Search Results (180)

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 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

In recent years, plastic decorative materials have been used in the automotive industry due to their advantages such as being environmentally friendly, aesthetic, light and economically affordable. Plastic decorative materials can exhibit high strength and metallic reflection with metal coatings. Chrome plating is generally preferred in the production of decorative plastic parts in the automotive industry. In this study, the effect of injection molding processing parameters on the metal–polymer adhesion of chrome-plated acrylonitrile butadiene styrene (ABS) was investigated. The ABS-based front grille frames are fabricated by means of using an industrial-scale injection molding machine. Then, the fabricated ABS-based front grille frame was plated with chrome by means of the electroplating method. The metal–polymer adhesion was investigated as a function of the injection molding processing parameters by means of a cross-cut test and scanning electron microscope (SEM). As a result, it was determined that the optimal injection process parameters, a cooling time of 18 s, a mold temperature of 70 °C, injection rates of 45-22-22-20-15-10 mm/s, and packing pressures of 110-100-100 bar, were effective in enhancing polymer–metal adhesion for the ABS-based front grille frame. 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

Precision micromilling is currently widely used for the fabrication of injection mold inserts for the mass production of microfluidic devices. However, for complex devices with micrometer-scale and high density of structures, micromilling results in high production times and costs for production runs of hundreds or thousands of units. Femtosecond laser (fs-laser) technology has emerged as a promising solution for high-precision micromachining. This study analyzes the potential of fs-laser micromachining for the fabrication of injection mold inserts for the large-scale production of thermoplastic microfluidic devices. For the evaluation of technology, a reference design was defined. The parameters of the fs-laser process were optimized to achieve high resolution of the structures and optimal surface quality, aiming to minimize production times and costs while ensuring the quality of the final part. The microstructures were replicated in two different grades of COC (Cyclic Olefin Copolymer) by injection molding. The dimensional tolerance of the structures and the surface finish achieved both in the insert and the polymer parts were characterized by scanning electron microscopy (SEM) and confocal microscopy. The surface quality of the final parts and its suitability for microfluidic fabrication were also assessed performing chemical bonding tests. The fs-laser machining process has shown great potential for the mass production of microfluidic devices. The developed process has enabled for a reduction of up to 90% in the fabrication times of the insert compared to micromilling. The parts exhibited very smooth surfaces, with roughness values (Sa) of 64.6 nm for the metallic insert and 71.8 nm and 72.9 nm for the COC E-140 and 8007S-04 replicas, respectively. The dimensional tolerance and the surface quality need to be improved to be competitive with the finishes achieved with precision micromilling. Nonetheless, there is still room for improvement considering the significant reduction in the production times through new laser processing strategies. Full article

The material in the mold of the injection-molding machine releases significant latent heat of solidification during the cooling process. The efficient recovery and utilization of this waste heat is crucial for improving energy efficiency. A novel integrated energy management system for mold cooling/heat pump/material preheating is proposed in this paper. Taking the symmetrical thermodynamic performance of the heat pump components as the basis and optimizing the system configurations, four system configurations were investigated: MC/BHP/MPCC, MC/RHP/MPCC, MC/HP/MP-IEMS, and MC/DCHP/MP-IEMS, utilizing EBSILON software. The performance of the systems was evaluated through the coefficient of performance (COP) and whole cycle energy efficiency (η). The T-q, T-s, and P-h diagrams were analyzed. It was found that, under comparative operating conditions, both the MC/HP/MP-IEMS and MC/DCHP/MP-IEMS systems exhibited significantly higher COP and η than the MC/BHP/MPCC and MC/RHP/MPCC systems. MC/HP/MP-IEMS achieves a COP of 13.66 and η of 22.09. Similarly, MC/DCHP/MP-IEMS achieves a COP of 14.00 and η of 22.53. The paper optimizes the other three systems using MC/BHP/MPCC as the comparison condition. Optimal cycle performances are achieved with COP and η values of 9, 16, 16, and 9, 26, 25, respectively. A comparison of the thermodynamic performance of five different refrigerants revealed that R123 and R245fa have superior overall performance. This study provides theoretical support for the engineering implementation of integrated energy management systems for injection-molding machines. Full article

The main aim of this Special Issue was to present the current state of the research on the subjects of theory, modeling, monitoring, and control of the operation of technology systems and processes, along with research and diagnostics of manufacturing systems and processes operation. The contributions have focused on manufacturing research, operation reliability, and diagnostics of machines; inspection, measurements, evaluation, and diagnostics of production quality in technologies of standard and progressive machining; reversible engineering; 3D printing; pressure die casting; injection molding; EDM; AWJ cutting; etc., which are used for advanced processing of materials and various kinds of technological applications. Full article

