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A novel method for modelling permanent magnets is investigated based on numerical approximations with rational functions. This study aims to introduce the AAA algorithm and other recently developed, cutting-edge mathematical tools, which provide outstandingly fast and accurate numerical computation of potentials and vector fields.

First, the AAA algorithm is briefly introduced along with its main variants and other advanced mathematical tools involved in the modelling. Then, the analysis of a circular Halbach array with a one-pole pair is carried out by means of the AAA-least squares method, focusing on vector potential and flux density in the bore and validating results by means of classic finite element software. Finally, the investigation is completed by a finite difference analysis.

AAA methods for field analysis prove to be strikingly fast and accurate. Results are in excellent agreement with those provided by the finite element model, and the very good agreement with those from finite differences suggests future improvements. They are also easy programming; the MATLAB code is less than 200 lines. This indicates they can provide an effective tool for rapid analysis.

AAA methods in magnetostatics are novel, but their extension to analogous physical problems seems straightforward. Being a meshless method, it is unlikely that local non-linearities can be considered. An aspect of particular interest, left for future research, is the capability of handling inhomogeneous domains, i.e. solving general interface problems.

The authors use cutting-edge mathematical tools for the modelling of complex physical objects in magnetostatics.

The premise of this research is that the coupling of reinforcement learning algorithms and computational dynamics can be used to design efficient control strategies and to improve the cooling of hot components by quenching, a process that is classically carried out based on professional experience and trial-error methods. Feasibility and relevance are assessed on various 2-D numerical experiments involving boiling problems simulated by a phase change model. The purpose of this study is then to integrate reinforcement learning with boiling modeling involving phase change to optimize the cooling process during quenching.

The proposed approach couples two state-of-the-art in-house models: a single-step proximal policy optimization (PPO) deep reinforcement learning (DRL) algorithm (for data-driven selection of control parameters) and an in-house stabilized finite elements environment combining variational multi-scale (VMS) modeling of the governing equations, immerse volume method and multi-component anisotropic mesh adaptation (to compute the numerical reward used by the DRL agent to learn), that simulates boiling after a phase change model formulated after pseudo-compressible Navier–Stokes and heat equations.

Relevance of the proposed methodology is illustrated by controlling natural convection in a closed cavity with aspect ratio 4:1, for which DRL alleviates the flow-induced enhancement of heat transfer by approximately 20%. Regarding quenching applications, the DRL algorithm finds optimal insertion angles that adequately homogenize the temperature distribution in both simple and complex 2-D workpiece geometries, and improve over simpler trial-and-error strategies classically used in the quenching industry.

To the best of the authors’ knowledge, this constitutes the first attempt to achieve DRL-based control of complex heat and mass transfer processes involving boiling. The obtained results have important implications for the quenching cooling flows widely used to achieve the desired microstructure and material properties of steel, and for which differential cooling in various zones of the quenched component will yield irregular residual stresses that can affect the serviceability of critical machinery in sensitive industries.

Integrating additive manufacturing (AM) tools in traditional mold-making provides complex yet affordable sand molds and cores. AM processes such as selective laser sintering (SLS) and Binder jetting three-dimensional printing (BJ3DP) are widely used for patternless sand mold and core production. This study aims to perform an in-depth literature review to understand the current status, determine research gaps and propose future research directions. In addition, obtain valuable insights into authors, organizations, countries, keywords, documents, sources and cited references, sources and authors.

This study followed the systematic literature review (SLR) to gather relevant rapid sand casting (RSC) documents via Scopus, Web of Science and EBSCO databases. Furthermore, bibliometrics was performed via the Visualization of Similarities (VOSviewer) software.

An evaluation of 116 documents focused primarily on commercial AM setups and process optimization of the SLS. Process optimization studies the effects of AM processes, their input parameters, scanning approaches, sand types and the integration of computer-aided design in AM on the properties of sample. The authors performed detailed bibliometrics of 80 out of 120 documents via VOSviewer software.

This review focuses primarily on the SLS AM process.

