Search Results (604)

For the Vacuum Induction Gas Atomization (VIGA) powder preparation process, a multi-scale coupled numerical simulation and experimental validation were employed to systematically reveal the influence mechanisms of process parameters on the primary atomization flow field structure, secondary atomization droplet breakup behavior, and powder particle size distribution Using Computational Fluid Dynamics (CFD) methods combined with the VOF (Volume of Fluid) multiphase flow model, the fragmentation morphology of the melt during primary atomization was simulated, capturing the dynamic characteristics of liquid film thinning and the reduction in initial droplet area. Concurrently, the DPM (Discrete Phase Model) coupled with the TAB (Taylor Analogy Breakup) model was applied to predict the droplet size distribution in secondary atomization. The results indicate that increasing atomization pressure (2.5–4.5 MPa) significantly enhances secondary fragmentation intensity, reducing the median particle size (D50) from 42.1 μm to 37.5 μm. Experimental studies on Ni-based superalloys, validated by laser particle size analysis, confirmed that higher atomization pressure improves gas velocity and gas–liquid energy conversion efficiency, optimizes turbulent flow structures, and refines powder particles. The study concludes that the multi-scale coupled model effectively predicts atomization dynamics. By optimizing atomization pressure, powder particle size can be significantly refined, providing a theoretical basis for process control of high-performance spherical powders used in additive manufacturing. Full article

In the context of mass customization and mixed-model production systems, Master Production Scheduling (MPS), which determines production start dates, plays a critical role. However, in such environments, MPS faces a dual challenge: ensuring due-date adherence under multiple capacity constraints while also reducing operational instability caused by uneven day-to-day consumption of critical components, referred to as Replenishment and Industrial Characteristics (RICs). This paper proposes a new mathematical model for MPS with a Smoothing Mechanism for RICs (MPS-SM). This bi-objective formulation extends a baseline due-date-driven model with an explicit production smoothing/leveling (also known as Heijunka) term, minimizing deviations of RIC usage from weekly ideal levels. By embedding smoothing directly into MPS, the approach provides a pre-leveling effect that can reduce (or ideally eliminate) downstream complexity, specifically related to schedule modifications required in a separate smoothing stage. To reflect changing scheduling priorities, smoothing is weighted through an innovative context-aware non-linear weekly function that assigns lower importance near execution and greater importance farther into the horizon. The models are evaluated in a rolling-horizon simulation-optimization framework using data from a real-world truck manufacturer. Several experiments over 300 discrete-event simulated days show that MPS-SM consistently reduces RIC variability while inducing a controlled increase in lateness penalties. Full article

Numerical simulation of metal microstructure evolution is essential for material design and performance optimization. This paper reviews major simulation methods for key evolution mechanisms, including recrystallization, grain growth, slip, twinning, and phase transformation. The reviewed methods are classified into atomistic models, discrete-field models, and continuous-field models. Molecular dynamics (MD) is discussed as an independent atomistic approach, with emphasis on its role in revealing atomic-scale mechanisms, calibrating mesoscale parameters, and bridging atomistic, mesoscale, and continuum simulations. Discrete-field methods, including Monte Carlo, cellular automata, and vertex models, are compared with continuous-field methods, including artificial neural networks, phase field models, finite element methods, and level-set methods. Furthermore, a semi-quantitative evaluation matrix based on accuracy, computational cost, scalability, and applicability is established to clarify the practical trade-offs among different methods. The results show that no single method is universally optimal; instead, method selection should depend on the dominant physical mechanism, target length scale, required accuracy, and available computational resources. This review provides methodological guidance for multiscale microstructure simulation and supports future applications in precision machining, additive manufacturing, and process parameter optimization. Full article

Industrial surface defect detection constitutes a fundamental component in automated quality inspection but remains challenging due to complex textures, diverse defect scales, and stringent real-time constraints. To address these issues, we present MWYOLO, an enhanced YOLO11-based detection framework tailored for accurate and efficient industrial inspection. First, a C3k2-Mamba Spatial Fusion Block (C3k2-MSFB) that integrates global contextual information with local structural cues via state-space modeling, enabling more discriminative representations of fine-grained texture variations. Second, a multi-scale wavelet attention (MWA) module is embedded into the backbone, leveraging wavelet-domain feature decomposition and dual attention to capture multi-frequency patterns, thereby improving sensitivity to fine-grained and subtle defect patterns. Third, an Inner-CIoU loss is developed to emphasize interior geometric alignment during bounding-box regression, offering more stable optimization for ambiguous or low-contrast targets. Extensive experiments conducted on three representative industrial datasets—NEU-DET, HRIPCB, and a self-constructed GSD dataset—demonstrate the effectiveness of MWYOLO. The model achieves mAP50 scores of 81.4%, 98.1%, and 67.7%, respectively, while maintaining a lightweight design with only 3.2M parameters and 7.3 GFLOPs. The results validate MWYOLO as a robust and computationally efficient solution, offering a favorable balance between accuracy, interpretability, and deployability for real-world industrial defect detection tasks. Full article

Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination. Full article

This study examines the relationship between corporate concentration and labour market conditions in Hungary’s food industry over the period 1993–2022. Using industry-level panel data for the four most highly concentrated subsectors, cereals, food processing, oils and fats, and sugar and confectionery, corporate concentration is measured using the Herfindahl–Hirschman Index (HHI), and a two-way fixed-effects panel regression model is employed to assess its association with wage structures, working-time arrangements, and employment composition. The results reveal a statistically significant negative relationship between corporate concentration and both gross monthly earnings and base hourly wages. A 1000-point increase in the HHI is associated with an approximately 10 percent decline in base wages. Higher concentration is also positively associated with greater reliance on part-time employment and increased overtime intensity, alongside a significant reduction in paid leave provision. Importantly, when variables capturing working-time arrangements and employment structure are incorporated into the earnings model, the direct effect of concentration becomes statistically insignificant. This pattern likely reflects the fact that these variables are directly embedded in the determination of gross monthly earnings, suggesting that the effect of concentration operates indirectly through adjustments in working time and employment composition rather than through a purely independent channel. This finding suggests that the impact of concentration on wages operates partly through structural adjustments in compensation systems and increased labour flexibility. Overall, the evidence indicates that corporate concentration in Hungary’s food manufacturing sector does not necessarily reduce nominal earnings but instead reshapes their composition. The role of base wages weakens, while regular bonuses emerge as the primary mechanism of income adjustment, increasing managerial discretion and income volatility. These findings contribute to the literature on labour market monopsony in transition economies and underscore the importance of integrating labour market considerations into competition policy frameworks. Full article

To improve the forming quality, precision, and machining stability of square hole structures in high-hardness gun steel (PCrNi₃MoVA) during electrochemical machining (ECM). A planar cathode bottom design array with liquid holes is innovatively proposed in this paper to achieve uniform distribution of the flow field in discrete bottom machining gaps. The modeling and simulation of the flow field within the ECM gap were carried out using simulation software. A cathode with 25 outlet holes in an array distribution and a profile thickness of 1 mm was designed. However, sparking occurred on the cathode bottom surface during ECM experiments, leading to machining short-circuit. Further analysis and structural optimization were conducted on the sparking area of the cathode bottom surface. The introduction of flow guide grooves on the cathode bottom surface can effectively improve the uniformity of flow field distribution and the stability of the machining process, thereby solving the problem of manufacturing square holes in high-hardness gun steel materials. Finally, under the conditions of an electrolyte pressure of 0.7 MPa, a machining voltage of 12 V, a frequency of 2 kHz, a duty cycle of 60%, and a feed rate of 0.8 mm/min, a square hole with a side length of 10.2 mm was obtained, with a straightness error of ±0.05 mm and a filet radius of 0.38 ± 0.05 mm. Full article

This paper proposes an efficient algorithm that integrates a variable neighborhood search (VNS) framework with an adaptive voxel discretization for the three-dimensional irregular packing problem. The problem arises in additive manufacturing, logistics loading, and other fields, especially in strip packing scenarios where the filling length in a virtual container with a fixed cross-section and infinite length is to be minimized. The algorithm first discretizes continuous three-dimensional geometric models into Boolean voxel matrices, thereby transforming complex geometric interference detection into efficient logical operations. An initial solution is generated using a greedy “largest-volume-first” strategy. An innovative adaptive voxel precision adjustment mechanism is introduced to dynamically modify the discretization granularity according to the current filling rate, realizing a hierarchical solution strategy of “coarse-grained fast search + fine-grained precise optimization”. On this basis, a variable-neighborhood iterative framework based on tabu search (TS-VNS) is constructed. Three complementary neighborhood operators are designed: single-item reinsertion, block exchange, and rotation perturbation, together with an adaptive operator selection mechanism driven by historical contributions. Experiments on multiple standard instances of varying scales and complexities (e.g., miniature chess pieces and engine components) show that the proposed algorithm outperforms comparative methods in both packing height and average height, achieving a favorable balance between solution efficiency and stability. Thus, it provides a reliable and efficient approach for the practical engineering application of three-dimensional irregular packing. Full article

