Search Results (1,970)

In Liquid Composite Molding (LCM), the high variability present in reinforcement properties such as permeability creates additional challenges during the injection process, such as void formation. Although improved injection strategy designs can mitigate the formation of defects, these processes can benefit from real-time process monitoring and control to adapt the injection conditions when needed. In this study, a machine vision algorithm is proposed, with the objective of detecting and tracking both fluid flow and bubbles in an LCM setup. In this preliminary design, 3D-printed porous geometries are used to mimic the architecture of textile reinforcements. The results confirm the applicability of the proposed approach, as the detection and tracking of the objects of interest is possible, without the need to incur in elaborate experimental preparations, such as coloring the fluid to increase contrast, or complex lighting conditions. Additionally, the proposed approach allowed for the formulation of a new dimensionless number to characterize bubble transport efficiency, offering a quantitative metric for evaluating void transport dynamics. This research underscores the potential of data-driven approaches in addressing manufacturing challenges in LCM by reducing the overall process monitoring complexity, as well as using the acquired reliable data to develop robust, data-driven models that offer new understanding of process dynamics and contribute to improving manufacturing efficiency. Full article

In additively manufactured (AM) Ti-6Al-4V, the role of martensitic α′ in governing brittleness versus toughness remains debated, largely because complex thermal histories and other intertwined physical factors complicate interpretation. To isolate and clarify the intrinsic effect of cooling rate, we employed a Gleeble thermal simulator, which enables precisely controllable cooling rates while simultaneously achieving ultra-fast quenching comparable to AM (up to ~7000 °C/s). By varying the cooling rate only, three distinct microstructures were obtained: α/β, α_m/α′, and fully α′. Compression tests revealed that the ultra-fast-cooled fully martensitic Ti-6Al-4V attained both higher strength and larger fracture strain, with densely distributed elongated dimples indicative of ductile failure. Three-dimensional microstructures reconstructed from microscopy, analyzed using an EVP-FFT crystal plasticity model, demonstrated that refined α′ laths and abundant high-angle boundaries promote more homogeneous strain partitioning and reduce stress triaxiality, thereby delaying fracture. These results provide potential evidence that extreme-rate martensitic transformation can overcome the conventional strength–ductility trade-off in Ti-6Al-4V, offering a new paradigm for processing titanium alloys and AM components with superior performance. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article

With the rapid development of Industry 5.0, smart manufacturing has become a key focus in production systems. Hence, achieving efficient planning and scheduling on the shop floor is important, especially in job shop environments, which are widely encountered in manufacturing. However, traditional job shop scheduling problems (JSP) assume fixed operation sequences, whereas in modern production, some operations exhibit sequence flexibility, referred to as sequence-free operations. To mitigate this gap, this paper studies the JSP with discrete operation sequence flexibility (JSPDS), aiming to minimize the makespan. To effectively solve the JSPDS, a mixed-integer linear programming model is formulated to solve small-scale instances, verifying multiple optimal solutions. To enhance solution quality for larger instances, a digital twin (DT)–enhanced initialization method is proposed, which captures expert knowledge from a high-fidelity virtual workshop to generate high-quality initial population. In addition, a statistical learning-assisted local search method is developed, employing six tailored search operators and Thompson sampling to adaptively select promising operators during the evolutionary algorithm (EA) process. Extensive experiments demonstrate that the proposed DT-statistical learning EA (DT-SLEA) significantly improves scheduling performance compared with state-of-the-art algorithms, highlighting the effectiveness of integrating digital twin and statistical learning techniques for shop scheduling problems. Specifically, in the Wilcoxon test, pairwise comparisons with the other algorithms show that DT-SLEA has p-values below 0.05. Meanwhile, the proposed framework provides guidance on utilizing symmetry to improve optimization in complex manufacturing systems. Full article

