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Additive manufacturing is a good alternative to conventional methods for the production of near net shape geometries with high geometric complexity shorter lead times, being a good option for the manufacturing of dies for die casting process. In this research, a novel hot-work tool steel for aluminum die casting applications manufactured by laser powder bed fusion was investigated. As-built and stress-relieved (AS-B + SR) state was established and used as the reference condition, and subsequent post-treatments were added and compared to the reference condition. Test parts were evaluated using tensile, impact, hardness and thermal fatigue testing. Compared to the reference condition, heat treatment (HT), significantly increased the hardness, yield and ultimate tensile strengths of the material, due to the obtained tempered martensite microstructure. Hot isostatic pressing (HIP) prior to HT significantly increased the impact toughness and ductility, and slightly increased the yield and ultimate tensile strength values compared to the HT condition. The addition of nitriding treatment after HT, without intermedium HIP step, resulted in the highest surface hardness and lowest impact toughness. Thermal fatigue was mostly affected by the hardness and the softening of the material during the thermal fatigue testing. Results showed that a high surface hardness resulted in a higher thermal fatigue crack nucleation, meanwhile conditions with a high softening during thermal fatigue performance resulted in a higher crack propagation.

Modern production systems require multiple manufacturing centers—usually distributed among different locations—where the outcomes of each center need to be assembled to generate the final product. This paper discusses the distributed assembly permutation flow-shop scheduling problem, which consists of two stages: the first stage is composed of several production factories, each of them with a flow-shop configuration; in the second stage, the outcomes of each flow-shop are assembled into a final product. The goal here is to minimize the makespan of the entire manufacturing process. With this objective in mind, we present an efficient and parameter-less algorithm that makes use of a biased-randomized iterated local search metaheuristic. The efficiency of the proposed method is evaluated through the analysis of an extensive set of computational experiments. The results show that our algorithm offers excellent performance when compared with other state-of-the-art approaches, obtaining several new best solutions.

The human T-cell leukaemia virus type 1 (HTLV-1) is associated with pulmonary inflammation. Indigenous Australians in central Australia have a very high prevalence of HTLV-1 infection and we hypothesised that this might contribute to high rates of bronchiectasis in this population. 80 Indigenous adults with confirmed bronchiectasis, each matched by age, sex and language to two controls without bronchiectasis, were recruited. Case notes and chest imaging were reviewed, HTLV-1 serology and the number of peripheral blood leukocytes (PBLs) infected with HTLV-1 (pro-viral load (PVL)) were determined, and radiological abnormality scores were calculated. Participants were followed for a mean±sd of 1.14±0.86 years and causes of death were determined. Median (interquartile range) HTLV-1 PVL for cases was 8-fold higher than controls (cases 213.8 (19.7-3776.3) copies per 105 PBLs versus controls 26.6 (0.9-361) copies per 105 PBLs; p=0.002). Radiological abnormality scores were higher for cases with HTLV-1 PVL ≥1000 copies per 105 PBLs and no cause of bronchiectasis other than HTLV-1 infection. Major predictors of bronchiectasis were prior severe lower respiratory tract infection (adjusted OR (aOR) 17.83, 95% CI 4.51-70.49; p<0.001) and an HTLV-1 PVL ≥1000 copies per 105 PBLs (aOR 12.41, 95% CI 3.84-40.15; p<0.001). Bronchiectasis (aOR 4.27, 95% CI 2.04-8.94; p<0.001) and HTLV-1 PVL ≥1000 copies per 105 PBLs (aOR 3.69, 95% CI 1.11-12.27; p=0.033) predicted death. High HTLV-1 PVLs are associated with bronchiectasis and with more extensive radiological abnormalities, which may result from HTLV-1-mediated airway inflammation.

Modern manufacturing systems are composed of several stages. We consider a manufacturing environment in which different parts of a product are completed in a first stage by a set of distributed flowshop lines, and then assembled in a second stage. This is known as the distributed assembly permutation flowshop problem (DAPFSP). This paper studies the stochastic version of the DAPFSP, in which processing and assembly times are random variables. Besides minimizing the expected makespan, we also discuss the need for considering other measures of statistical dispersion in order to account for risk. A hybrid algorithm is proposed for solving this NP-hard and stochastic problem. Our approach integrates biased randomization and simulation techniques inside a metaheuristic framework. A series of computational experiments contribute to illustrate the effectiveness of our approach.