Search Results (44)

Polylactide (PLA) that is reinforced with diatomaceous earth (DE) is a promising and eco-friendly material with high engineering potential. This article provides a comprehensive overview of various PLA types and processing methods for PLA + DE composites. This study aimed to determine the mechanical strength limits of PLA + DE composites using two PLA grades—amorphous PLE 005-A and semi-crystalline Ingeo 4043D—that are each filled with Perma-Guard DE 5, 10, and 15% by weight, and two manufacturing methods, injection molding (IM) and additive manufacturing (3DP), using fused filament fabrication (FFF). The mechanical properties were assessed through static tensile tests in accordance with ISO 527-1 and compared with values reported in the literature. The results indicate a linear increase in stiffness (Young’s modulus) with increasing DE content. This is accompanied by a reduction in maximum tensile strength (σ_max) and elongation at break (ε_b). The highest Young’s modulus, around 4.65 GPa, was observed for injection-molded, semi-crystalline PLA with a 15% by weight DE. The greatest tensile strength, approx. 72 MPa, was achieved for printed, semi-crystalline PLA without filler. Furthermore, 3D printing achieved a tensile strength and stiffness comparable to injection molding, though the latter ensured significantly better ductility. These findings provide a basis for adjusting the PLA + DE composite properties to specific applications by selecting the matrix type, DE content, and manufacturing method. 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

Biodegradable polymer composites offer promising alternatives to petroleum-based plastics, supporting the principles of a zero waste and circular economy. This study investigates the reinforcing potential of alkali-treated wood flour derived from recycled pine (Pinus brutia Ten.) and poplar (Populus alba L.) waste wooden pallets in poly(lactic acid) (PLA) biocomposites. Wood flour was initially recovered through grinding and screening during recycling, followed by alkali treatment via a green chemistry approach to enhance interfacial bonding with the PLA matrix. The impact of alkali concentration and two fabrication methods—additive manufacturing (AM) and injection molding (IM)—on the properties of developed biocomposite materials was assessed through mechanical, physical, morphological, and thermal analyses. IM samples outperformed AM counterparts, with the IM PLA containing 30 wt% wood flour (alkali-treated with 10% solution) showing the highest mechanical gains: tensile (+71.35%), flexural (+64.74%), and hardness (+2.62%) compared to untreated samples. Moreover, the AM sample with 10 wt% wood flour and 10% alkali treatment showed a 49.37% decrease in water absorption compared to the untreated sample, indicating improved hydrophobicity. Scanning electron microscopy confirmed that alkali treatment reduced void content and enhanced morphological uniformity, while thermal properties remained consistent across fabrication methods. This work introduces a green composite using non-toxic materials and treatments, facilitating eco-friendly production aligned with zero waste and circular economy principles throughout the manufacturing lifecycle. Full article

Lateral flow tests (LFTs) had a pivotal role in combating the spread of the SARS-CoV-2 virus throughout the COVID-19 pandemic thanks to their affordability and ease of use. Most of LFT devices were based on nitrocellulose membrane strips whose industrial upscaling to billions of devices has already been extensively demonstrated. Nevertheless, the assay option in an LFT format is largely restricted to qualitative detection of the target antigens. In this research, we surveyed the potential of UV nanoimprint lithography (UV-NIL) and extrusion coating (EC) for the high-throughput production of disposable capillary-driven, foil-based tests that allow multistep assays to be implemented for quantitative readout to address the inherent lack of on-demand fluid control and sensitivity of paper-based devices. Both manufacturing technologies operate on the principle of imprinting that enables high-volume, continuous structuring of microfluidic patterns in a roll-to-roll (R2R) production scheme. To demonstrate the feasibility of R2R-fabricated foil chips in a point-of-care biosensing application, we adapted a commercial chemiluminescence multiplex test for COVID-19 antibody detection originally developed for a capillary-driven microfluidic chip manufactured with injection molding (IM). In an effort to build a complete ecosystem for the R2R manufacturing of foil chips, we also recruited additional processes to streamline chip production: R2R biofunctionalization and R2R lamination. Compared to conventional fabrication techniques for microfluidic devices, the R2R techniques highlighted in this work offer unparalleled advantages concerning improved scalability, dexterity of seamless handling, and significant cost reduction. Our preliminary evaluation indicated that the foil chips exhibited comparable performance characteristics to the original IM-fabricated devices. This early success in assay translation highlights the promise of implementing biochemical assays on R2R-manufactured foil chips. Most importantly, it underscores the potential utilization of UV-NIL and EC as an alternative to conventional technologies for the future development in vitro diagnostics (IVD) in response to emerging point-of-care testing demands. Full article