Due to the complexity of shipyards’ operating scenes and the inconsistency of ship parts’ type and size, current sorting operations for ship parts mainly rely on laborers, resulting in weak control over the production process and key nodes. With the gradual advancement of intelligent manufacturing technology in the shipbuilding process, the trend of machines replacing humans is obvious. In order to promote the automation of the sorting process, intelligent scene recognition and route planning algorithms are needed. In this work, we introduce a localization method based on a laser line profile sensor and ship parts layout analysis algorithm, aiming at obtaining the information needed for sorting route planning. In addition, a heuristic-based route planning algorithm is proposed to solve the built mathematical model of the ship part sorting process. The proposed method can optimize the sorting order of parts, realize stable stacking, shorten sorting distance (taking about 490 m for 43 parts), and thereby improve operation efficiency. These results show that the proposed approach can make intelligent and comprehensible sorting route planning for the ship parts layout. Full article

Additive manufacturing involves numerous independent parameters, often leading to inconsistent print quality and necessitating costly trial-and-error approaches to optimize input variables. Machine learning offers a solution to this non-linear problem by predicting optimal printing parameters from a minimal set of experiments. Using Fused Deposition Modeling (FDM) as a case study, this work develops a machine learning-powered process to predict porosity defects. Specimens in two geometrical scales were 3D-printed and CT-scanned, yielding raw datasets of grayscale images. A machine learning image classifier was trained on the small-cube dataset (~2200 images) to distinguish exploitable images from defective ones, averaging over 97% accuracy and correctly classifying more than 90% of the large-cube exploitable images. The developed preprocessing scripts extracted porosity features from the exploitable images. A repeatability study analyzed three replicate specimens printed under identical conditions, and quantified the intrinsic process variability, showing an average porosity standard deviation of 0.47% and defining an uncertainty zone for quality control. A multi-layer perceptron (MLP) was independently trained on 1709 data points derived from the small-cube dataset and 3746 data points derived from the large-cube dataset. Its accuracy was 54.4% for the small cube and increased to 77.6% with the large-cube dataset, due to the larger sample size. A rigorous grouped k-fold cross-validation protocol, relying on splitting data per cube, strengthened the ML algorithms against data leakage and overfitting. Finally, a dimensional scalability study further assessed the use of the pipeline for the large-cube dataset and established the impact of geometrical scaling on defect formation and prediction in 3D-printed parts. Full article

Over millions of years of evolution, nature provided tools to optimize different functions in animals and plants. Different strategies observed in nature serve as models for solving complex engineering problems. Additive manufacturing (AM), also known as 3D printing, enables us to produce shapes that would not be possible with traditional subtractive manufacturing. In this way, it is possible to produce complex detailed shapes using an automatic process. Biomimetics involves drawing inspiration from nature and applying it to solve specific engineering challenges, often with the goal of optimization and enhanced performance. Three-dimensional printing enables the replication of complex natural shapes, opening new avenues for innovation. In this paper, we review the state of the art in biomimetics, including studies on mechanical properties, design strategies, manufacturing techniques, and the use of composites. Full article

Additively manufactured thermoplastic polyurethane (TPU 95A) is widely used in engineering, yet its machining behavior remains insufficiently explored. This study investigates the post-processing machinability of FDM-fabricated TPU 95A using CNC milling, with a particular focus on material removal rate (MRR) and surface roughness (Ra). A full factorial design of experiments (81 runs) is conducted, considering four input parameters such as spindle speed (N; 2000, 4000, 6000 rpm) and feed rate (F; 100, 200, 300 mm/min) on the CNC vertical machining center, together with infill density (ϕ; 33%, 66%, 100%) and layer thickness (LT; 1.0, 1.5, 2.0 mm). MRR is modeled and optimized across all densities, achieving strong fit (R² = 0.94; Adj-R² = 0.93). The optimum conditions are found to be MRR ≈ 1251 mm³/min at F = 300 mm/min, ϕ = 100%, N ≈ 3500 rpm and LT ≈ 1.05 mm. Ra can only be measured for 100% infill specimens, as lower infill surfaces violate profile measurement requirements. Its regression model shows weak explanatory power (R² = 0.14; Adj-R² = 0.03) and is excluded from optimization. Instead, Ra is reported descriptively: milling reduced roughness from ≈25–30 μm (as-printed) to ≈13.8 μm under favorable conditions. Overall, the study highlights machining’s role in the hybrid manufacturing practice. Full article

