Search Results (1,644)

This paper introduces a novel approach to anomaly-based failure forecasting that jointly optimizes both the ensemble function and the anomaly threshold used for decision making. Unlike conventional methods that apply fixed or classifier-defined thresholds, the proposed framework simultaneously tunes the threshold of the failure probability or anomaly score and the parameters of an ensemble function that integrates multiple machine learning models—specifically, Random Forest and Isolation Forest classifiers trained under diverse preprocessing configurations. The distinctive contribution of this work lies in introducing a weighted mean ensemble function, whose coefficients are co-optimized with the anomaly threshold using a global optimization algorithm, enabling adaptive, data-driven decision boundaries. The method is designed for predictive maintenance applications and validated using sensor data from three industrial domains: aluminum anode production, plastic injection molding, and automotive manufacturing. The experimental results demonstrate that the proposed combined optimization significantly enhances forecasting reliability, improving the Matthews Correlation Coefficient by up to 6.5 percentage units compared to previous approaches. Beyond its empirical gains, this work establishes a scalable and computationally efficient framework for integrating threshold and ensemble optimization in real-world, cross-industry predictive maintenance systems. Full article

3D printing offers great potential for rapid and cost-effective fabrication of microfluidic lab-on-a-chip systems. Through a comparative approach, we implemented staggered herringbone mixer (SHM), Tesla mixer, and split and recombine mixer (SAR), along with a basic unperturbed channel into one chip and performed comparative mixing efficiency experiments. We also introduced a phaseguide-based, T-shaped stop structure at the Y-shaped inlets for bubble-free and parallel filling. The structures were analyzed with two poorly mixable dye solutions at flow rates ranging from 1 µL/min to 200 µL/min. The mixing efficiency was evaluated using optical gray value analysis and compared against diffusion-based mixing. The fluid-aligning phaseguides in the 3D-printed system were shown to work. Among the three different mixing structures tested, SHM exhibited the best mixing efficiency at all tested flow rates. Uniformly designed SHM structures contain a region of poor mixing between the two zones of turbulence. In a non-uniform design, fluid breakers were placed between two SHM units to redirect poorly mixed fluids to the edges, resulting in 100% mixing efficiency across all measured flow rates. These results, especially SHM with fluid breakers, support the development of cost-effective injection-molded lab-on-a-chip systems with improved mixing functionalities at close range instead of simple long-length meandric systems. Full article

This study investigates the mechanical performance and surface morphology of polyamide-based materials commonly used in plastic injection molding. Two resins, PA6 and PA66, were analyzed in both neat and 20 wt% glass fiber-reinforced (GF20) forms. The influence of reinforcement and material type on tensile strength and ductility was examined through integrated experimental and numerical approaches, complemented by microstructural and elemental analyses. PA6 and PA66 specimens were produced in accordance with ISO 527, and tensile tests revealed a significant increase in elastic modulus and tensile strength with glass fiber reinforcement, accompanied by a reduction in elongation at break. Flammability was evaluated via Glow Wire and Tracking tests. SEM–EDS analyses provided insights into fracture morphology and elemental distribution, showing that fiber–matrix interfacial debonding and fiber pull-out dominated failure in reinforced specimens, whereas neat polymers exhibited homogeneous surfaces. Finite element simulations performed in ANSYS Explicit Dynamics supported the experimental findings by identifying stress concentration zones and failure initiation regions. Although numerical simulations successfully captured stress distribution trends, quantitative differences were attributed to idealized modeling assumptions and processing-induced microstructural effects. Overall, this work provides a comprehensive assessment of the reinforcement effects in PA6 and PA66 systems, offering valuable guidance for material selection and design optimization in polymer-based engineering components. Full article

Shrinkage of injection-molded parts is a major challenge for dimensional accuracy, especially for semi-crystalline polymers where crystallization induces pronounced volume change and heat release during cooling. Because packing pressure is effective only before gate or local solidification, multi-stage packing is commonly used to regulate the overall shrinkage behavior. In practice, however, the solidification/transition temperature taken from standard material tests does not necessarily represent the actual in-cavity state behavior under specific cooling rate and pressure history, which compromises the consistency of P–V–T-based shrinkage prediction. In this study, a modified P–V–T-based framework (Tait equation) is developed for polypropylene (PP) by introducing a Thermal Enthalpy Transformation Method (TETM) to determine a process-relevant solidification time and crystallization-completion temperature (including the corresponding target specific volume) directly from in-cavity melt temperature monitoring using an infrared temperature sensor. The novelty TETM utilizes the crystallization-induced enthalpy release to identify the temperature–time plateau, from which one can identify the effective solidification point. Because the Tait equation adopts a two-domain formulation (molten and solidified states), accurate identification of the domain-switching temperature is critical for reliable shrinkage prediction in practical molding conditions. In the experiment execution, the optimum filling time was defined using the minimum pressure required for melt-filling. Four target specific volumes, three melt temperatures, and two mold temperatures were examined, and a two-stage packing strategy was implemented to achieve comparable shrinkage performance under different target specific volumes. A conventional benchmark based on the solidification temperature reported in the Moldex3D material database was used for comparison only. The results show that the target specific volume determined by the TETM exhibits a more consistent and near-linear relationship with the measured shrinkage rate, demonstrating that the TETM improves the robustness of solidification-time identification and the practical usability of P–V–T information for shrinkage control. Full article