Adhesion between fused deposition modelling (FDM) printed polymers and textile substrates is critical for durable printed-on-textile hybrids. Since no dedicated test standard exists for additively manufactured textile interfaces, many studies use T-peel methods adapted from adhesive-bond standards. However, printed-on-textile joints are often governed by polymer penetration into the fabric and mechanical interlocking, rather than by a discrete adhesive layer. This work evaluates a fixture-based perpendicular (normal-separation) tensile method, using a circular dolly printed directly onto a cotton plain-weave substrate and a fully 3D-printable, threaded, self-aligning clamping assembly. Three representative filaments, namely polyethylene terephthalate glycol-modified (PETG), polylactic acid (PLA), and thermoplastic polyurethane (TPU), were tested using both the proposed pull-off method and an ISO 11339-type T-peel benchmark, with n = 8 specimens per polymer. The perpendicular method produced complete datasets for all polymers and clearly differentiated adhesion performance (TPU > PLA > PETG). In contrast, for T-peel, the standard evaluation window (25–125 mm) was completed for all PETG specimens but only for a subset of PLA specimens and a single TPU specimen. In the remaining tests, premature substrate failure prevented completion of this window, so the results could not be evaluated. Microscopy confirmed distinct interlocking morphologies across polymers, supporting the observed differences in failure behavior between peel and normal separation. Overall, the results indicate that perpendicular dolly pull-off testing is a practical and reproducible alternative for quantifying adhesion across a wider range of printed-on-textile bonding conditions. Full article

Considering that consumers have dual reference effects of price and goodwill; that is, consumers have psychological expectations for price and brand goodwill when making consumption decisions. A difference game model with reference effects is established for a closed-loop supply chain composed of a manufacturer, a retailer and a recycler, and a bidirectional cost-sharing contract is adopted for coordination. At the same time, the impact of dual reference effects and the bidirectional cost sharing contract on supply chain members’ profits are further analyzed by numerical simulation. We find that: (1) The impact of the price reference effect and the goodwill reference effect on supply chain decisions and market demand exhibits significant cost interval dependence. Notably, within a specific cost interval and under the influence of the dual reference effects, the market exhibits a phenomenon of “high price, high demand.” (2) The price reference effect influences the power structure of the supply chain. Specifically, when the price reference effect exceeds a certain threshold, the retailer’s profit surpasses the manufacturer’s profit. (3) The bidirectional cost-sharing contract coordinates the discrete dynamic closed-loop supply chain under dual reference effects. Consequently, it achieves a double Pareto improvement in supply chain members’ profits and brand goodwill. Full article

This paper investigates the powder spreading process in a Z-direction multi-material fabrication system utilizing a blade. Focusing on 316L stainless steel and CuCrZr, a discrete element model was developed to simulate powder spreading at the heterogeneous material interface. The effects of spreading speed and theoretical layer thickness on the resulting powder bed quality were systematically examined. The results reveal that during spreading over a heterogeneous bed, the underlying powder exhibits an unsteady “forward-surging and rearward-suppressing” motion pattern, with inter-particle force chains displaying significant spatiotemporal fluctuations. Increasing the spreading speed exacerbates the disturbance and removal of the underlying powder, leading to a reduction in the deposited mass of CuCrZr and a deterioration in its distribution uniformity. Conversely, increasing the layer thickness effectively mitigates the mechanical disturbance of the underlying powder by the blade, significantly enhancing both the deposited mass of CuCrZr and its distribution uniformity. Further investigation demonstrates that employing a higher spreading speed in combination with a larger layer thickness can achieve a favorable powder bed quality while maintaining high spreading efficiency, thereby enabling a synergistic optimization of productivity and bed quality. This work elucidates the mesoscopic dynamic mechanisms governing the powder spreading process at Z-direction heterogeneous interfaces and provides a theoretical foundation for process optimization in multi-material laser powder bed fusion. Full article