Thermoplastic injection molding is a widely used process for producing complex three-dimensional plastic parts with tight dimensional tolerances. A key determinant of part quality is the switchover point—the transition from velocity-controlled filling to pressure-controlled packing. This transition affects critical product attributes, such as d imensional accuracy, weight consistency, and surface finish. Precise control of the switchover point enhances process stability, robustness, and adaptability. This review consolidates recent advancements in switchover methods and adaptive control techniques. Improvements in traditional methods include the use of pressure gradient detection to mitigate viscosity variations and adaptive control to refine stroke- and time-dependent switchovers. In addition, deformation-based strategies detect the mold-opening force associated with cavity pressure through clamping force, mold separation, or tie-bar elongation. The integration of machine learning and feature extraction techniques enables the real-time adjustment of the switchover point by mapping relationships between process parameters and quality criteria. In addition, ultrasonic sensors provide non-invasive melt front detection, reducing the risk of mold damage. Real-time simulations, updated through nozzle pressure feedback, complement these methods to achieve precise switchover timing. This review also identifies persistent challenges, such as sensitivity to material properties, machine wear, and environmental conditions, and it explores future directions for improving the accuracy and adaptability of switchover control in modern injection molding processes. Full article

The increased interest in artificial intelligence in manufacturing has driven the adoption of machine learning to optimize processes and improve efficiency. A key challenge in injection molding is the variability of recycled materials, which affects part quality and processing stability. This study presents a novel closed-loop process control approach for injection molding, leveraging machine learning to adaptively predict processing inputs and quality outcomes. The methodology was tested on five blends of recycled polypropylene (rPP), using artificial neural networks (ANNs), linear regression, and polynomial regression to model the relationships between material properties and process parameters. The dataset was split 80/20 into training and testing sets. The ANN model was implemented using TensorFlow and Keras, with six hidden layers of 32 neurons per layer, ReLU activation, and an Adam optimizer. Empirical tuning and early stopping were used to optimize performance and prevent overfitting. Predictions were evaluated based on mean absolute error (MAE), mean squared error (MSE), and percentage error. The results showed that yield stress, ultimate elongation, and part weight were accurately predicted within a 5% error for linear and polynomial regression models and within a 10% error for the ANN. However, modulus predictions were less reliable, with errors of ~11% for ANN and linear regression and ~40% for polynomial regression, reflecting the inherent variability of this property in rPP blends. Predictions of processing inputs had errors ranging from 3% to 25%, depending on the model and response variable. No single modeling approach was consistently superior across all responses, highlighting the complexity of the relationship between material properties, process parameters, and quality metrics. Overall, the work demonstrates that closed-loop process control, powered by machine learning, can effectively predict key quality parameters in injection molding of recycled materials. The proposed approach can improve process stability and material utilization, facilitating increased adoption of sustainable materials. 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

The paper presents a simple and cost-effective way of enhancing the thermal conductivity of the paraffin/graphite phase change material (PCM) composite spheres manufactured by using a low-cost and eco-friendly method. The composite materials were made of an admixture of 5–20% vol. graphite powder. The manufacturing process of macro-encapsulated PCM consists of creating digital models, mold printing, and PCM injections. The experimental data shows that composite materials have an increased thermal conductivity, from 3 to 11 times compared to paraffin, and are effective in cooling application of electronic components where they lowered the maximum temperature up to 30 °C. For low-volume PCM sphere fabrication, it was proposed the injection molding in the 3D printed mold; the results show that fused deposition modeling (FDM) is efficient in saving energy up to 30% compared to machining. The carbon emissions generated during the fabrication technology were found to be strongly dependent on printing process parameters and the energy mix used to produce the electrical energy used. 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

Dielectric materials have gained traction for their energy-storage capacitive and electrically insulating properties as sensors and in smart surface technologies such as in In-Mold Electronics (IME). IME is a disruptive technology that involves environmentally protected electronics in plastic thermoformed and molded structures. The use of IME in a human–machine interface (HMI) provides a favorable experience to the users and helps reduce production costs due to a smaller list of parts and lower material costs. A few functional components that are compatible with one another are crucial to the final product’s properties in the IME structure. Of these components, the dielectric layers are an important component in the smart surface industry, providing insulation for the prevention of leakage currents in multilayered printed structures and capacitance sensing on the surface of specially designed shapes in IME. Advanced dielectric materials are non-conductive materials that impend and polarize electron movements within the material, store electrical energy, and reduce the flow of electric current with exceptional thermal stability. The selection of a suitable dielectric ink is an integral stage in the planning of the IME smart touch surface. The ink medium, solvent, and surface tension determine the printability, adhesion, print quality, and the respective reaction with the bottom and top conductive traces. The sequence in which the components are deposited and the heating processes in subsequent thermoforming and injection molding are other critical factors. In this study, various commercially available dielectric layers were each printed in two to four consecutive layers with a mesh thickness of 50–60 µm or 110–120 µm, acting as an insulator between conductive silver traces overlaid onto a polycarbonate substrate. Elemental mapping and optical analysis on the cross-section were conducted to determine the compatibility and the adhesion of the dielectric layers on the conductive traces and polycarbonate substrate. The final selection was based on the functionality, reliability, repeatability, time-stability, thickness, total processing time, appearance, and cross-sectional analysis results. The chosen candidate was then placed through the final product design, circuitry design, and plastic thermoforming process. In summary, this study will provide a general guideline to optimize the selection of dielectric inks for in-mold electronics applications. Full article