Plastic waste poses a growing environmental challenge due to the extensive use of plastics in packaging applications. Recycling plastics offers environmental and economic advantages. Wood flour-derived from cypress wood, often generated as a by-product and discarded in landfills, contributes to environmental In this study, wood–plastic composites were fabricated from recycled high-density polyethylene, wood flour, and high-density polyethylene with maleic anhydride-grafted polyethylene as a coupling agent. Five composite formulations were produced by varying the recycled high-density polyethylene and wood flour volume ratios and processed through injection molding. The mechanical properties, including flexural, tensile, and impact strengths, along with water absorption behavior and microstructural characteristics, were evaluated in accordance with relevant standards using a universal testing machine, Charpy impact test, and scanning electron microscopy. The results revealed that increasing the recycled high-density polyethylene content from 20% to 35% significantly improved the composite performance, reducing water absorption by 9.86% and enhancing flexural, tensile, and impact strengths by 43.33%, 36%, and 35.03%, respectively. Morphological analysis confirmed improved fiber–matrix interfacial adhesion with higher recycled plastic content. These findings demonstrate the potential of recycled high-density polyethylene wood composites as sustainable materials for structural applications, combining environmental benefits with enhanced mechanical performance. Full article

Powder Injection Molding (PIM) is a versatile manufacturing technology widely used for fabricating components with complex geometries from metals and ceramics, yet its application to high-performance thermoplastics remains underutilized. This study explores the feasibility of manufacturing products from Polyphenylene Sulfide (PPS)—a promising linear aromatic polymer synthesized in powder form—using PIM technology and investigates the development of PE-based feedstocks with PPS and solid fillers. Regarding the matrix formulation, it was found that using pure paraffin as a binder limited the maximum PPS content to 20%. Consequently, a modified binder system consisting of Low-Density Polyethylene (LDPE) and paraffin in a 70:30 wt.% ratio was utilized, which successfully increased the PPS loading in the feedstock to 50% and enabled stable molding. Following matrix optimization, the study examined composites incorporating various fillers, including chalk, talc, and carbon fibers. Systematic rheological analysis confirmed that these composite suspensions possess characteristics necessary for molding products with complex geometries. Key results indicate that optimal sintering conditions were established to achieve the required mechanical properties. Among the tested fillers, carbon fibers were the most effective reinforcement, increasing the elastic modulus by 33% and flexural strength by 20%. Representative examples of samples successfully manufactured via this approach are presented. Full article

Background/Objectives: This article presents a novel approach that combines the Palatal Silicone Key and Injection Molding techniques as a viable alternative for complex anterior cases with high esthetic demands, where layering multiple shades is essential to achieve a natural appearance, rather than using a single monochromatic composite. Methods: The Palatal Silicone Key technique utilizes a silicone index to transfer palatal and incisal anatomy from a diagnostic wax-up, allowing freehand layering of proximal and buccal surfaces with multiple composite shades. The Injection Molding technique provides a simpler and more predictable workflow by using a transparent silicone index to replicate the wax-up. However, the original injection technique relies on a single-shade composite, limiting the esthetic outcomes. In the presented case canines and first premolars were reshaped to replace congenitally missing lateral incisors. Palatal surfaces were built with medium-viscosity enamel shade composite using the silicone key, and dentin anatomy was sculpted freehand with dentin shade composite. Buccal anatomy was restored by injecting enamel shade flowable composite into the transparent index. Results: This combined protocol facilitated the precise transfer of the wax-up, minimizing adjustments, while the use of multiple composite shades reproduced the natural translucency of adjacent teeth, resulting in highly esthetic restorations. Conclusions: Handling traditional composites in complex anterior cases can be time-consuming and technique-sensitive. The presented combination of techniques, while requiring a high level of skill and precision, integrates the strengths of both approaches, enabling a minimally invasive, additive workflow with reduced clinical time and more predictable esthetic outcomes. Full article

