermographic measurements of Ti-6Al-4V specimens fabricated via Laser Engineered Net Shaping® (LENS), a blown-powder/laser-based DED method. Results demonstrate the heterogeneity in the part’s spatiotemporal temperature field, and support the fact that as the part number, or single part size or geometry, vary, the resultant residual stress due to temperature gradients will be impacted. As the time inter-layer time interval increases from 0 to 10 second, the temperature gradient magnitude in vicinity of the melt pool will increase slightly. 

Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods. 

Despite recent research efforts improving Additive Manufacturing (AM) systems, quality and reliability of AM built products remains as a challenge. There is a critical need to achieve process parameters optimizing multiple mechanical properties or geometry accuracy measures simultaneously. The challenge is that the optimal value of various objectives may not be achieved concurrently. Most of the existing studies aimed to obtain the optimal process parameters for each objective individually, resulting in duplicate experiments and high costs. In this study we investigated multiple geometry accuracy measures of parts fabricated by Fused Filament Fabrication (FFF) system. An integrated framework for systematically designing experiments is proposed to achieve multiple sets of FFF process parameters resulting in optimal geometry integrity. The proposed method is validated using a real world case study. The results show that optimal properties are achieved in a more efficient manner compared with existing methods. 

A bstract We report on a measurement of the $$ {\Lambda}_c^{+} $$ Λ c + to D 0 production ratio in peripheral PbPb collisions at $$ \sqrt{s_{\textrm{NN}}} $$ s NN = 5 . 02 TeV with the LHCb detector in the forward rapidity region 2 < y < 4 . 5. The $$ {\Lambda}_c^{+} $$ Λ c + ( D 0 ) hadrons are reconstructed via the decay channel $$ {\Lambda}_c^{+} $$ Λ c + → pK − π + ( D 0 → K − π + ) for 2 < p T < 8 GeV/ c and in the centrality range of about 65–90%. The results show no significant dependence on p T , y or the mean number of participating nucleons. They are also consistent with similar measurements obtained by the LHCb collaboration in pPb and Pbp collisions at $$ \sqrt{s_{\textrm{NN}}} $$ s NN = 5 . 02 TeV. The data agree well with predictions from PYTHIA in pp collisions at $$ \sqrt{s} $$ s = 5 TeV but are in tension with predictions of the Statistical Hadronization model. 

A bstract A search for the lepton-flavour violating decays B 0 → K *0 μ ± e ∓ and $$ {B}_s^0 $$ B s 0 → ϕμ ± e ∓ is presented, using proton-proton collision data collected by the LHCb detector at the LHC, corresponding to an integrated luminosity of 9 fb − 1 . No significant signals are observed and upper limits of $$ {\displaystyle \begin{array}{c}\mathcal{B}\left({B}^0\to {K}^{\ast 0}{\mu}^{+}{e}^{-}\right)<5.7\times {10}^{-9}\left(6.9\times {10}^{-9}\right),\\ {}\mathcal{B}\left({B}^0\to {K}^{\ast 0}{\mu}^{-}{e}^{+}\right)<6.8\times {10}^{-9}\left(7.9\times {10}^{-9}\right),\\ {}\mathcal{B}\left({B}^0\to {K}^{\ast 0}{\mu}^{\pm }{e}^{\mp}\right)<10.1\times {10}^{-9}\left(11.7\times {10}^{-9}\right),\\ {}\mathcal{B}\left({B}_s^0\to \phi {\mu}^{\pm }{e}^{\mp}\right)<16.0\times {10}^{-9}\left(19.8\times {10}^{-9}\right)\end{array}} $$ B B 0 → K ∗ 0 μ + e − < 5.7 × 10 − 9 6.9 × 10 − 9 , B B 0 → K ∗ 0 μ − e + < 6.8 × 10 − 9 7.9 × 10 − 9 , B B 0 → K ∗ 0 μ ± e ∓ < 10.1 × 10 − 9 11.7 × 10 − 9 , B B s 0 → ϕ μ ± e ∓ < 16.0 × 10 − 9 19.8 × 10 − 9 are set at 90% (95%) confidence level. These results constitute the world’s most stringent limits to date, with the limit on the decay $$ {B}_s^0 $$ B s 0 → ϕμ ± e ∓ the first being set. In addition, limits are reported for scalar and left-handed lepton-flavour violating New Physics scenarios. 

A bstract A first search for the lepton-flavour violating decays B 0 → K *0 τ ± μ ∓ is presented. The analysis is performed using a sample of proton-proton collision data, collected with the LHCb detector at centre-of-mass energies of 7, 8 and 13 TeV between 2011 and 2018, corresponding to an integrated luminosity of 9 fb − 1 . No significant signal is observed, and upper limits on the branching fractions are determined to be $$ \mathcal{B}\left({B}^0\to {K}^{\ast 0}{\tau}^{+}{\mu}^{-}\right)<1.0(1.2)\times {10}^{-5} $$ B B 0 → K ∗ 0 τ + μ − < 1.0 1.2 × 10 − 5 and $$ \mathcal{B}\left({B}^0\to {K}^{\ast 0}{\tau}^{-}{\mu}^{+}\right)<8.2(9.8)\times {10}^{-6} $$ B B 0 → K ∗ 0 τ − μ + < 8.2 9.8 × 10 − 6 at the 90% (95%) confidence level.