Search Results (2,602)

This study presents a deep learning-based framework for forecasting energy demand in a quicklime production company, aiming to enhance operational efficiency and enable data-driven decision-making for industrial scalability. Using one year of real electricity consumption data, the methodology integrates temporal and operational variables—such as load profile, active power, shift indicators, and production-related proxies—to capture the dynamics of energy usage throughout the manufacturing process. Several neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Conv1D models, were trained and compared to predict short-term power demand with 10-min resolution. Among these, the GRU model achieved the highest predictive accuracy, with a best performance of RMSE = 2.18 kW, MAE = 0.49 kW, and SMAPE = 3.64% on the test set. The resulting forecasts support cost-efficient scheduling under time-of-use tariffs and provide valuable insights for infrastructure planning, capacity management, and sustainability optimization in energy-intensive industries. Full article

This study investigated the statistical association between public incentives and industrial innovation as reflected in firms’ financial performances. In particular, the analysis was carried out considering a Regional Operational Program, namely, the 2007–2013 ERDF Regional Program in Lombardy, and investigating a dataset of Lombardy-based companies that received support through the mentioned initiative. For each of them, balance sheet variables before and after the acquisition of the incentive and the development of the related innovation project were detected and analyzed by means of both standard and normalized linear regression. Notably, normalized regressions showed that higher subsidy intensity was positively associated with subsequent changes in revenues and intangible assets, especially among manufacturing firms, thereby supporting policies that target sectors with a high innovation capacity. Furthermore, this research underscores the importance of tailoring policy instruments to local and sectoral contexts, recognizing the limitations of one-size-fits-all approaches. In keeping with this exploratory stance, this study does not build a counterfactual control group and makes no causal claims; it simply documents balance sheet associations that may inform future, impact-oriented research. Given the absence of a control group, the design is observational; all findings describe associations and do not allow causal inference. Full article

High-fidelity numerical models are widely used to study the behavior of complex structures in Structural Health Monitoring (SHM), but their high computational cost limits their use in stochastic settings such as fleet-level applications. In practice, fleets of engineering assets show natural variability due to differences in loading, materials, and manufacturing, making them inherently stochastic. To address these challenges, this work develops a probabilistic surrogate model based on conditional variational autoencoders (CVAEs). The CVAE is trained to reconstruct the high-dimensional boundary response field of a critical structural region while explicitly conditioning on operational and structural parameters. By learning a latent probabilistic representation, the model explains the behavior of all individual members of a homogeneous population. Synthetic training and testing data are generated using a finite element model together with an aerodynamic panel model of a UAV. Results show that the CVAE can efficiently reproduce the spatial and stochastic features of the system response, providing accurate approximations at a fraction of the computational cost of high-fidelity simulations. Full article

Quiet drivetrains have become a central requirement in modern electric vehicles, where the absence of engine masking makes even subtle gear tones clearly audible. As a result, manufacturers are looking for more reliable ways to understand how design choices, manufacturing variability, and operating conditions shape gear noise and vibration. Digital Twin (DT) approaches—linking high-fidelity models with measured data throughout the product lifecycle—offer a potential route to achieve this, but their use in gear NVH is still emerging. This review examines recent work from the past decade on DT concepts applied to gears and drivetrain NVH, drawing together advances in simulation, metrology, sensing, and data exchange standards. The survey shows that several building blocks of an NVH-oriented twin already exist, yet they are rarely combined into an end-to-end workflow. Clear gaps remain. Current models still struggle with high-frequency behavior. Real-time operation is also limited. Manufacturing and test data are often disconnected from simulations. Validation practices lack consistent NVH metrics. Hybrid and surrogate modeling methods are used only to a limited extent. The sustainability benefits of reducing prototypes are rarely quantified. These gaps define the research directions needed to make DTs a practical tool for future gear NVH development. A research Gap Map is presented, categorizing these gaps and their impact. For each gap, we propose actionable future directions—from multiscale “hybrid twins” that merge test data with simulations, to benchmark datasets and standards for DT NVH validation. Closing these gaps will enable more reliable gear DTs that reduce development costs, improve acoustic quality, and support sustainable, data-driven NVH optimization. Full article