Fused deposition modeling (FDM) is a widely used additive manufacturing (AM) technology, favored for its design flexibility and suitability for low-volume production. However, precise cost estimation remains a critical challenge, particularly in industrial environments where decision-making depends on accurate financial assessments. This study proposes a comprehensive, parameter-based cost calculation model for FDM processes, with a special focus on the wear of machine tooling. Unlike conventional methods, the model separates tooling costs from general machine operation costs and introduces a novel approach to nozzle wear estimation based on extruded material volume rather than printing time. The framework incorporates key cost components—including material usage, support removal, machine operation, tooling degradation, and labor—and links them to quantifiable parameters such as part volume, build time, and energy consumption. The methodology was tested across multiple scenarios with different geometries and production volumes, revealing significant differences between time- and volume-based wear calculations. The results demonstrate that the proposed model provides more accurate and adaptable cost predictions, especially in varied production settings. This approach enhances the financial transparency of FDM workflows and supports better-informed decisions in both prototyping and small-batch manufacturing contexts. Full article

The stability and uniformity of a liquid line formed by the sequential deposition of droplets are essential to the quality of products in many industry applications. In this work, a numerical model based on the front tracking method (FTM) is developed to investigate the displacement dynamics of sequential droplets on wetting confinement. We systematically examine the impact of wetting conditions and confinement width on the spreading length, morphology, and confined angle for a droplet. In addition, an analytical model is derived to predict the droplet displacement spacing for a uniform line. The analytical results align well with the numerical results, and the sequential droplets displaced with the predicted space achieve the minimum cross-section error and exhibit enhanced uniformity. Our numerical and analytical studies of droplet displacement within wetting confinement provide fundamental insights and a predictive framework for enhancing the uniformity and stability of liquid lines in precision manufacturing processes. Full article

Fused Deposition Modeling (FDM), also known as Fused Filament Fabrication (FFF), is a widely used additive manufacturing (AM) method for thermoplastic materials due to its low cost, accessibility, and ability to produce fully functional machine parts. Cylindrical components, common in mechanical devices, require precise dimensional and form accuracy to ensure long service life. To assess their quality, cylindricity deviation measurements are essential, as they reveal defects generated during the printing process. This study investigates the potential of optical scanning for measuring form deviations specifically cylindricity and roundness of ABS components manufactured via FDM. The influence of printing orientation (0°, 45°, 90°) on dimensional accuracy was examined using experimental models comprising three series of ten samples each, with identical process parameters except orientation. Measurements were performed using a Zeiss Prismo Navigator (Zeiss, Oberkochen, Germany) coordinate measuring machine and an ATOS II Triple Scan (GOM, Brunswick, Germany) optical scanner. Results indicate that print orientation significantly affects cylindricity deviation. The lowest deviations were achieved for specific orientations, offering guidelines for producing cylindrical surfaces of acceptable quality. The findings also show that optical scanners are not suitable for precise form deviation analysis in FDM-printed parts, confirming the higher accuracy of tactile coordinate measurement methods. Full article

High-precision control of robotic manipulators plays a vital role in improving the efficiency and quality of industrial manufacturing. However, the inherent nonlinear and time-varying characteristics of robotic systems make high-accuracy control a challenging task, and external noise interference further complicates reliable state estimation. Conventional time-delay control methods often involve computationally intensive procedures for gain determination and are limited in their ability to suppress noise effectively. To overcome these limitations, this paper proposes a robust time-delay control strategy based on an improved state observer. By deriving a linearized form of the dynamic model, an offline computation scheme for control gain determination is developed, which eliminates the need for additional tuning parameters and simplifies the design process. Furthermore, the proposed state observer integrates model reference estimation with noise suppression techniques, enabling accurate acquisition of joint states and improving system robustness under noisy conditions. Experimental results validate the effectiveness of the proposed method, showing that it can efficiently determine control gains and significantly outperform existing advanced approaches in terms of trajectory tracking accuracy and overall control performance. Full article

