Search Results (990)

Thermal wick-debinding, commonly used in low-pressure injection molding, remains challenging due to complex interactions between binder transport, capillary forces, and thermal effects. This study presents a numerical simulation of binder removal kinetics by coupling Darcy’s law with the Phase Transport in Porous Media interface in COMSOL Multiphysics. The model was validated and subsequently used to study the influence of key debinding parameters. Contrary to the Level Set method, which predicts isolated binder clusters, the Multiphase Flow in Porous Media method proposed in this work more accurately reflects the physical behavior of the process, capturing a continuous binder extraction throughout the green part and a uniform binder distribution within the wicking medium. The model successfully predicted the experimentally observed decrease in binder saturation with increasing debinding temperature or time, with deviation limited 3–10 vol. % (attributed to a mandatory brushing operation, which may underestimate the residual binder mass). The model was then used to optimize the debinding process: for a temperature of 100 °C and an inter-part gap distance of 5 mm, the debinding time was minimized to 7 h. These findings highlight the model’s practical utility for process design, offering a valuable tool for determining optimal debinding parameters and improving productivity. Full article

The main thrust of the study is the need to cut down the time taken for mold changes in plastic injection molding which is fundamental to the productivity and efficiency of the process. The research encompasses Lean Manufacturing, DMAIC, and SMED which are improved using fuzzy logic and AI for rapid changeover optimization on the NEGRI BOSSI 650 machine. A decrease in downtime by 65% and an improvement in the Process Cycle Efficiency by 46.8% followed the identification of bottlenecks, externalizing tasks, and streamlining workflows. AI-driven analysis could make on-the-fly adjustments, which would ensure that resources are better allocated, and thus sustainable performance is maintained. The findings highlight how integrating Lean methods with advanced technologies enhances operational agility and competitiveness, offering a scalable model for continuous improvement in industrial settings. Full article

The deformation behavior of polymers is influenced not only by service conditions such as temperature and the strain rate but also significantly by the formation process. However, existing simulation frameworks typically treat injection molding and the in-service mechanical response separately, making it difficult to capture the impact of the thermal history on large deformation behavior. In this study, the deformation behavior of injection-molded polycarbonate (PC) was investigated by accounting for its thermal history during formation, achieved through combined experimental characterization and constitutive modeling. PC specimens were prepared via injection molding followed by annealing at different molding/annealing temperatures and durations. Uniaxial tensile tests were conducted using a Zwick universal testing machine at strain rates of 10⁻³–10⁻¹ s⁻¹ and temperatures ranging from 293 K to 353 K to obtain stress–strain curves. The effects of the strain rate, testing temperature, and annealing conditions were thoroughly examined. Building upon a previously proposed phenomenological model, a new constitutive framework incorporating thermal history effects during formation was developed to characterize the large deformation behavior of PC. This model was implemented in ABAQUS/Explicit using a user-defined material subroutine. Predicted stress–strain curves exhibit excellent agreement with the experimental data, accurately reproducing elastic behavior, yield phenomena, and strain-softening and strain-hardening stages. Full article

The mold clamping mechanism is crucial in injection molding machines and significantly influences molding. This research optimizes the Stephenson-chain mechanism with double-toggle effects, particularly focusing on acceleration and driving force. A design incorporating double-toggle effects in the closed position enhances clamping force and ensures safety. For a 6-bar linkage, the Watt-chain mechanism and Stephenson-chain mechanism are available. In this paper, Stephenson-chain mechanisms were selected and subjected to a comprehensive analysis of their kinematic characteristics using vector loop and finite difference methods. The optimal design process included defining the objective function and evaluating the maximum acceleration and force ratio. The results show that the optimal Stephenson-I mechanism achieves a 1.92% increase in the maximum acceleration, and the maximum driving force decreases by 12.34% compared to the optimal Watt-chain mechanism. The Stephenson-II mechanism performs even better, with a 33.94% reduction in maximum acceleration and a 6.81% decrease in maximum driving force compared to the optimal Watt-chain mechanism. The results indicate that the Stephenson-II mechanism outperforms the Stephenson-I mechanism and other existing designs in terms of the maximum acceleration and driving force. Full article