Fused deposition modeling (FDM) is one of the most widely adopted additive manufacturing (AM) technologies due to its accessibility and versatility; however, ensuring process reliability and product quality remains a significant challenge. This work introduces a novel methodology to evaluate the fidelity of programmed extruder head trajectories and speeds against those executed during the printing process. The approach integrates infrared thermography and image processing. A type-V ASTM D638-14 polylactic acid (PLA) specimen was fabricated using 16 layers, and its G-code data were systematically compared with kinematic variables extracted from long-wave infrared (LWIR) thermal images. The results demonstrate that the approach enables the detection of deviations in nozzle movement, providing valuable insights into layer deposition accuracy and serving as an early indicator for potential defect formation. This thermal image–based monitoring can serve as a non-invasive tool for in situ quality control (QC) in FDM, supporting process optimization and improved reliability of AM polymer components. These findings contribute to the advancement of smart sensing strategies for integration into industrial additive manufacturing workflows. Full article

In light of ongoing advancements in smart manufacturing, there is a growing need for intelligent fault diagnosis methods that maintain reliability under noisy, high-variability operating conditions. Conventional feature selection strategies often struggle when data contain outliers or suboptimal feature subsets, limiting their diagnostic utility. This study introduces a density-based feature space optimization (DBFSO) framework that integrates feature selection with localized density estimation to enhance feature space separability and classifier efficiency. Using k-nearest neighbor density estimation, the method identifies and removes low-density feature vectors associated with noise or outlier behavior, thereby sharpening the feature space and improving class discriminability. Experiments using roll-to-roll (R2R) manufacturing data under mechanical disturbances demonstrate that DBFSO improves classification accuracy by up to 36–40% when suboptimal feature subsets are used and reduces training time by 60–71% due to reduced feature space volume. Even with already-optimized feature sets, DBFSO provides consistent performance gains and increased robustness against operational variability. Additional validation using a bearing fault dataset confirms that the framework generalizes across domains, yielding improved accuracy and significantly more compact, noise-resistant feature representations. These findings highlight DBFSO as an effective preprocessing strategy for intelligent fault diagnosis in intelligent manufacturing systems. Full article

Background: The compaction of formulation blends is a critical stage in pharmaceutical tablet manufacturing, particularly when drug substances or functional excipients exhibit limited flowability and tabletability. Objectives: This study systematically examined the mechanical behaviour of viscoelastic microcrystalline cellulose (MCC) and brittle anhydrous dibasic calcium phosphate (DCPA), as well as their mixtures, to check how deformation mechanisms influence powder handling and tablet performance. Methods: A compaction simulator, mimicking a small rotary tablet press, was used to evaluate tablet weight variability, densification profiles, die-filling height, force–displacement behaviour, and in-die Heckel analysis. Additional assessments included compression times, breaking force, tensile strength, elastic recovery, as well as in-die and out-of-die tablet thickness across various compositions and compaction pressures. Results/Conclusions: Bulk density values from the simulator showed strong correlation with pharmacopeial measurements (R² ≥ 0.997). Measurable differences in true density and cohesiveness led to poor flowability for MCC and good flow for DCPA, with mixtures containing higher DCPA concentration displaying markedly improved flow characteristic. Compaction analyses confirmed extensive plastic deformation for MCC and fragmentation for DCPA. Increasing MCC content elevated die-fill height, compaction energy, and tablet weight variability, whereas higher DCPA fractions decreased apparent density of tablets and reduced energy demand. Tabletability and compressibility profiles reflected that MCC generated hard tablets but exhibited higher elastic recovery, while DCPA formed softer tablets with closer to linear strength–pressure relationships. Energy profiling demonstrated that MCC stored more elastic energy and required higher overall compression work, whereas DCPA reduced elastic accumulation. Overall, blending viscoelastic and brittle excipients offers a robust strategy for optimizing manufacturability, mechanical strength, and energy efficiency in tablet production. Full article

With the continuous development of semiconductor manufacturing technology and information technology, the sizes of wafer chips are becoming smaller and the variety is increasing, which has put forward high requirements for wafer chip precision manufacturing and packaging workshops. On the one hand, the market demand for multiple varieties and small batches will increase the difficulty of scheduling. On the other hand, the complex manufacturing environment brings various types of dynamic events that will disrupt production plans. Accordingly, this work researches the wafer chip precision packaging workshop rescheduling problem under events of machine breakdown, emergency order inserting and original order modification. Firstly, the mathematical model for the addressed problem is established, and the rolling horizon technology is adopted to deal with multiple dynamic events. Then, a hybrid algorithm combining an improved firefly optimization framework and variable neighborhood search strategy is proposed. The population evolution mechanism is designed based on the location-updating law of fireflies in nature. The variable neighborhood search is applied for avoiding local optima and sufficiently exploring in the neighborhood. At last, the test results of comparative experiments and engineering cases indicate that the proposed method is effective and stable and is superior to the current advanced algorithms. Full article

