Search Results (131)

The plastics manufacturing sector has experienced remarkable growth, requiring more optimized operations through reduced repair times and product defects. In this context, the theoretical aim of this research is to prove that the integration of classic continuous improvement tools (TPM and SMED) with advanced data science techniques (machine learning) forms a synergistic approach capable of significantly increasing operational efficiency in manufacturing environments. The study was conducted at a Peruvian plastic container manufacturing company with a first overall equipment efficiency (OEE) of 61.87%, affected by low availability of injection and blow molding machines and a high rework rate. Total Productive Maintenance (TPM) strategies were implemented to improve equipment maintenance, the SMED method to reduce setup times, and a machine learning model to predict defects and burs in products. The effectiveness of the approach was confirmed through simulations in Arena and analysis of historical data. As a result, OEE increased to 80.86%, reducing downtime and rework. In conclusion, this study shows that the combination of TPM, SMED, and machine learning not only improves operational performance but also offers a replicable and robust methodological framework for process optimization in the manufacturing industry. Full article

This paper presents the development and validation of a cost-effective 3D-printed conductive phantom for EEG sensing system validation that achieves 85% cost reduction (£48.10 vs. £300–£500) and 48-hour fabrication time while providing consistent electrical properties suitable for standardized electrode testing. The phantom was fabricated using conductive PLA filament in a two-component design with a conductive upper section and a non-conductive base for structural support. Comprehensive validation employed three complementary approaches: DC resistance measurements (821–1502 $\mathsf{\Omega }$), complex impedance spectroscopy at 100 Hz across anatomical regions (3.01–6.4 k$\mathsf{\Omega }$ with capacitive behavior), and 8-channel EEG system testing (5–11 k$\mathsf{\Omega }$ impedance range). The electrical characterization revealed spatial heterogeneity and consistent electrical properties suitable for comparative electrode evaluation and EEG sensing system validation applications. To establish context, we analyzed six existing phantom technologies including commercial injection-molded phantoms, saline solutions, hydrogels, silicone models, textile-based alternatives, and multi-material implementations. This analysis identifies critical accessibility barriers in current technologies, particularly cost constraints (£5000–20,000 tooling) and extended production timelines that limit widespread adoption. The validated 3D-printed phantom addresses these limitations while providing appropriate electrical properties for standardized EEG electrode testing. The demonstrated compatibility with clinical EEG acquisition systems establishes the phantom’s suitability for electrode performance evaluation and multi-channel system validation as a standardized testing platform, ultimately contributing to democratized access to EEG sensing system validation capabilities for broader research communities. Full article

As global industries seek to reduce energy consumption and lower CO₂ emissions, the need for sustainable, efficient maintenance processes in manufacturing has become increasingly important. Traditional mold cleaning methods often require complete tool disassembly, extended downtime, and heavy use of solvents, resulting in high energy costs and environmental impact. This study presents a novel localized ultrasonic cleaning process for injection molding tools that enables targeted, in situ cleaning of mold cavities without removing the tool from the press. A precisely positioned ultrasonic transducer delivers cleaning energy directly to contaminated areas, eliminating the need for complete mold removal. Multiple cleaning agents, including alkaline and organic acid solutions, were evaluated for their effectiveness in combination with ultrasonic excitation. Surface roughness measurements were used to assess cleaning performance over repeated contamination and cleaning cycles. Although initial tests were performed manually in the lab, results indicate that the method can be scaled up and automated effectively. This process offers a promising path toward energy-efficient, low-emission tool maintenance across a wide range of injection molding applications. Full article

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

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

In this paper, we are looking for the answer to the following question: what geometric deviations do polymer gears made by 3D printing have from the theoretical geometry? From a practical point of view, the question is whether the currently installed injection-molded gear can be replaced by a 3D-printed gear. Thus, the measurements are also carried out on the sample gear and the comparison is made with this data as well. Knowing the data of the existing gear wheel, the CAD model was created, and based on this, samples of the gear were printed using various 3D printing machines. The printed gears were then subjected to geometrical analysis. During the inspection, we performed the measurement of the chordal thickness of the gear wheel using a gear tool caliper, instead of pin measurement and span measurement using a special micrometer, and 3D scanning and analysis. A surface roughness measurement was carried out as well. By conducting measurements on the injection-molded and 3D-printed samples, this research seeks to evaluate the reliability and limitations of the 3D-printed gears, providing insights into their industrial use. This study aims to determine whether 3D printing technologies can produce gears with sufficient accuracy and surface quality for practical applications. Based on the conducted analysis, general conclusions were drawn regarding the potential applicability of the 3D-printed gears. The experimental results indicate notable differences in dimensional accuracy between gears manufactured using Fused Deposition Modeling (FDM) and Selective Laser Sintering (SLS). In terms of chordal thickness measurements, FDM gears exhibited a mean relative error of 1.96 mm, whereas SLS gears showed a significantly higher average deviation of 5.64 mm. For the pin measurement, the relative error averaged 0.193 mm in the case of FDM gears, compared to 0.616 mm for SLS gears. Similarly, the span over four teeth measurements resulted in an average deviation of 0.153 mm for FDM gears, while SLS gears demonstrated a markedly higher mean error of 0.773 mm. With regard to surface roughness, it can be concluded that SLS-manufactured gears exhibit superior performance compared to FDM gears, with an average Ra value of 2.65 µm versus 9.28 µm, although their surface quality remains inferior to that of the injection-molded gear. In light of the higher relative errors observed in SLS gears compared to FDM gears, the dimensions of the theoretical model should be refined to improve the manufacturing accuracy of SLS-produced gears. Full article

