Given a set A and an abelian group B with operators in A, in the sense of Krull and Noether, we introduce the Ore group extension B[x; sigma B, delta B] as the additive group B[x], with A[x] as a set of operators. Here, the action of A[x]on B[x] is defined by mimicking the multiplication used in the classical case where A and Bare the same ring. We derive generalizations of Vandermonde's and Leibniz's identities for this construction, and they are then used to establish associativity criteria. Additionally, we prove a version of Hilbert's basis theorem for this structure, under the assumption that the action of A on B is what we call weakly s-unital. Finally, we apply these results to the case where B is a left module over a ring A, and specifically to the case where A and B coincide with a non-associative ring which is left distributive but not necessarily right distributive. (c) 2025 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http:// creativecommons.org/licenses/by/4.0/).
Wire Arc Additive Manufacturing (WAAM) has emerged as a promising metal additive manufacturing technique due to its high deposition rate, cost-effectiveness, and ability to build large-scale components. However, challenges such as porosity, poor mechanical properties, limited microstructural control, and residual stress hinder its full potential. Incorporating nanoparticles into the WAAM process has recently gained significant attention as a strategy to enhance material performance. This review provides a detailed and systematic analysis of the various types of nanoparticles used in WAAM, their methods of incorporation, effects on microstructure, mechanical performance, and functional properties of the built components. This review provides the first comprehensive classification and quantitative analysis of nanoparticle incorporation strategies in WAAM, systematically categorising 72 research articles across four distinct deposition strategies, including feedstock modification, interlayer application, direct melt pool injection, and ultrasonic dispersion. This work presents a comparative framework analysing the relative efficacy of different nanoparticle types (carbides, nitrides, and oxides) across multiple alloy systems, revealing that TiC emerges as the most extensively studied reinforcement. The review establishes that nanoparticle addition demonstrates positive influence on yield strength and ultimate tensile strength up to optimal concentrations, beyond which agglomeration-induced property deterioration occurs. Furthermore, the review identifies future perspectives for the optimized integration of nanoparticles in WAAM for high-performance manufacturing, design of multifunctional and hybrid reinforcement strategies, and adoption of AI-driven predictive modeling. The review discusses the industrial adoption barriers of the process. This systematic framework provides practical guidance for nanoparticle selection and process optimization, accelerating the industrial deployment of nanoparticle-reinforced WAAM technology.
Friction stir consolidation (FSC) is a relatively new solid-state recycling process developed to obtain small billets from metal chips in a unique working step. One of the main unresolved issues in solid-state recycling processes (e.g., Friction Stir Extrusion, Continuous Friction Extrusion, etc.) is the understanding of the effects of the oxide layers initially present on the chips on the final recycled products. In this paper, FSC was conducted on AA 6082 aluminum chips with different rotational speeds of 1000, 1500, 2000, and 2500 rpm. Microstructural evolution was examined using scanning electron microscopy (SEM) integrated with electron backscattered diffraction (EBSD). Backscattered electron (BSE) images showed that fine equiaxed grain structures were developed through the microstructure of the processed samples. Intermetallic compounds were broken down by the stirring action of the rotating tool, leading to the formation of micro- and nano-sized second-phase particles. The presence of loose high-angle grain boundaries connected to low-angle grain boundaries confirmed the occurrence of continuous dynamic recrystallization (CDRX). Texture analysis revealed the development of simple shear texture components of face-centered cubic (FCC) structured materials with the Oblique Cube component. Hardness measurements revealed that the hardness of the microstructure increased with a greater proportion of low-angle grain boundaries (LAGBs) and higher texture intensity.
Weld quality in friction stir welding (FSW) is difficult to maintain because rapid changes in heat input and material flow can generate transient surface defects during welding. These defects cannot be detected in real time using conventional inspection approaches, resulting in increased inspection time and higher production cost. Real-time visual monitoring is therefore required to support stable and efficient production. This study investigates whether modern convolutional neural network (CNN) models can provide reliable, in-situ segmentation of FSW surface defects together with accurate geometric measurements during welding. A multi-class dataset of weld-surface video frames was created and annotated for flash, burrs, voids, galling, tool interaction, and weld-zone regions. Several CNN-based segmentation models were evaluated, and a lightweight architecture suitable for real-time deployment was selected and integrated with a high-dynamic-range industrial camera on the FSW setup. The system performs continuous segmentation and extracts weld width and defect area from live video at approximately 25 frames per second. Quantitative validation against optical-microscope measurements demonstrated near microscope-level accuracy, with sub-millimetre weld-width deviations and defect-area errors below 6 %. These results demonstrate that real-time visual segmentation can provide reliable weld-quality monitoring in FSW, support early defect detection, and establish a practical foundation for future automated process-control strategies in manufacturing environments.
This article examines the inherent dualism within the HR profession, where two distinct yet interdependent dimensions emerge: a rational, results-driven and an empathetic, emotionally attuned dimension. These dimensions are here conceptualized as homo economicus (the rational actor) and homo emoticus (the emotion-driven counterpart). Drawing on critical theory, the analysis explores how certain ideas become dominant and authoritative within HR practice, shaping its priorities and decision-making processes. The article ultimately argues that for HR to evolve into a more inclusive and balanced function, it must integrate instrumental rationality with emotional competence, ensuring it effectively serves both organizational objectives and employee well-being.
