Binder jetting additive manufacturing of aluminum nitride components

In this work, we report on the novel fabrication of aluminum nitride (AlN) components using Binder Jetting (BJT) additive manufacturing (AM). The AlN constructs were subjected to post-fabrication thermal treatment by hot isostatic pressing (HIPing) for 8 hours at a pressure of 206 MPa and temperature of 1900 °C. This treatment resulted in a 60.1% relative density maximum densification for AlN. The BJT printed AlN specimens were analyzed using various characterization techniques. The purity, microstructure, and polycrystallinity of the AlN phase formed were confirmed by techniques that included x-ray diffraction (XRD), scanning electron microscopy (SEM) coupled with energy dispersive spectroscopy (EDS), and high-resolution transmission electron microscopy (HRTEM). Second harmonic generation (SHG) microscopy showed polarization dependence and second harmonic signal at 470 nm, indicating the potential to produce thermal and optical-mechanical devices. Mechanical properties obtained by nanoindentation resulted in an elastic modulus of ~251 GPa when measured in fully dense, contiguous crystalline regions, corresponding to an apparent, porous bulk stiffness of ~90 GPa for the final, 60.1 % dense products. Finally, the laser flash method (LFM) was used to measure the thermal conductivity of the material as a function of temperature resulting in values from 4.82 W/mK to 3.17 W/mK for the temperature range from 23 °C to 500 °C, respectively.     

Scientific mapping to identify competencies required by industry 4.0

The objective of this study was to identify what competencies are identified in the literature as necessary for Industry 4.0 by conducting a survey of the literature and a scientific mapping of the evolution of the issues related to the qualification of professionals for Industry 4.0 and possible paths for research and education. A search was conducted on the Scopus, Web of Science and Science Direct databases for the interval from 2010 to 2018. This systematic review revealed topics and authors currently specialized in the field and allowed mapping the field of study. The identification of journals and keywords useful in future studies was also an object of this study. SciMAT software was used for the systematic literature review. The results are highlighted by the set of competencies (knowledge and skills) that must be developed in professional education to accompany the new industrial revolution, as well as the importance of integrating efforts by companies, governments and universities. These efforts should focus on creating “learning factories”, which are understood to be environments that provide practical experiences to these professionals, preparing them in the best way possible for the requirements of Industry 4.0. This conceptual map showed that the main competencies needed include skills: (leadership, strategic vision of knowledge, self-organization, giving and receiving feedback, pro-activity, creativity, problem solving, interdisciplinarity, teamwork, collaborative work, initiative, communication, innovation, adaptability, flexibility and self-management) and knowledge of contemporary fields (information and communication technology, algorithms, automation, software development and security, data analysis, general systems theory and sustainable development theory).     

Tropane alkaloid contamination of agricultural commodities and food products in relation to consumer health: Learnings from the 2019 Uganda food aid outbreak

Tropane alkaloids (TAs) are secondary plant metabolites derived mainly from Solanaceae plant families, with the most virulent invasive species being Datura stramonium. Datura stramonium commonly grows in cereal fields and produce TAs (e.g., hyoscyamine and scopolamine) which may accidentally contaminate cereals (and cereal-based foods) at occasionally high levels. Dietary exposure to TAs can be toxic and depending on the dose ingested can cause outcomes ranging from anticholinergic effects to acute poisoning and death. In 2019, 315 adults became ill and another five adults died in Uganda following consumption of a “Super Cereal” (a fortified blended food) that was later confirmed to be contaminated by TAs—a scenario which provoked this holistic review on TAs in foodstuffs. Thus, this article provides information on the history, development, occurrences, exposures, and human legislative and health benchmarks for TAs. It describes control strategies for reducing TA contamination of agricultural commodities and resultant health implications following consumption of TA contaminated foodstuffs. Adequate application of food safety control measures (including maximum limits) and good practices, from the start of cereal cultivation through to the final stages of manufacturing of food products can aid in the reduction of seeing toxic plants including D. stramonium in cereal fields.     

Observer‐based adaptive optimal output containment control problem of linear heterogeneous Multiagent systems with relative output measurements

This paper develops a relative output‐feedback–based solution to the containment control of linear heterogeneous multiagent systems. A distributed optimal control protocol is presented for the followers to not only assure that their outputs fall into the convex hull of the leaders' output but also optimizes their transient performance. The proposed optimal solution is composed of a feedback part, depending of the followers' state, and a feed‐forward part, depending on the convex hull of the leaders' state. To comply with most real‐world applications, the feedback and feed‐forward states are assumed to be unavailable and are estimated using two distributed observers. That is, a distributed observer is designed to measure each agent's states using only its relative output measurements and the information that it receives by its neighbors. Another adaptive distributed observer is designed, which uses exchange of information between followers over a communication network to estimate the convex hull of the leaders' state. The proposed observer relaxes the restrictive requirement of having access to the complete knowledge of the leaders' dynamics by all the followers. An off‐policy reinforcement learning algorithm on an actor‐critic structure is next developed to solve the optimal containment control problem online, using relative output measurements and without requiring the leaders' dynamics. Finally, the theoretical results are verified by numerical simulations.     

Interlayer bonding between thermoplastic composites and metals by in-situ polymerization technique

Fiber-metal laminates (FMLs) offer the superior characteristics of polymer composites (i.e., light weight, high strength and stiffness) with the ductility and fracture strength of metals. The bond strength between the two dissimilar materials, composite and metal, dictates the properties and performance of the FMLs. The bonding becomes more critical when the polymer matrix is thermoplastic and hydrophobic in nature. This work employed a novel bonding technique between thermoplastic composites and a metal layer using six different combinations of organic coatings. The flexural, and interlaminar shear strength of the thermoplastic fiber metal laminates (TP-FMLs) were examined to investigate the bond strengths in the different cases along with fracture characteristics revealed from the tested samples using scanning electron microscopy. The viscoelastic performance of the fabricated TP-FMLs were also investigated using the dynamic mechanical thermal analysis method.     

Cure simulations of thick adhesive bondlines for wind energy applications

Curing of adhesive bondlines is a critical and time-consuming operation in wind turbine blade manufacturing. Significant variation in adhesive thickness can lead to important differences in thermal histories trough the adhesive bonds due to the exothermic nature of the cure process. Reducing bondline cure cycle time and avoiding adhesive overheating are two competing factors in the design of cure temperature cycles. Predictive models on the impact of adhesive thickness variability in bondline cure temperature cycle is currently limited. Adhesive curing and temperature evolution can be simulated by finite element (FE) models coupling the heat transfer problem with the cure kinetics of the adhesive. The cure kinetics of the adhesive system was characterized by isothermal differential scanning calorimetry experiments and implemented in the FE software Abaqus/CAE by user subroutines. Predictions from the FE model were validated experimentally against temperature readings from the curing of 10, 20, and 30 mm thick adhesive bondlines. To highlight the role that predictive models potentially have in the optimization of bondline cure cycles a 2D cross section model representing the trailing edge of a wind turbine blade was used as case study. It was demonstrated that computational models enable customizing cure profiles for nonuniform adhesive thicknesses, ensuring fully cured bondlines with acceptable mechanical properties.