This study presents a methodology for optimizing key parameters of a fused deposition modeling (FDM) printer to minimize energy consumption (EC) while exceeding a specified tensile strength (TS) threshold. Employing Design of Experiments (DoE) with Taguchi and Response Surface analysis, we identify influential parameters affecting TS and EC. A Mixed-Integer Nonlinear Multi-Objective Optimization model is then utilized to balance TS and EC, resulting in optimal parameter values. Validation using fabricated specimens demonstrates less than 5 % error in Tensile Strength and less than 2 % error in Energy Consumption, confirming the efficacy of the proposed methodology.

Parallel Machine Scheduling (PMS) is a well-known problem in modern manufacturing. It is an optimization problem aiming to schedule n jobs using m machines while fulfilling certain practical requirements, such as total tardiness. Traditional approaches, e.g., mix integer programming and Genetic Algorithm (GA), usually fail, particularly in large-size PMS problems, due to computational time and/or memory burden and the large searching space required, respectively. This work aims to overcome such challenges by proposing a heuristic-based GA (DAS/GA). Specifically, a large-scale PMS problem with n independent jobs and m identical machines with a single server is studied. Individual heuristic algorithms (DAS) and GA are used as benchmarks to verify the performance of the proposed combined DAS/GA on 18 benchmark problems established to cover small, medium, and large PMS problems concerning standard performance metrics from the literature and a new metric proposed in this work (standardized overall total tardiness). Computational experiments showed that the heuristic part (DAS-h) of the proposed algorithm significantly enhanced the performance of the GA for large-size problems. The results indicated that the proposed algorithm should only be used for large-scale PMS problems because DAS-h trapped GA in a region of local optima, limiting its capabilities in small- and mainly medium-sized problems.

Reduced-activation ferritic/martensitic steels of Cr concentration between 2.25 and 12 wt% are candidate structural materials for next-generation nuclear reactors. In this study, molecular dynamics (MD) simulation is used to generate the displacement cascades in Fe–Cr structures with different Cr concentrations by using different primary knock-on atom (PKA) energies between 2 and 10 keV. A concentration-dependent model potential has been used to describe the interactions between Fe and Cr. Single crystals (SCs) of three different coordinate bases (e.g. [310], [510], and [530]) and bi-crystal (BC) structures with three different [001] tilt grain boundaries (GBs) (e.g. Σ5, Σ13, and Σ17) have been simulated. The Wigner–Seitz cell criterion has been used to identify the produced Frenkel pairs. The results show a marked difference between collisions observed in SCs and those in BC structures. The numbers of vacancies and interstitials are found to be significantly higher in BC structures than those found in SCs. The number of point defects exhibits a power relationship with the PKA energies; however, the Cr concentration does not seem to have any influence on the number of survived point defects. In BC models, a large fraction of the total survived point defects (between 59% and 93%) tends accumulate at the GBs, which seem to trap the generated point defects. The BC structure with Σ17 GB is found to trap more defects than Σ5 and Σ13 GBs. The defect trapping is found to be dictated by the crystallographic parameters of the GBs. For all studied GBs, self-interstitial atoms (SIAs) are easily trapped within the GB region than vacancies. An analysis of defect composition reveals an enrichment of Cr in SIAs, and in BC cases, more than half of the Cr-SIAs are found to be located within the GB region.

The pervasive integration of Artificial Intelligence (AI) in materials science has transformed the design, functionality, properties and structures of innovative, as well as traditional molecules and crystalline. The new AI-powered laboratories promise to address persistent problems facing humans such cost reduction and productivity improvement in research and development. AI accelerates the discovery of resilient and durable chemical structures capable of providing humans with quality lasting products. More importantly, AI allows researchers to devise new bioactive materials making drugs more potent and affordable. The present research offers readers a succinct comprehensive overview of the progress in Ai application within materials science. Findings demonstrate that AI has improved chemoinformatics, imaging and drug discovery. Moreover, the implementation of AI has resulted in advancing additive manufacturing through transforming 3D printing design and optimization processes. The study documents an increasing trend in developing specific AI tools for advancing materials science such as ChatMOF, MatKG, BioinspiredLLM, MatterGen, and SpectATNet. Notwithstanding the progress in AI’s implementation within materials science, there are important limits to the process represented by data quality, availability and accessibility. Future directions of research in the discipline include developing autonomous research assistant models and laboratories, improving the flexibility and responsiveness of AI systems to mimic human decision-making, and advancing interdisciplinary collaborations to enhance AI-enabled materials discovery.