Thermoplastic polycarbonate polyurethane (PCU) has been applied in numerous biomedical applications owing to its superior properties. The objective of this study is to obtain the comprehensive performance of PCU materials with different hardness processed through various molding methods. The performance will be compared with that of natural intervertebral discs to assess their degree of match, with the expectation of further enhancing the application of PCU in the field of elastic intervertebral disc products. PCU materials with four different hardness grades, namely 75A, 85A, 95A, and 55D, were prepared through injection molding (IM), compression molding (CM), and fused deposition modeling in three-dimensional printing (3D). Material property analysis and mechanical performance characterization were conducted on the PCU materials. The PCU materials processed through the three different molding methods exhibited similar results in terms of hardness, scanning electron microscopy (SEM) images, X-ray energy-dispersive spectroscopy (EDS) spectra, and attenuated total reflection Fourier-transform infrared (ATR-FTIR) spectra, indicating that the materials did not degrade or introduce impurities during the molding process and the molding methods used in this study were acceptable. Differences were observed in the tensile and compressive properties of PCU materials. The mechanical properties of 85A- and 95A-hardness materials processed by CM and 3D molding were relatively close to those of natural intervertebral discs. In terms of water contact angle, under the same hardness condition, the materials processed by CM molding exhibited the largest water contact angle, while those processed by IM and 3D molding were similar. The PCU materials with 85A and 95A hardness processed through IM, CM, and 3D exhibited properties that were close to the performance requirements of natural intervertebral discs. There is a high potential for their application in intervertebral disc products to enhance product performance, replace diseased natural discs, and promote the development of cervical total disc replacement (TDR) surgery. 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

Injection molding (IM) is one complex manufacturing process characterized by nonlinear behavior. Unlike classic linear modeling techniques like simple regression, many machine learning (ML) models have the ability to adjust to the nonlinear behaviors and interactions between input and output parameters. Artificial neural networks (ANNs) specifically have demonstrated exceptional performance in problems involving nonlinear modeling. This work will employ complete factorial design of experiments (DoE) to acquire a dataset which is both resilient and suitable for training, validation, and testing purposes. The predictive model demonstrated outstanding performance throughout the training, validation, and test sets. The aggregate R² values for the training, validation, and tests datasets were 97.58%, 93.76%, and 91.31%, respectively, demonstrating a strong ability to accurately foresee outcomes. Differential evolution (DE) successfully achieved a 2% decrease in weight and a notable 14% decrease in energy consumption. The results indicate that combining an ANN with DE is a viable approach for enhancing injection molding parameters, especially in scenarios with multiple objectives. Full article

Injection molding (IM) is a process in which completely melted plastic material is injected into the mold cavity under high pressure at a specific temperature, and the molded product is obtained after pressure holding, cooling, and demolding. During the mold cooling process, the conformal cooling channel system can improve the uniformity of mold temperature, reduce warping deformation, and significantly improve product accuracy. However, the cost consumption of conformal cooling channels for the cavity and core of injection molds is significant, which is a distinct disadvantage. This paper proposes an innovative conformal cooling channel. Compared with conventional cooling channels, the warpage of plastic parts has been reduced by 0.3401 mm. Moreover, the cooling time difference between C2 and C4 is relatively small, about 7.9 s. Among them, C4 takes the shortest time, C1 takes the longest, and C4 is 4.371 s shorter than C1. Compared with C1, the cooling efficiency of C4 has increased by 35.48%. In addition, from a commercial value perspective, many mold manufacturing companies’ real production applications are better suited for using conformal cooling channels alone on the injection mold core. This paper establishes injection molding models under different working conditions, simulates the cooling of dynamic mold temperature molds, and analyzes the effects of fluid media and various fluid rates on mold temperature changes. The results indicate that the cooling effect of cooling water is significantly better than that of cooling oil at the same fluid rate. When the fluid rate increases from 0.75 L/min to 6 L/min, the effect of cooling oil on the temperature change in the mold is significantly higher than that of cooling water. The influence of mold temperature on the cooling medium’s fluid rate tends to stabilize once the cooling medium’s flow rate reaches a specific value. Full article