To address the thermal management challenges in electronic devices, this study systematically investigates the effects of injection molding and compression molding on the microstructure and thermal conductivity of poly(vinylidene fluoride)/boron nitride nanosheet (PVDF/BNNs) composites. Using 10 μm diameter BNNs as thermal conductive fillers and PVDF as the matrix, the composites were characterized via scanning electron microscopy (SEM), thermal conductivity measurements, rheological analysis, X-ray diffraction (XRD), and mechanical tests. The results demonstrate that the strong shear stress in injection molding induces significant alignment of BNNs along the flow direction, leading to remarkable thermal conductivity anisotropy. At a PVDF/BNNs mass ratio of 90/10, the in-plane thermal conductivity of the injection-molded composite reaches 1.26 W/(m·K), while the through-plane conductivity is only 0.40 W/(m·K). In contrast, compression molding, which involves minimal shear, results in randomly dispersed BNNs and isotropic thermal conductivity, with both in-plane and through-plane values around 0.41 W/(m·K) at the same filler loading. Both processing methods preserve the coexistence of α- and β-crystalline phases in PVDF. However, injection molding enhances matrix crystallinity through stress-induced crystallization, yielding composites with higher density and superior tensile properties. Compression molding, due to slower cooling, leads to incomplete PVDF crystallization, as evidenced by a shoulder peak near 164 °C in differential scanning calorimetry (DSC) curves. This study elucidates the mechanism by which processing methods regulate the structure and properties of PVDF/BNNs composites, offering theoretical and practical guidance for designing high-performance thermally conductive materials. Full article

This study proposes an artificial intelligence-integrated Six Sigma framework for reducing multiple critical defects in plastic injection molding using real industrial production data from a washing-machine control-panel manufacturing line. Predictive models were developed under severe class imbalance conditions and combined with SHAP-based interpretability to identify the most influential process parameters. A multi-objective NSGA-II optimization strategy was then employed to simultaneously minimize major defect types, including gas-trapped burn (GTB), short shot (SS), sink mark (SK), and flash (FL). The proposed framework was validated through on-site continuous trial production of 300 parts after process stabilization, demonstrating substantial and consistent defect reduction. The results indicate that the integration of data-driven modeling, explainable artificial intelligence, and evolutionary multi-objective optimization provides a practical and scalable approach for quality improvement in industrial injection molding processes. Full article

Metal Injection Molding (MIM) enables complex orthodontic-bracket geometries but can introduce surface and geometric discontinuities that act as initiation sites for crevice and pitting corrosion. The effect of acidic, kombucha-like exposure on corrosion and repassivation was assessed for MIM-316L brackets relative to a commercial comparator, and the coupling between surface quality (roughness and wettability) and localized damage at scanning electron microscopy (SEM)-identified hot-spots was examined. Kombucha was characterized by pH and titratable acidity. Surfaces were characterized by SEM, areal roughness metrics (R_a, S_a, S_z, and A2), and wettability by sessile-drop goniometry. Electrochemical behavior in artificial saliva was measured using open-circuit potential and cyclic potentiodynamic polarization (ASTM F2129/G59), and a qualitative magnetic check was included as a pragmatic quality-assurance screen. Exposure in kombucha reduced breakdown and repassivation potentials and increased passive current density, with the strongest effects co-localizing geometric discontinuities. Commercial brackets exhibited markedly poorer surface quality (notably higher S_z), amplifying acidity-driven susceptibility. These findings indicate that, under acidic challenges, surface/geometry quality dominates corrosion behavior; non-magnetic-phase compliance and simple chairside screening (e.g., magnet test), alongside tighter manufacturing controls on roughness and edge finish, should be incorporated into clinical and industrial quality assurance (QA). Full article

Using additive manufacturing for the design of inserts in injection moulding (IM) offers advantages in product development and customization. However, challenges related to operating temperature and mechanical resistance remain. This article presents a systematic screening methodology to evaluate the suitability of materials for specific applications. Ten commercial Material Extrusion (MEX) filaments were selected to produce test samples. Moldex3D simulation software was employed to model the IM process using two thermoplastics and to determine the temperature and pressure conditions that the printed inserts must withstand. Simulation results were critically interpreted and cross-referenced with the experimental material characterisations to evaluate material suitability. Nine of the ten MEX materials were suitable for IM with LDPE, and five with PP. Dimensional assessments revealed that six insert solutions required further post-processing for assembly, while three did not. All of the selected materials successfully survived 10 injection cycles without encountering any significant issues. The simulation results were validated by comparing temperature data from a thermal imaging camera during IM, revealing only minor deviations. The study concludes that combining targeted material characterization with CAE simulation provides an effective and low-cost strategy for selecting MEX filaments for injection moulding inserts, supporting rapid tooling applications in niche production. Full article