A SLR and bibliometrics using VOSviewer software for patternless sand mold and core production via the AM process.

The purpose of this study is to present a novel extended hybrid analytical method (HAM) that leverages a two-dimensional (2-D) coupling between the semi-analytical Maxwell–Fourier analysis and the finite element method (FEM) in Cartesian coordinates.

The proposed model is applied to flat permanent-magnet linear electrical machines with rotor-dual. The magnetic field solution across the entire machine is established by coupling an exact analytical model (AM), designed for regions with relative magnetic permeability equal to unity, with a FEM in ferromagnetic regions. The coupling between AM and FEM occurs bidirectionally (x, y) along the edges separating teeth regions and their adjacent regions through applied boundary conditions.

The developed HAM yields accurate results concerning the magnetic flux density distribution, cogging force and induced voltage under various operating conditions, including magnetic or geometric parameters. A comparison with hybrid finite-difference and hybrid reluctance network methods demonstrates very satisfactory agreement with 2-D FEM.

The original contribution of this paper lies in establishing a direct coupling between the semi-analytical Maxwell–Fourier analysis and the FEM, particularly at the interface between adjacent regions with differing magnetic parameters.

Additive manufacturing (AM) is a layer-by-layer technique that helps to create physical objects from a three-dimensional data set. Fused deposition modeling is a widely used material extrusion (MEX)-based AM technique that melts thermoplastic filaments and selectively deposits them over a build platform. Despite its simplicity and affordability, it suffers from various printing defects, with partial warping being a prevalent issue. Warpage is a physical deformation caused by thermal strain incompatibility that results in the bending of the printed part away from the build platform. This study aims to investigate the warpage characteristics of printed parts based on geometrical parameters and build orientations to reduce the warpage extent.

Cuboidal samples of thermoplastic acrylonitrile butadiene styrene ranging from 5 to 80 mm were printed using a commercial MEX system. A Taguchi method-based design of experiment trial was performed to optimize the placement and orientation of the part for minimal warpage.

It was found that a lower value of the “in-plane” aspect ratio and a more prominent part thickness are favorable for minimal warpage. The part should always be placed near the region with the highest temperature (least thermal gradient) to minimize the warpage.

A novel dimensionless parameter (Y) is proposed that should be set to a minimum value to achieve minimal warpage. The results of this study can help improve the design and part placement for the MEX technique, thus elevating the print quality.

In traditional methods, a one-directional distance transformation is employed to eliminate the near-singularity in the radial direction. However, when the projection point of the source point is near the boundary of the integral element, the near-singularity in the circumferential direction still exists, resulting in large errors in the numerical results. The purpose of this paper is to propose a bi-directional distance transformation for the elimination of near-singularities in two directions arising from the irregular integral element.

The sources of the circumferential near-singularity caused by irregular sub-triangular element are analyzed in this paper. A bi-directional distance transformation based on the (a, β) transformation is proposed to eliminate the near-singularities in the two directions. The (a, β) transformation is initially introduced to separate the integral variables and streamline the implementation of subsequent transformations. In the transformed (a, β) coordinate system, a new distance transformation applied in the circumferential direction is constructed.

With the proposed method, the radial and circumferential near-singularities are eliminated using two different distance transformations, respectively. Thus, accurate calculations of the nearly singular integrals can be achieved, irrespective of the shape of the integral element.

Numerical examples are presented to calculate the nearly singular integrals of different orders over both the linear integral element and the quadratic integral element. Comparative studies demonstrate that the proposed method significantly improves the accuracy of these calculations.

The study aims to explore the available academic literature on the decision-making frameworks used in additive manufacturing management (AMM).

This research formulates a systematic literature review to determine the research trend of the decision-making framework in AMM. Further, the theory, context, characteristics, and methodology (TCCM) framework is used to identify the research gaps and suggest future research directions.

The systematic literature review (SLR) delves into overarching research themes within decision-making frameworks in AMM. Additionally, it uncovers trends in article publication, geographical distribution, methodologies utilized, and industry applications. This review not only reveals research gaps but also proposes directions for future exploration.