The use of pyrolyzed carbon fibers (pCFs) in the secondary raw material market is growing, but potential applications for pCFs are limited by their wool-like appearance. Common solutions are further processing into fiber mats or shredding and adding the fibers during compounding in twin-screw extruders (TSEs). In the latter process, the initial fiber length is usually reduced to less than 1 mm during compounding and further reduced during injection molding. Hence, this paper presents an alternative compounding approach by investigating if internal mixers (IMs) are suitable for retaining pCFs after compounding longer. First, the influence of the mixing sequence for adding pCFs to the mixing process of the resulting fiber length was investigated. Second, a design of experiments was carried out using a laboratory IM, considering the process parameters of rotational speed, mixing time, coupling agent content, initial fiber length, and chamber filling level. Third, the results obtained were scaled up and applied to a production-scale IM. Important findings are that the melting of the matrix polymer should occur before fibers are added. This results in fiber contents of 20 wt.%. To achieve higher fiber contents, small amounts of carbon fiber must be added during the melting process. The process parameters investigated had no significant influence on the resulting fiber length. Compounding with IM is suitable for an initial fiber length of up to 24 mm. A composite with carbon fibers from industrial offcuts (rCFs) prepared by TSE compounding was used to compare the mechanical properties of the injection-molded samples due to the non-availability of composites with pyrolyzed fibers. Compounding resulted in an improvement in the weight-average fiber length from 226 µm (TSE) to 540 µm (IM). However, this fiber length could not be preserved during injection molding, resulting in similar mechanical properties of both, the pCF composites prepared by an IM and the commercially available rCF composites. Full article

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain–computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain–computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%. Full article

This paper examines the emissions tradeoffs of additive manufacturing (i.e., 3D printing) using plastic waste in fused granular fabrication (FGF) versus traditional fused filament fabrication (FFF) and injection molding (IM). A ‘cradle-to-gate’ life cycle assessment (LCA) was utilized to compare these methods, built in OpenLCA v1.11.0 with the Ecoinvent v3.9.1 database. Different scenarios were used to evaluate the impacts of varying transportation and material inputs, highlighting critical emission contributors in manufacturing plastic goods. FGF with waste plastic can significantly reduce climate impact by 82.1% relative to FFF and 70.6% relative to IM for a specified unit product. Even with varied transportation and materials, FGF is a lower CO₂-equivalent emitting method. Utilizing FGF with waste plastic as a manufacturing method could reduce emissions and divert plastic from landfills and the environment, thereby contributing to a circular plastic economy. Full article

Optimizing process parameters to minimize defects remains an important challenge in injection molding (IM). Machine learning (ML) techniques offer promise in this regard, but their application often requires extensive datasets. Transfer learning (TL) emerges as a solution to this problem, leveraging knowledge from related tasks to enhance model training and performance. This study explores TL’s viability in predicting weld line visibility in injection-molded components using artificial neural networks (ANNs). TL techniques are employed to transfer knowledge between datasets related to different components. Furthermore, both source datasets obtained from simulations and experimental tests are used during the study. In order to use process simulations to obtain data regarding the presence of surface defects, it was necessary to correlate an output variable of the simulations with the experimental observations. The results demonstrate TL’s efficacy in reducing the data required for training predictive models, with simulations proving to be a cost-effective alternative to experimental data. TL from simulations achieves comparable predictive metric values to those of the non-pre-trained network, but with an 83% reduction in the required data for the target dataset. Overall, transfer learning shows promise in streamlining injection molding optimization and reducing manufacturing costs. Full article