This study pioneered an intelligent process optimization framework integrating dimensionality reduction and Box–Behnken Design response surface methodology (RSM) with MATLAB R2021b(v9.11) analytics, to advance the development of functional foods from red vinasse-blue round scad. The comprehensive nutraceutical stability assessment for key functional lipids during 4 °C storage were established by systematically evaluating microwave, boiling, and foil-baking processing. The results of intelligent processing optimization showed that the optimal parameters (red vinasse addition: 2.8 g/g; processing temperature: 4 °C; processing time: 10 h) maximized the composite quality score Y (50% texture + 50% sensory), validated by MATLAB R2021b(v9.11) to achieve near-theoretical maxima. The results of functional lipid stability showed that total fat decreased significantly (p < 0.05), with foil-baking showing the highest loss. Partial least squares regression (PLSR) analysis revealed critical degradation of nutraceutical lipids (C20:5n-3, C22:6n-3) and an increase in saturated fats (p < 0.05), where boiling induced the most severe fatty acid alterations. Microwave processing accelerated lipid oxidation (highest TBARS, p < 0.05), compromising lipid bioactivity. The framework of red vinasse biosynthesis technology enabled precise parameter optimization, and enhanced functional component retention in underutilized fish species. This work provided a theoretical and technical foundation for intelligent manufacturing of lipid-stable nutraceuticals, positioning red vinasse—a model biosynthesis technology output—as a key ingredient for next-generation functional foods. Full article

Generative artificial intelligence (genAI) plays a crucial role in improving AI-based digital twins (DTs), enabling more dynamic, adaptive, and accurate industrial simulations, essential as Industry 5.0/6.0 paradigms evolve and are implemented. In industry, genAI can simulate complex manufacturing processes or entire production lines, enabling companies to optimize operations, predict maintenance needs, reduce downtime, and develop more scenarios for correct operation (e.g., for faster transitions to new products or new materials) and address potential failures. GenAI also helps DTs continuously learn and evolve by generating new data and scenarios based on historical and current inputs. This capability ensures that DTs remain current and reflective of the real systems they represent, for both operational and training purposes (e.g., training operators for situations that rarely occur on a real production line).Furthermore, it facilitates the creation of synthetic data, which is important for training AI models when real-world data is scarce or expensive. This accelerates the development and improvement of DTs and increases the predictive accuracy, personalization, and operational efficiency of AI-based digital twins, making them more reliable and versatile tools in medicine and industry. However, in addition to strengths, it is also worth considering threats to prepare for risk mitigation. This article helps capture and maintain a balance between opportunities and threats in this area. Full article

Background/Objectives: Among different milling techniques, spiral air jet milling can produce finer particles without the use of solvents or additives, thereby improving the bioavailability and content uniformity of the final dosage form. However, milling can complicate downstream processability of active pharmaceutical ingredients (APIs) due to reduced bulk powder flowability and post-milling lump formation. Process settings are often optimized only for particle size reduction, without sufficient consideration of manufacturability, largely because of limited API availability and a lack of knowledge about influential material properties. This study aimed to investigate the impact of material properties and process settings on milling performance and downstream manufacturability. Methods: Four APIs, examined in a total of eight grades, were characterized for their bulk mechanical properties and compression energy parameters using a compaction simulator. These grades were subjected to milling experiments within a design-of-experiments framework. Statistical analyses were performed, and population balance models (PBMs) were developed and calibrated for each experiment to link material properties and process settings to milling outcomes. Results: A higher gas flow rate was identified as the most significant contributor to particle size reduction. The influence of mechanical properties, particularly Young’s modulus and Poisson’s ratio, was evident and correlated with unmilled particle sizes. PBM analyses showed that a higher gas feed rate decreased the critical particle size for breakage, while intrinsic mechanical properties affected the breakage rate function. Conclusions: By integrating material properties and process settings into PBM analyses, specific breakage mechanisms could be identified. These findings provide a framework for optimizing jet milling not only for particle size reduction but also for downstream processability of APIs. Full article