This study presents a breakthrough in sustainable injection molding by uniquely combining a backpropagation neural network (BPNN) with particle swarm optimization (PSO) to overcome traditional optimization challenges. The BPNN’s exceptional ability to learn complex nonlinear relationships between six key process parameters (including melt temperature and holding pressure) and product quality is amplified by PSO’s intelligent search capability, which efficiently navigates the high-dimensional parameter space. Together, this hybrid approach achieves what neither method could accomplish alone: the BPNN accurately models the intricate process-quality relationships, while PSO rapidly converges on optimal parameter sets that simultaneously meet strict quality targets (66–70 g weight, 3–5 mm thickness) and minimize energy consumption. The significance of this integration is demonstrated through three key outcomes: First, the BPNN-PSO combination reduced optimization time by 40% compared to traditional trial-and-error methods. Second, it achieved remarkable prediction accuracy (RMSE 0.8229 for thickness, 1.5123 for weight) that surpassed standalone BPNN implementations. Third, the method’s efficiency enabled SMEs to achieve CAE-level precision without expensive software, reducing setup costs by approximately 25%. Experimental validation confirmed that the optimized parameters decreased energy use by 28% and material waste by 35% while consistently producing parts within specifications. This research provides manufacturers with a practical, scalable solution that transforms injection molding from an experience-dependent craft to a data-driven science. The BPNN-PSO framework not only delivers superior technical results but does so in a way that is accessible to resource-constrained manufacturers, marking a significant step toward sustainable, intelligent production systems. For SMEs, this framework offers a practical pathway to achieve both economic and environmental sustainability, reducing reliance on resource-intensive CAE tools while cutting production costs by an estimated 22% through waste and energy savings. The study provides a replicable blueprint for implementing data-driven sustainability in injection molding operations without compromising product quality or operational efficiency. Full article

Acoustic tweezers, as advanced micro/nano manipulation tools, play a pivotal role in biomedical engineering, microfluidics, and precision manufacturing. However, piezoelectric-based acoustic tweezers face performance limitations due to multi-physical coupling effects during microfabrication. This study proposes a novel approach using injection molding with embedded electronics (IMEs) technology to fabricate piezoelectric micro-ultrasonic transducers with micron-scale precision, addressing the critical issue of acoustic node displacement caused by thermal–mechanical coupling in injection molding—a problem that impairs wave transmission efficiency and operational stability. To optimize the IME process parameters, a hybrid multi-objective optimization framework integrating NSGA-II and MOPSO is developed, aiming to simultaneously minimize acoustic node displacement, volumetric shrinkage, and residual stress distribution. Key process variables—packing pressure (80–120 MPa), melt temperature (230–280 °C), and packing time (15–30 s)—are analyzed via finite element modeling (FEM) and validated through in situ tie bar elongation measurements. The results show a 27.3% reduction in node displacement amplitude and a 19.6% improvement in wave transmission uniformity compared to conventional methods. This methodology enhances acoustic tweezers’ operational stability and provides a generalizable framework for multi-physics optimization in MEMS manufacturing, laying a foundation for next-generation applications in single-cell manipulation, lab-on-a-chip systems, and nanomaterial assembly. Full article

Resin Transfer Molding (RTM) is a widely used composite manufacturing process where liquid resin is injected into a closed mold filled with a fibrous preform. By applying this process, large pieces with complex shapes can be produced on an industrial scale, presenting excellent properties and quality. A true physical phenomenon occurring in the RTM process, especially when using vegetable fibers, is related to the absorption of resin by the fiber during the infiltration process. The real effect is related to the slowdown in the advance of the fluid flow front, increasing the mold filling time. This phenomenon is little explored in the literature, especially for non-isothermal conditions. In this sense, this paper does a numerical study of the liquid injection process in a closed and heated mold. The proposed mathematical modeling considers the radial, three-dimensional, and transient flow, variable injection pressure, and fluid viscosity, including the effect of liquid fluid absorption by the reinforcement (fiber). Simulations were carried out using Computational Fluid Dynamic tools. The numerical results of the filling time were compared with experimental results, and a good approximation was obtained. Further, the pressure, temperature, velocity, and volumetric fraction fields, as well as the transient history of the fluid front position and injection fluid volumetric flow rate, are presented and analyzed. Full article

This study investigates a hybrid manufacturing approach that combines 3D printing and injection molding to extend the limitations of each individual technique. Injection molding is often limited by high initial tooling costs, long lead times, and restricted geometric flexibility, whereas 3D-printed molds tend to suffer from material degradation, extended cooling times, and lower surface quality. By integrating 3D-printed molds into the injection-molding process, this hybrid method enables the production of complex geometries with improved cost-efficiency. The approach is demonstrated using a range of polymeric materials, including ABS, nylon, and polyurethane foam—each selected to enhance the mechanical and thermal performance of the final products. Finite element method (FEM) analysis was conducted to assess thermal distribution, deformation, and stress during manufacturing. Results indicated that both temperature and stress remained within safe operational limits for 3D-printed materials. An economic analysis revealed substantial cost savings compared to fully 3D-printed components, establishing hybrid manufacturing as a viable and scalable alternative. This method offers broad industrial applicability, delivering enhanced mechanical properties, design flexibility, and reduced production costs. Full article