The polymer foil industry is one of the leading producers of plastic waste. The development of new recycling methods for packaging products is one of the biggest demands in today’s engineering. The subject of this research was the melt processing of multilayered PET-based foil waste with PETG copolymer. The resulting blends were intended for additive manufacturing processing using the fused deposition modeling (FDM) method. In order to improve the properties of the developed materials, the blends compounding procedure was conducted with the addition of a reactive chain extender (CE) and elastomeric copolymer used as an impact modifier (IM). The samples were manufactured using the 3D printing technique and, for comparison, using the traditional injection molding method. The obtained samples were subjected to a detailed characterization procedure, including mechanical performance evaluation, thermal analysis, and rheological measurements. This research confirms that PET-based film waste can be successfully used for the production of filament, and for most samples, the FDM printing process can be conducted without any difficulties. Unfortunately, the unmodified blends are characterized by brittleness, which makes it necessary to use an elastomer additive (IM). The presence of a semicrystalline PET phase improves the thermal resistance of the prepared blends; however, an annealing procedure is required for this purpose. Full article

Injection molding (IM) is an ideal technique for the low-cost mass production of moderately thick plane lenses (MTPLs). However, the optical performance of injection molded MTPL is seriously degraded by the warpage and sink marks induced during the molding process with complex historical thermal field changes. Thus, it is essential that the processing parameters utilized in the molding process are properly assigned. And the challenges are further compounded when considering the MTPL molding energy consumption. This paper presents a set of procedures for the optimization of injection molding process parameters, with warpage, sink marks reflecting the optical performance, and clamping force reflecting the molding energy consumption as the optimization objectives. First, the orthogonal experiment was carried out with the Taguchi method, and the S/N response shows that these three objectives cannot reach the optimal values simultaneously. Second, considering the experimental data scale, the back propagation neural network updated by the particle swarm optimization method (PSO-BPNN) was applied to establish the complex nonlinear mapping relationship between the process parameters and these three trade-off objectives respectively. Then, the Pareto optimal frontier of the multi-objective optimization problem was attained by multi-objective particle swarm optimization using a mutation operator and dominance coefficient algorithm (OMOPSO). And the competitive relationship between these objectives was further confirmed by the corresponding pairwise Pareto frontiers. Additionally, the TOPSIS method with equal weights was employed to achieve the best optimal solution from the Pareto optimal frontier. The simulation results yielded that the maximum values of warpage, sink marks, and clamping force could be reduced by 7.44%, 40.56%, and 5.56%, respectively, after optimization. Finally, MTPL products were successfully fabricated. Full article

LDS-MIDs (laser direct structured mechatronic integrated devices) are 3D (three-dimensional) circuit carriers that are used in many applications with a focus on antennas. However, thanks to the rising frequencies of HF (high-frequency) systems in 5G and radar applications up to the mmWave (millimeter wave) region, the requirements regarding the geometrical accuracy and minimal wall thicknesses for proper signal propagation in mmWave circuits became more strict. Additionally, interest in combining those with 3D microstructures like trenches or bumps for optimizing transmission lines and subsequent mounting processes is rising. The change from IM (injection molding) to ICM (injection compression molding) could offer a solution for improving the 3D geometries of LDS-MIDs. To enhance the scientific insight into this process variant, this paper reports on the manufacturing of LDS-MIDs for mmWave applications. Measurements of the warpage, homogeneity of local wall thicknesses, and replication accuracy of different trenches and bumps for mounting purposes are presented. Additionally, the effect of a change in the manufacturing process from IM to ICM regarding the dielectric properties of the used thermoplastics is reported as well as the influence of ICM on the properties of LDS metallization—in particular the metallization roughness and adhesion strength. This paper is then concluded by reporting on the HF performance of CPWs (coplanar waveguides) on LDS-MIDs in comparison to an HF-PCB. Full article