The key novelty of this research lies in revealing the five most contributing themes of decision-making frameworks in AMM, with the highest contributing theme being AM process selection, followed by part selection for AM. This finding enables decision-makers to make informed decisions to address similar problems while exploring AM technology. Moreover, this research introduces an AM part fabrication roadmap inspired by the literature review. Lastly, the paper highlights key research gaps for future research.

This paper aims to explore recent advances in drift compensation algorithms for Electronic Nose (E-nose) technology and addresses sensor drift challenges through offline, online and neural network-based strategies. It offers a comprehensive review and covers causes of drift, compensation methods and future directions. This synthesis provides insights for enhancing the reliability and effectiveness of E-nose systems in drift issues.

The article adopts a comprehensive approach and systematically explores the causes of sensor drift in E-nose systems and proposes various compensation strategies. It covers both offline and online compensation methods, as well as neural network-based approaches, and provides a holistic view of the available techniques.

The article provides a comprehensive overview of drift compensation algorithms for E-nose technology and consolidates recent research insights. It addresses challenges like sensor calibration and algorithm complexity, while discussing future directions. Readers gain an understanding of the current state-of-the-art and emerging trends in electronic olfaction.

This article presents a comprehensive review of the latest advancements in drift compensation algorithms for electronic nose technology and covers the causes of drift, offline drift compensation algorithms, online drift compensation algorithms and neural network drift compensation algorithms. The article also summarizes and discusses the current challenges and future directions of drift compensation algorithms in electronic nose systems.

This paper aims to propose a method for automatic design of additive manufacturing (AM) flow channel paths driven by path length and pressure loss. The research focuses on the automatic design of channel paths, intending to achieve the shortest flow channel length or minimum pressure loss and improve the design efficiency of AM parts.

The initial layout of the flow channels is redesigned to consider the channels print supports. Boundary conditions and constraints are defined according to the redesigned channels layout, and the equation consisting of channel length and pressure loss is used as the objective function. Then the path planning simulation is performed based on particle swarm algorithm. The proposed method describes the path of flow channels using spline cures. The spline curve is controlled by particle (one particle represents a path), and the particle is randomly generated within the design space. After the path planning simulation is completed, the generated paths are used to create 3D parts.

Case study 1 demonstrates the automatic design of hydraulic spool valve. Compared to conventional spool valve, the pressure loss was reduced by 86% and the mass was reduced by 83%. The design results of case study 2 indicate that this approach is able to find the shortest channel path with lower computational cost.

The automatic design method of flow channel paths driven by path length and pressure loss presented in this paper provides a novel solution for the creation of AM flow components.

This study aims to investigate the demoulding characteristics of material-jetted rapid mould inserts having different surface textures for micro-injection moulding using in-line measurements and surface metrology.

Material-jetted inserts with the negative cavity of a circular test product were fabricated using different surface finishes and printing configurations, including glossy, matte and vertical settings. In-line measurements included the recording of demoulding forces at 10 kHz, which was necessary to capture the highly-dynamic characteristics. A robust data processing algorithm was used to extract reliable demoulding energies per moulding run. Thermal imaging captured surface temperatures on the inserts after demoulding. Off-line measurements, including focus variation microscopy and scanning electron microscopy, compared surface textures after a total of 60 moulding runs.

A framework for capturing demoulding energies from material-jetted rapid tools was demonstrated and compared to the literature. Glossy surfaces resulted in significantly reduced demoulding forces compared to the industry standard steel moulds in the literature and their material-jetted counterparts. Minimal changes in the surface textures of the material-jetted inserts were found, which could potentially permit their prolonged usage. Significant correlations between surface temperatures and demoulding energies were demonstrated.

The research presented here addresses the very topical issue of demoulding characteristics of soft, rapid tools, which affect the quality of prototyped products and tool durability. This was done using state-of-the-art, high-speed sensing technologies in conjunction with surface metrology and their durability for the first time in the literature.