This study developed a multi-objective optimization procedure aimed at minimizing surface roughness and volumetric shrinkage in injection-molded products. Surrogate models for both outputs were constructed using the Kriging technique, based on experimental data and seven input parameters: packing pressure, mold temperature, cooling time, injection speed, injection pressure, melt temperature, and packing time. A multi-objective optimization problem was formulated and solved using the pattern search algorithm, generating a Pareto front that highlights the trade-off between the two objectives. This Pareto front was further analyzed to determine three optimal parameter sets. The first point minimizes volumetric shrinkage at 1.9314 mm³ but results in the highest surface roughness of 0.55956 µm. In contrast, the second point yields the lowest surface roughness of 0.20557 µm but the highest volumetric shrinkage of 3.9286 mm³. The third point offers the best compromise between the two objectives, with a volumetric shrinkage of 2.2348 mm³ and surface roughness of 0.28246 µm. The proposed approach provides an experimentally validated tool for plastic engineers, enabling informed parameter adjustments to achieve optimal trade-offs in surface quality and dimensional stability within practical manufacturing constraints. Full article

The use of conformal cooling channels (CCC) in the injection molding process has revolutionized the polymer industry by enhancing part cooling uniformity and improving cooling efficiency, minimizing undesired defects such as warpage and shrinkage inherent to the process. This review paper provides a detailed investigation of the literature on CCC, with special focus on how computational fluid dynamics (CFD) has been employed to analyze and optimize the thermal performance of the cooling system. Additionally, key aspects of CCC design, including geometry optimization, surface roughness, and flow dynamics, are evaluated to improve cooling efficiency, reduce cycle time, and enhance product quality. Several CFD-based studies are reviewed to highlight commonly used simulation methods and CCC optimization approaches for heat transfer enhancement. Particular attention is given to how simulation tools contribute to design improvement and decision-making, addressing practical constraints related to thermal behavior and manufacturability. Key performance parameters such as pressure drop, temperature uniformity, cooling time, and manufacturing limitations are examined and compared, offering a foundation for future directions to advance CCC design and CFD analysis to optimize injection molding. Aiming at contributing to the academia and the industry, the novelty of this review paper lies in its integrative perspective, providing a comprehensive analysis of coupling designing tasks with CFD simulations. As a result, this paper serves as a valuable resource for researchers and industry professionals aiming to leverage CFD for the development of high-performance, energy-efficient CCC. Full article

Plastic injection molding is a fundamental manufacturing process used in various industries, accounting for approximately 30% of the global plastic product market. A significant challenge of this process lies in the need to employ sophisticated computational techniques to optimize the various phases. This review examines the optimization methodologies in injection molding, with a focus on integrating advanced modeling, surrogate models, and multi-objective optimization techniques to enhance efficiency, quality, and sustainability. Key phases such as plasticizing, filling, packing, cooling, and ejection are analyzed, each presenting unique optimization challenges. The review emphasizes the importance of cooling, which accounts for 50–80% of the cycle time, and examines innovative strategies, such as conformal cooling channels (CCCs), to enhance uniformity and minimize defects. Various computational tools, including Moldex3D and Autodesk Moldflow, are discussed due to their role in process simulation and optimization. Additionally, optimization algorithms such as evolutionary algorithms, simulated annealing, and multi-objective optimization methods are explored. The integration of surrogate models, such as Kriging, response surface methodology, and artificial neural networks, has shown promise in addressing computational cost challenges. Future directions emphasize the need for adaptive machine learning and artificial intelligence techniques to optimize molds in real time, offering more innovative and sustainable manufacturing solutions. This review is a comprehensive guide for researchers and practitioners, bridging theoretical advancements with practical implementation in injection molding optimization. Full article

This work presents the redesign of an aircraft aluminum turbine wheel into a thermoplastic composite flange wheel with the support of 3D printing technology, which increases the turbine efficiency thanks to the introduction of the flange geometry, not possible with the current machined aluminum part. This work seeks the reduction of the aircraft’s structural weight by replacing metallic components with thermoplastic alternatives and proves the feasibility of producing a complex geometry product through injection molding, paving the way for manufacturing intricate designs using removable inserts created via 3D printing. This work has been developed within the INN-PAEK project of the H2020-CLEAN SKY 2 program. The thermoplastic component is produced using an innovative process that employs removable inserts in the mold, and its development has followed following three steps: redesign of aluminum part according to functional and plastic materials requirements, design of the mold, and validation of real plastic parts by means of tomography. This paper highlights highly positive results for the project, influenced by the new plastic flange wheel’s ability to achieve both weight reduction and an overall efficiency enhancement that decreases the aircraft’s kerosene consumption, and proves that 3D printing is a highly potential technology for complex thermoplastic part tooling production. Full article

This study investigates the development of a rapid wax injection tooling with enhanced heat dissipation performance using aluminum-filled epoxy resin molds and cooling channel roughening technology. Experimental evaluations were conducted on cooling channels with eleven surface roughness variations, revealing that a maximum roughness of 71.9 µm achieved an 81.48% improvement in cooling efficiency compared to smooth channels. The optimal coolant discharge rate was determined to be 2 L/min. The heat dissipation time for wax patterns was significantly reduced, enabling a cooling time reduction of approximately 12 s per product. For a production scale of 100,000 units, this equates to a time savings of about 13 days. Empirical equations were established for estimating heat dissipation time and pressure drop, with a high coefficient of determination. This research provides a valuable contribution to the mold and dies manufacturing industry, offering practical solutions for sustainable and efficient production processes. Full article