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Abstract: Wood processing remains a manufacturing field that relies heavily on craftsmanship and the experience of skilled workers. Although Computer Numerical Control (CNC) techniques have introduced significant progress in wood manufacturing, one of the main challenges remains the natural variability of wood. Variations in grain direction, density, moisture content, and other wood properties can result in inconsistent cutting behavior, increased surface roughness, and dimensional inaccuracies. Furthermore, the growing need to conserve natural resources continuously drives innovation in wood processing. This paper investigates CNC machining of hardwoods with a particular focus on evaluating surface roughness as a key quality parameter. Aiming to achieve better surface roughness leads to eliminating or reducing sanding operations. Cutting conditions, tool path optimization, and time efficiency during hardwood processing remain crucial factors in the furniture industry. Finished wooden components are typically assembled from multiple pieces using joining techniques; however, unlike in the forestry industry, the quality of the prepared samples is not systematically monitored and is often assessed solely by human judgment. Consequently, the impact of variable parameters, such as feed speed, timber species, fiber orientation, during the cutting and workpiece preparation was evaluated by multivariate statistical analysis. The study of milling curvilinear shapes in hardwoods (ash, oak, walnut) revealed that the annual growth ring section is the most significant factor affecting the quality of the finished product. Consequently, the adapted tool path according to the materials growth ring section would be advantageous while milling curvilinear shapes of the hardwood materials.
Keywords: CNC milling, hardwood processing, wood manufacturing, surface roughness, fiber orientation, multivariate analysis, MANOVA, wood machining quality
Published in DKUM: 22.01.2026; Views: 0; Downloads: 0
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Abstract: Micro-milling is recognized as one of the most important manufacturing technologies for producing micro-components/products. Amongst various materials, graphite has an important role in conventional micro-electrical discharge machining electrodes. This paper is focused on the investigation of the effect of micro-milling process parameters on the dimensional accuracy and surface quality of ultrafine grain graphite TTK-4. Depth of cut, spindle speed, stepover distance and feed rate have been considered as process variables of micro ball-end milling in experimental design. Moreover, the influence of the workpiece’s inclination angle was also investigated. Taguchi’s L9 (34) orthogonal array was chosen to design the experiments, whereas grey relational analysis (GRA) was utilized for the multi-objective optimization of the micro ball end milling process with minimum dimensional deviation and minimum arithmetic mean roughness as objective functions. Furthermore, principal component analysis (PCA) was used to extract principal components and identify the corresponding weights for performance characteristics. In order to determine the significance of micro-milling parameters on overall machining performance, analysis of variance (ANOVA) was performed. The result of the study revealed that the proposed approach is adequate to address the multi-objective optimization of micro-milling parameters.
Keywords: micro-milling, graphite, workpiece inclination angle, optimization, dimensional accuracy, surface quality, taguchi method, grey relational analysis
Published in DKUM: 20.01.2026; Views: 0; Downloads: 2
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Abstract: In an increasingly complex and turbulent global environment, achieving resilience in manufacturing supply chains has become a critical strategic priority. Drawing on a sample of 300 manufacturing firms, this study examines both the net and configurational effects of supply chain governance mechanisms and dynamic digital capabilities on supply chain resilience. Using structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA), the findings reveal that: Contractual governance, relational governance, digital sensing capability, digital resource integration, digital-driven innovation, and digital-enabled business capabilities each have a positive impact on manufacturing supply chain resilience. In the overall sample, only relational governance demonstrates a relatively strong individual effect, while none of the six governance or digital capability dimensions serve as necessary conditions for high resilience in subsample analyses. For high-tech manufacturing firms, two resilient configurations are identified: 1) basic digital enablement with strong governance synergy, and 2) advanced digital enablement with strong governance synergy. In contrast, non-high-tech firms exhibit three distinct resilient configurations: 1) digital integration–driven, 2 advanced digital enablement with relational governance dominance, and 3) dual-core digital enablement with robust governance synergy. These insights provide nuanced theoretical contributions and practical implications for configuring governance and digital strategies to build sustainable supply chain resilience in the manufacturing sector.
Keywords: manufacturing supply chain, sustainable supply chain resilience, supply chain governance, dynamic digital capability, fuzzy-set qualitative comparative analysis, configuration analysis
Published in DKUM: 19.01.2026; Views: 0; Downloads: 0
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Abstract: Laterally loaded slender piles present a classic soil–structure interaction problem where pile displacements and flexural demands are governed by the mobilized lateral resistance of the surrounding soil and the axial-bending capacity of the reinforced concrete section. In response to increasing pressure to reduce embodied emissions, this study develops LAVERCO, an optimization framework for cost- and CO2-efficient design of bored reinforced-concrete piles in cohesive soils subjected to combined lateral and axial actions. The framework integrates Eurocode-based geotechnical checks with full N–M section verification of the RC pile and applies a genetic algorithm over a multi-parametric grid of lateral load, vertical load, and undrained shear strength, using economic cost and embodied CO2 as alternative single objectives. Rank-based (Spearman) sensitivity analysis quantifies how actions, soil strength, and design variables influence the optimal solutions. The results reveal two consistent geometry regimes: CO2-optimal piles are systematically longer and slimmer, while COST-optimal piles are shorter and thicker. In both cases, the objective is dominated by pile length and is reduced by higher undrained shear strength; vertical load has a moderate direct effect, while horizontal load contributes mainly through deflection and bending checks. Feasibility improves significantly in stronger clays, and CO2-optimal geometries generally incur higher costs, clarifying the trade-off between economic and environmental performance. The framework provides explicit geometry-level guidance for selecting bored pile designs that balance cost and embodied CO2 across a wide range of soil and loading conditions and can be directly applied in both preliminary and detailed designs.
Keywords: laterally loaded pile, reinforced-concrete piles, structural analysis, reinforced-concrete design, optimization, CO2 emissions, genetic algorithm, multiparametric analysis, civil engineering practice
Published in DKUM: 19.01.2026; Views: 0; Downloads: 4
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Abstract: This paper examines the controllability of the stator and rotor magnetic flux linkages of doubly fed induction machines (DFIMs), considering small-signal oscillations and their relationship with electromagnetic machine design. To this aim, we first analyze 66 sets of lumped parameters from the dynamic model of a DFIM. Based on this, we classify DFIMs into three categories based on their modal parameters, namely, two for wind applications and one for pumped-hydro applications. Additionally, we identify reciprocal relationships between the damping of both identified oscillatory modes and the largest eigenvalues of the stator and rotor controllability Gramian matrices. Among the different lumped parameters, the winding resistances are found to influence these relationships the most. All analyzed DFIMs for wind applications were found to be less controllable and to exhibit better damping of oscillatory modes than DFIMs for pumped-hydro applications. Furthermore, based on the results of the controllability analysis, we selected three objective function candidates suitable for an optimization procedure aimed at minimizing the energy required to control the rotor flux linkages. An example objective function was evaluated in a multi-objective DFIM design process for wind applications that enables the design of more controllable machines that provide the desired damping of oscillations, which can consequently contribute to improving grid resilience.
Keywords: contorollability, design optimization, doubly fied indusction generators, modal analysis
Published in DKUM: 14.01.2026; Views: 0; Downloads: 2
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