In manufacturing processes, both material processing methods and the resulting microstructure play a fundamental role in determining material behavior during component fabrication and subsequent service conditions. Materials produced by additive manufacturing exhibit a unique microstructure due to the rapid heating and solidification cycles inherent to the process, distinguishing them from conventionally cast counterparts and leading to differences in mechanical and functional properties. This article presents problems related to the longitudinal turning of Ti6Al4V titanium alloy elements produced by the casting and powder laser sintering (DMLS) methods. The authors made an attempt to establish a procedure for determining the optimal parameters of finishing cutting while minimizing the specific cutting force, taking into account the criterion of machined surface quality. In the course of the experiments, the influence of the cutting data on the cutting force values, surface roughness parameters, and chip shape was examined. The material hardening state during machining and the variability of the specific cutting force as a function of the cross-sectional shape of the cutting layer were also tested. The authors presented a practical application of the proposed optimization algorithm. It was found that by changing the shape of the cross-section of the cutting layer, it was possible to carry out the turning process with significantly reduced specific cutting force (from 2300 N/mm² to 1950 N/mm²) without deteriorating the surface roughness. Full article

The papermaking and recycling industries face increasing demands to improve efficiency, product quality, and environmental performance under conditions of water closure and high furnish variability. This study presents a comprehensive assessment of process control and management strategies for optimizing fines behavior, retention and fixation efficiency, de-inking performance, and ash balance in modern papermaking systems. The surface chemistry of fines was found to play a pivotal role in regulating charge distribution, additive demand, and drainage behavior, acting both as carriers and sinks for dissolved and colloidal substances. Results show that light, targeted refining enhances external fibrillation and produces beneficial fines that strengthen fiber bonding, while excessive refining generates detrimental fines and impairs drainage. Sequential retention programs involving polyamines, polyaluminum compounds, and microparticle systems significantly improve fines capture and drainage stability when operated under controlled pH and ionic strength. In recycling operations, optimized flotation conditions coupled with detackifiers and mineral additives such as talc effectively reduce micro-stickies formation and deposition risks. Ash management strategies based on partial purge and coordinated filler make-up maintain bonding, optical properties, and energy efficiency. Overall, the findings emphasize the need for an integrated wet-end management framework combining chemical, mechanical, and operational controls. Perspectives for future development include the application of biodegradable additives, nanocellulose-based reinforcements, and data-driven optimization tools to achieve sustainable, high-performance paper manufacturing. Full article

In this study, simulations and analyses of arc characteristics in EP (positive polarity) and EN (negative polarity) stages (including the arc polarity transition stage) of variable polarity cold metal transition (VP-CMT) during arc additive manufacturing of aluminum alloys are carried out. Temperature field, potential field, and current density distribution of arc plasma at different stages are systematically investigated by establishing a numerical model of arc heat–force coupling in combination with single-layer single-pass additive manufacturing experiments. The results indicate that the arc’s high-temperature zone in EP stage shows the wider distribution range, with enhanced heat transfer efficiency, reaching a surface temperature of up to 11,555.8 K at 2 mm from the substrate. In contrast, the arc during the EN stage demonstrates a more concentrated high-temperature zone, attributed to a more pronounced electromagnetic contraction effect, resulting in reduced heat input and a lower peak substrate temperature in comparison with EP stage. As revealed by analysis of potential and current density distribution, the arc in EP stage shows the “bell-shaped” expansion pattern with widely distributed current density, whereas the EN stage arc displays a “wrapped” contraction pattern with a more concentrated current density. The transition from EN to EP stage exhibits greater arc stability than the reverse transition. Moreover, electrode spacing significantly influences arc characteristics; a reduction in spacing leads to a more focused high-temperature zone and a substantial increase in peak current density. This study elucidates the dynamic variations in heat transfer behavior between the EP and EN stages, offering a theoretical foundation for optimizing process parameters in aluminum alloy arc additive manufacturing. Full article