Dissolvable microneedles (DMNs) represent an innovative approach to patient-friendly drug delivery, eliminating the need for conventional hypodermic injections. This study reports on the fabrication, Confocal Laser Scanning Microscopy (CLSM)-based optical visualization of drug distribution, and mechanical characterization of maltose-based DMNs impregnated with lignocaine, a local anesthetic. Microneedles were fabricated using a micro-molding technique and dried for nine hours. Structural integrity was evaluated using Field Emission Scanning Electron Microscopy (FESEM); drug distribution was examined via CLSM; and mechanical strength was assessed using nanoindentation. The FESEM results showed uniform microneedle formation with sharp tips and smooth surfaces, averaging 435 µm in height and 116 µm in width, with no significant dimensional variability (p > 0.5). CLSM analysis indicated even distribution of lignocaine throughout the matrix. Mechanical testing showed that each microneedle withstood 0.6 N, surpassing the 0.1 N threshold required for skin insertion. These results support the viability of maltose-based DMNs for local anesthetic delivery, with implications for outpatient, pediatric, and self-administered care settings. Future investigations will include Franz diffusion and in vitro dissolution studies to examine release kinetics. Full article

To address the severe wear of the hole wall and orifice in ejector pin guide holes of injection molds caused by frequent hole-shaft sliding, this study proposes a composite strengthening method that combines nitriding with oblique laser shock peening (N-OLSP). The strengthening uniformity in both circumferential and axial directions was evaluated by defining a laser shock peening uniformity coefficient ($k$). By strictly controlling the uniformity coefficient ratio of two adjacent spots to be no less than 0.98, the optimal step angles for circumferential and axial directions were determined. Comparative experiments were conducted on three types of samples: Untreated, Nitrided, and N-OLSP treated. The results demonstrate that N-OLSP significantly enhances both surface hardness and residual compressive stress of the guide hole, and the degree of improvement increases with a higher value of $k$. Among the tested samples, N-OLSP exhibited the best wear resistance at the orifice, reducing the wear rate to 0.60 μm/h. Compared with the untreated and nitrided samples, the wear rate reduction achieved by N-OLSP was 66.85% and 16.67%, respectively. Full article

This research explores the optimization of mechanical properties and predictive modeling of polylactic acid (PLA) reinforced with boron nitride nanoplatelets (BNNPs) using data-driven machine learning (ML) models. PLA-BNNP composites were fabricated through injection molding, with a focus on how key processing parameters influence their mechanical performance. A Taguchi L27 orthogonal array was applied to assess the effects of BNNP composition (0.02 wt.% and 0.04 wt.%), injection temperature (135–155 °C), injection speed (50–70 mm/s), and pressure (30–50 bar) on properties such as tensile strength, Young’s modulus, and hardness. The results indicated that a 0.04 wt.% BNNP loading improved tensile strength, Young’s modulus, and hardness by 18.6%, 32.7%, and 20.5%, respectively, compared to pure PLA. Taguchi analysis highlighted that higher BNNP concentrations, along with optimal injection temperatures, improved all mechanical properties, although excessive temperatures compromised tensile strength and modulus, while enhancing hardness. Analysis of variance (ANOVA) revealed that injection temperature was the dominant factor for tensile strength (68.88%) and Young’s modulus (86.39%), while BNNP composition played a more significant role in influencing hardness (78.83%). Predictive models were built using machine learning (ML) models such as Random Forest Regression (RFR), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost). Among the ML models, XGBoost demonstrated the highest predictive accuracy, achieving R² values above 98% for tensile strength, 92–93% for Young’s modulus, and 96% for hardness, with low error metrics i.e., Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE). These findings underscore the potential of using BNNP reinforcement and machine learning-driven property prediction to enhance PLA nanocomposites’ mechanical performance, making them viable for applications in lightweight packaging, biomedical implants, consumer electronics, and automotive components, offering sustainable alternatives to petroleum-based plastics. Full article

This paper presents the potential use of sunflower seed hulls (SSH) as a sustainable filler for poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) biocomposites. Ground SSH were incorporated into the PHBV matrix at loadings of 15, 30, and 45 wt% via extrusion and injection molding. The Fourier Transform Infrared Spectroscopy (FTIR) analysis indicated the presence of possible interactions between the filler and the matrix. Mechanical testing revealed a significant increase in stiffness, with the tensile modulus increasing from 2.6 GPa for pure PHBV to approximately 4.5 GPa for the composite containing 45 wt% SSH. However, the tensile strength decreased by approximately 10–40%, while elongation at break dropped to 1.0–1.5%, depending on the SSH dosage, respectively. The thermal analysis indicated that high filler contents suppress crystallization during cooling under laboratory conditions in Differential Scanning Calorimetry (DSC) analysis due to the confinement effect. The key practical advantage is the exceptional improvement in dimensional stability with a processing shrinkage reduction of approximately 80% in the thickness direction. Although water absorption increased with filler loading, biocomposites containing 15–30 wt% SSH exhibited the optimal balance of high stiffness, hardness, and dimensional accuracy. These properties make the developed material a promising option for the production of precise technical molded parts. Full article