Polystyrene (PS), a thermoplastic polymer, is used in many applications due to its mechanical performance, good chemical inertness, and excellent processability. However, it is doped with different nanomaterials for reasons such as improving its electrical conductivity and mechanical properties. In this study, carbon nanotube (CNT)-added PS composites were produced with the aim of combining the properties of CNTs, such as their low weight and high tensile strength and Young’s modulus, with the versatility, processability, and mechanical properties of PS. In this study, multi-walled carbon nanotube (MWCNT)-reinforced polystyrene (PS) composites with different percentage ratios (0.1, 0.2, and 0.3 wt%) were prepared by a plastic injection molding method. The mechanical, microstructural, and thermal properties of the fabricated PS/MWCNT composites were characterized by Scanning Electron Microscopy (SEM), Fourier Transform Infrared (FTIR) Spectroscopy, Atomic Force Microscopy (AFM) and Thermogravimetric Analysis (TGA) techniques. AFM analyses were carried out to investigate the surface properties of MWCNT-reinforced composite materials by evaluating the root mean square (RMS) values. These analyses show that the RMS value for MWCNT-reinforced composite materials decreases as the weight percentage of MWCNTs increases. The TGA results show that there is no change in the degradation temperature of the 0.1%- and 0.2%-doped MWCNT composites compared to pure polystyrene, but the degradation of the 0.3%-doped MWCNT composite is almost complete at a temperature of 539 °C. Among the PS/MWCNT composites, the 0.3%-doped MWCNT composite exhibits more thermal stability than pure PS and other composites. Similarly, the values of the percentage elongation and tensile strength of 0.3% MWCNT-doped composites was obtained as 1.91% and 12.174% mm², respectively. These values are higher than the values of 0.1% and 0.2% MWCNT-doped composite materials. In conclusion, the mechanical and thermal properties of MWCNT-reinforced PS polymers provide promising results for researchers working in this field. Full article

Injection molding is a fundamental process for transforming plastics into various industrial components. Among the critical aspects studied in this process, volumetric contraction and warpage of plastic parts are of particular importance. Achieving precise control over warpage is crucial for ensuring the production of high-quality components. This research explores optimizing injection process parameters to minimize volumetric contraction and warpage in rectangular polypropylene (PP) parts. The study employs experimental analysis, MoldFlow simulation, and Artificial Neural Network (ANN) modeling. MoldFlow simulation software provides valuable data on warpage, serving as input for the ANN model. Based on the Backpropagation Neural Network algorithm, the optimized ANN model accurately predicts warpage by considering factors such as part thickness, flow path distance, and flow path tangent. The study highlights the importance of accurately setting injection parameters to achieve optimal warpage results. The BPNN-based approach offers a faster and more efficient alternative to computer-aided engineering (CAE) processes for studying warpage. Full article

Mesoporous silicon dioxide films have been shown to be well suited as adhesion-promoting interlayers for generating high-strength polymer–metal interfaces. These films can be fabricated via microwave plasma-enhanced chemical vapor deposition using the precursor hexamethyldisiloxane and oxygen as working gas. The resulting mesoporous structures enable polymer infiltration during overmolding, which leads to a nanoscale form-locking mechanism after solidification. This mechanism allows for efficient stress transfer across the interface and makes the resulting adhesion highly dependent on the morphology of the deposited film. To gain a deeper understanding of the underlying deposition mechanisms and improve process stability, this work investigates the growth behavior of mesoporous silica films using a multiple regression analysis approach. The seven process parameters coating time, distance, chamber pressure, substrate temperature, flow rate, plasma pulse duration, and pause-to-pulse ratio were systematically varied within a Design of Experiments framework. The resulting films were characterized by their free surface area, mean agglomerate diameter, and film thickness using digital image analysis, white light interferometry, and atomic force microscopy. The deposited films exhibit a wide range of morphological appearances, ranging from quasi-dense to dust-like structures. As part of this research, the free surface area varied from 15 to 55 percent, the mean agglomerate diameter from 17 to 126 nm, and the film thickness from 35 to 1600 nm. The derived growth model describes the deposition process with high statistical accuracy. Furthermore, all coatings were overmolded via injection molding and subjected to mechanical testing, allowing a direct correlation between film morphology and their performance as adhesion-promoting interlayers. Full article