The flexible job shop scheduling problem (FJSP), which simultaneously involves machine assignment and operation sequencing under multiple constraints, is a typical NP-hard combinatorial optimization problem, and efficient scheduling is of great importance for improving production efficiency and manufacturing flexibility. To address this problem, the success-history beaver behavior optimizer (SHBBO) is introduced to solve FJSP with the objective of minimizing the makespan. First, considering the discrete characteristics of FJSP, an effective encoding and decoding scheme is designed to represent operation sequences and machine assignments. Then, the adaptive success-history mechanism of SHBBO is employed to dynamically adjust the search parameters during the optimization process, enabling a better balance between global exploration and local exploitation. Meanwhile, the behavioral update strategy of SHBBO is adapted to the scheduling environment so that candidate solutions can be effectively evolved in the discrete solution space. In addition, a population updating strategy and elite-guided search mechanism are incorporated to enhance solution quality and convergence performance. Finally, extensive experiments are conducted on benchmark FJSP instances to verify the effectiveness of the proposed method. Experimental results show that SHBBO achieves the best average results on 11 out of 12 CEC2022 benchmark functions, with particularly notable improvements over the original beaver behavior optimizer (BBO) on functions such as F6 (56.69%), F5 (12.20%), and F10 (9.18%). On the BRdata benchmark instances, SHBBO obtains the best or tied-best makespan on all 10 instances, with an average percentage relative deviation (PRD) of 0, and reduces the makespan by 7.69% on MK10 and 6.25% on MK06 compared with BBO. Full article

The multi-finger robotic hand offers great potential for precise control due to its high degrees of freedom. Yet, manipulating objects forms a closed-chain kinematic system, which compounds the dimensionality and computational complexity of trajectory tracking. To tackle this challenge, and inspired by the widespread application of the zeroing neurodynamics (ZND) in robotic control, this study proposes a novel direct-discrete robust neurodynamics (DDRN) algorithm. The proposed algorithm advances the ZND methodology by employing a direct discretization design strategy. This strategy is crucial for two reasons. First, it fits naturally with the discrete-time nature of digital systems, enabling practical implementation. Second, it enhances precision by avoiding the integration errors inherent in continuous-to-discrete transformations. By simultaneously integrating this direct discretization with explicit noise suppression mechanisms, the DDRN algorithm efficiently solves the high-dimensional tracking problem formulated as a constrained time-varying quadratic programming (CTVQP) problem. Theoretical analyses demonstrate that under various noise environments, the steady-state residuals (SSRs) achieve global convergence, guaranteeing the algorithm’s strong robustness and high accuracy. Furthermore, comprehensive numerical simulations substantiate its superior performance. Practically, this DDRN algorithm enables more reliable and precise real-time control of dexterous robotic hands, with potential benefits for advanced manufacturing, prosthetic hands, and automated assembly where accurate trajectory tracking under sensor noise is critical. Full article

Additive manufacturing (AM) requires process-comparison tools that remain practical when sample generation and testing are costly. We propose a univariate, nonparametric workflow for comparing a candidate AM process to a stable reference process by testing distributional equivalency for a single response variable. The method discretizes the reference distribution into empirical percentile-defined bins and combines this representation with a sequential sampling protocol designed to reduce unnecessary sampling when evidence for equivalency or non-equivalency becomes sufficient. Simulation studies were used to evaluate operating characteristics across experimental settings, and a validation case study based on geometric measurements of laser based powder bed fusion plate scans correctly classified a candidate process expected to be equivalent to the reference while identifying a non-equivalent process at the first sampling step. The workflow is most appropriate for low-sample, high-cost, or throughput-constrained settings, and is best viewed as a tool for process comparability, change control, calibration, and requalification support rather than as a standalone replacement for qualification standards. The full workflow is implemented in the open-source AMEquivalency package to support reproducible analysis. Full article

This paper addresses the use of discrete-event simulation as a tool for optimizing the production process of an assembly line and identifying the potential for improving production efficiency. A digital model of the manufacturing system was developed in the FlexSim simulation environment based on real production data, technological operation sequences, and statistically defined cycle times. The model was designed to accurately represent real production conditions, including control logic, resource interactions, and material flow. The simulation results were analyzed using graphical and quantitative reports, which enabled the identification of production bottlenecks and inefficient resource utilization. Based on the obtained data, a process optimization strategy was proposed in the form of intelligent operator rotation between workstations to increase operator utilization and improve overall system efficiency. The proposed modifications were subsequently verified through simulation experiments, confirming the preservation of the required production capacity while improving the efficiency of human resource utilization. The findings confirm that simulation modeling represents an effective tool for the analysis, design, and verification of optimization measures, enabling the reduction in operational costs and risks associated with implementing changes in real manufacturing environments. Full article