This paper investigates the impact of corporate ESG performance on internal pay gaps using data from Chinese A-share listed companies from 2013 to 2023. Our study finds that, after controlling for relevant variables and fixed effects for firms and years, corporate ESG performance significantly widens the internal pay gap. To address endogeneity concerns, we use policy shocks, construct instrumental variables with the number of ESG investment fund holdings, and apply propensity score matching methods, all of which support our main findings. Furthermore, the negative impact of ESG performance on internal pay equality is mainly driven by compensation incentives and corporate financialization. Heterogeneity analysis shows that the negative effect of ESG performance on internal pay gaps is less pronounced in state-owned enterprises (SOEs) and non-manufacturing firms. Additionally, charitable donations and strengthened agency mechanisms can effectively mitigate excessive internal pay gaps. This paper offers a novel theoretical perspective on corporate sustainable development and provides significant implications for internal pay policy formulation and governmental policies aimed at reducing income inequality. Full article

Infant formulas are specialized foods designed for babies and toddlers who cannot be exclusively breastfed. However, acrylamide (AA) may form during the thermal processing involved in their production. Although chromatographic techniques offer high sensitivity and detection capability for AA analysis, their application remains limited due to the complexity of diverse food matrices, high operating costs, time requirements, and environmental concerns. A new validated liquid chromatography–mass spectrometry (LC-MS) protocol for AA detection in infant formula was developed using sequential hydration, acetonitrile (ACN) precipitation, and dual-sorbent clean-up, which minimized matrix effects and ensured clarity and high reproducibility. The validated method demonstrated excellent linearity (R² = 0.9985, solvent-based; 0.9903, matrix-based), a pronounced matrix effect (−67%), satisfactory sensitivity (limit of detection, LOD: 10 µg/kg; limit of quantification, LOQ: 20 µg/kg), and consistent recovery (82–99%) with less than 15% variation. AA analysis was performed on 31 infant formula samples. The highest individual AA level (268.2 µg/kg) was detected in an amino acid-based formula intended for infants under one year of age while the highest mean concentration was found in cereal-based samples (188.1 ± 100.8 µg/kg), followed by goat’s milk-based (52.7 ± 25.67), plant-based (48.8 ± 31.68), and cow’s milk-based (27.5 ± 29.62) formulas (p < 0.001). The wide variability in AA concentrations among infant formulas can be attributed to differences in formulation, ingredient composition, manufacturing processes, and analytical methodologies. These findings highlight the need for continuous monitoring of AA levels in infant foods to ensure their safety. Full article

This study develops a unified continuum damage mechanics (CDM) model for high-cycle fatigue life prediction of large manually arc-welded flange shafts manufactured from 45Mn steel (quenched and tempered) under combined bending–torsion loading. Fatigue tests revealed consistent crack initiation at the weld toe, with multiaxial loading reducing fatigue life by 35–42% compared to pure bending. The CDM parameters were calibrated against experimental data and implemented through an ABAQUS 2021 UMAT subroutine, achieving prediction errors below 5%—significantly outperforming conventional nominal and hotspot stress methods. For high-cycle fatigue conditions, a simplified CDM model neglecting plastic damage maintained engineering accuracy while improving computational efficiency by 3–5 times. The damage variable D = 0.9 was identified as a universal threshold for accelerated damage progression. These findings provide quantitative basis for multiaxial fatigue design and structural health monitoring of large welded components. Full article

The increasing number of wind turbines in sparsely populated areas poses significant challenges to the security of the power system. The need for centralized control of unstable sources such as wind turbines and PV installations makes it significantly more difficult to maintain operational stability. The subject matter of wind power plant research can be divided into three groups: wind prediction and productivity forecasts, optimization of the energy generation process, and the impact of power plants on the system and the environment. The subject matter of this article falls within the scope of research on the first group, namely, the impact of wind on actual power production. This study presents the results of a year-long investigation of the Enercon E48 wind turbine located in northeastern Poland. The influence of wind speed and direction on the actual turbine output was analyzed and compared with the manufacturer’s power curve. The findings indicate that the actual performance of the turbine exhibits greater variability than suggested by catalog data, with local conditions and seasonal effects exerting a significant influence on its efficiency. Full article