This study consisted of the injection molding simulation of polycaprolactone (PCL)-based nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs) for biomedical implant manufacturing. The simulation was additionally supported by experimental validation. The influence of varying MWCNT concentrations (0.5%, 5%, and 10% by weight) on key injection molding parameters, i.e., melt flow behavior, pressure distribution, temperature profiles, and fiber orientation, was analyzed with SolidWorks Plastics software. The results proved the low CNT content (0.5 wt.%) to be endowed with stable filling times, complete mold cavity filling, and minimal frozen regions. Thus, this formulation produced defect-free modular filament sticks suitable for subsequent 3D printing. In contrast, higher CNT loadings (particularly 10 wt.%) led to longer fill times, incomplete cavity filling, and early solidification due to increased melt viscosity and thermal conductivity. Experimental molding trials with the 0.5 wt.% CNT composites confirmed the simulation findings. Following minor adjustments to processing parameters, high-quality, defect-free sticks were produced. Overall, the PCL/MWCNT composites with 0.5 wt.% nanotube content exhibited optimal injection molding performance and functional properties, supporting their application in modular, patient-specific biomedical 3D printing. Full article

This article presents research and a discussion on the proper use of filtration in optical measurements. Measurements were taken using a Werth multisensory machine using a Werth Zoom optical sensor. During optical measurements, the filtration option can be used. The manufacturer defines filters as “Dust”. They allow the machine operator to define the appropriate size depending on the type of inclusions or artifacts created in the production process. They can occur in processes such as punching on presses or production in the injection molding process of plastics. The presented research results and statistical analyses confirm the assumptions regarding the validity of using filters and their values. The use of filters with a higher value significantly affects the obtained results and forces the machine user to make a reasonable choice. Full article

For automotive GFRP structural components, beyond structural design, the warpage, residual stress/strain, and fiber orientation inevitably induced during the injection molding process significantly compromise their service performance. These factors also diminish the reliability of performance assessments. Thus, it is imperative to develop a process–structure co-optimization approach for GFRP components. In this paper, the performance of a front-end module is evaluated through topological structure design, injection molding process optimization, and simulation with mapped injection molding history, followed by experimental validation and analysis. Under ±1000 N loading, the initial design shows excessive displacement at the latch mounting points (2.254 mm vs. <2.0 mm limit), which is reduced to 1.609 mm after topology optimization. By employing a sequential valve control system, the controls of the melt line and fiber orientation are is superior to thatose of conventional gating systems. The optimal process parameter combination is determined through orthogonal experiments, reducing the warpage to 1.498 mm with a 41.5% reduction compared to the average warpage of the orthogonal tests. The simulation results incorporating injection molding data mapping (fiber orientation, residual stress–strain) show closer agreement with experimental measurements. When the measured displacement exceeded 0.65 mm, the average relative error $E_r$, range $R$, and variance $s^2$ between the experimental results and mapped simulations were 11.78%, 14%, and 0.002462, respectively, validating the engineering applicability of this method. The methodology and workflow can provide methodological support for the design and performance assessment of GFRP automotive body structures, which enhances structural rigidity, improves control over injection molding process defects, and elevates the reliability of performance evaluation. Full article

This study investigates the industrial potential of red gypsum (RG), a major by-product of titanium dioxide (TiO₂) production, for the development of thermoplastic polypropylene (PP)-based composites via melt extrusion, targeting agricultural applications. Prior to compounding, RG was thermally treated at approximately 200 °C to remove residual moisture and chemically bound water, resulting in its anhydrous form (CaSO₄). PP/RG composites were then formulated with RG loadings up to 20 wt.%, employing stearic acid (SA) as a compatibilizer. The resulting materials were thoroughly characterized and successfully processed through industrial-scale injection molding up to 250 °C. Morphological and FTIR analyses confirmed the role of SA in enhancing both filler dispersion and interfacial adhesion between RG and the PP matrix. SEM images revealed finer and more uniformly distributed RG particles, resulting in a reduced loss of ductility and elongation at break typically associated with filler addition. Specifically, the Young’s Modulus increased from 1.62 GPa (neat PP) up to 3.21 GPa with 20 wt.% RG and 0.6 wt.% SA. The addition of 0.6 wt.% SA also helped limit the reduction in stress at break from 46.68 MPa (neat PP) to 34.05 MPa and similarly mitigated the decrease in Charpy impact energy, which declined slightly from 2.66 kJ/m² (neat PP) to 2.24 kJ/m² for composites containing 20 wt.% RG. Preliminary phytotoxicity was assessed using germination tests on Lepidium sativum L. seeds. Eluates from both untreated and SA-treated RG powders resulted in germination indices below 80%, indicating phytotoxicity likely due to high sulfate ion concentrations. In contrast, eluates from composite pellets exhibited germination indices equal to or exceeding 100%, demonstrating the absence of phytotoxic effects. These results highlight the suitability of the developed composites for applications in floriculture and horticulture. The optimized composite pellets were successfully processed via injection molding to manufacture plant pots, which exhibited a dark brown coloration, confirming the effective pigmenting function of RG. These results demonstrate the potential of red gypsum to serve both as a functional filler and pigment in PP composites, providing a sustainable alternative to iron oxide pigments and promoting the valorization of industrial waste through resource recovery. Full article