Search Results (1,689)

This study investigates laser transmission welding of 30% glass fiber-reinforced polybutylene terephthalate (PBT-GF30). Injection-molded plates were used as base material, from which specimens were prepared, welded, and experimentally tested. The influence of key process parameters, including laser power, beam size, and scanning speed, on weld quality was systematically evaluated through an iterative optimization approach. An optimized parameter set (400 W laser power, reduced beam size, and increased scanning speed) enabled stable and repeatable weld formation with minimal thermal degradation. Experimental results were further supported by finite element analysis, showing good agreement between numerical and experimental data. The findings confirm the feasibility of laser welding for PBT-GF30 and its potential for aerospace applications requiring precision, weight reduction, and structural reliability. Full article

In smart manufacturing, the injection molding industry faces a “data scarce environment” due to prohibitive physical trial costs. Processing recycled polypropylene (rPP) exacerbates this challenge, as traditional response surface methodology (RSM) fails to capture complex non-linear rheological behaviors induced by material variability. This study proposes a “domain-knowledge guided data augmentation framework,” integrating Taguchi experimental data (L₂₅) with Moldex3D digital twin simulations to construct a 300-sample hybrid dataset. A back-propagation neural network (BPNN) with L₂ regularization was employed for small-sample learning, providing a continuous differentiable physical mapping. To rigorously prevent neighborhood data leakage, the model was evaluated via a strict nested group-based 5-fold cross-validation. Particle swarm optimization (PSO) was coupled to overcome the local minima of gradient descent. Comparative analysis demonstrates that BPNN significantly outperforms both traditional RSM and a newly introduced Random Forest (RF) baseline, achieving a testing mean squared error (MSE) of 0.001 (±0.0002) and a testing R² of 0.95. PSO minimized the shrinkage rate to 3.079%, validated via Moldex3D digital twin simulation with a 0.19% relative error. Synergizing virtual–physical integration with robust neural computing enables superior process control precision in small-data regimes, offering small and medium-sized enterprises (SMEs) a cost-effective pathway for smart optimization. Full article

The valorization of agri-food residues represents an attractive strategy within the circular economy for the development of bio-based materials. In this study, a PLA–cellulose biocomposite (PLACEL10) was developed using cellulose extracted from vine pruning residues (Vitis vinifera, Tintilla de Rota). Cellulose was isolated through sequential acid and alkaline treatments, and the extracted material was incorporated into PLA by melt blending to produce injection-molded specimens. FT-IR confirmed the progressive removal of non-cellulosic components during extraction, while SEM revealed a relatively homogeneous dispersion of cellulose within the polymer matrix. Mechanical characterization showed that PLACEL10 exhibited higher stiffness and tensile strength than the processed PLA and BCF10 controls, although with reduced elongation at break. Biocompatibility was evaluated using hFOB 1.19 osteoblasts by MTS assay, showing viability values above 95% and a proliferative response at 72 h. These results suggest that vine-pruning-derived cellulose can act as an effective reinforcement in PLA and support the potential of this agricultural residue as a feedstock for bio-based composites with possible biomedical and packaging applications. Although the current extraction route involves chemical treatments and cannot be considered fully green, the approach provides a promising route for agricultural waste valorization. Full article

The combination of the dry fiber AFP preforming process and RTM injection process brings new possibilities with regard to automation, high-quality manufacturing, and high-performance characteristics for out-of-autoclave composite manufacturing, particularly in aerospace industry. This paper describes the manufacturing of an aircraft engine Outlet Guide Vane (OGV), made with a dry carbon fiber preform manufactured with Automated Fiber Placement (AFP) and co-injected, co-cured, and co-bonded with titanium fittings through the Resin Transfer Molding (RTM) Process. The details of the assembly process and necessary steps are described. Parts of the digitalization process behind the manufacturing are described, including information about integrated sensors and data management. Full article

The increasing availability of automated development workflows and data-driven methods raises the question of when approaches based on artificial intelligence (AI) provide potential benefits over established engineer-driven workflows in lightweight structural design. This paper presents a quantitative comparison between a conventional engineer-driven process and an AI-assisted, automated workflow for an injection-molded component with fixed installation space, identical boundary conditions, and manufacturing constraints. In the conventional process, topology optimization is followed by manual CAD reconstruction and iterative finite element analysis. In the AI-assisted process, an automated workflow generates many design variants that are simulated and used to train a regression-based surrogate model for rapid exploration of the design space. The conventional workflow yields a manufacturable structure with a high stiffness-to-mass ratio and controlled stresses, whereas the geometry selected from the surrogate model’s prediction shows reduced stiffness, higher stress peaks, and manufacturability issues. The analysis of the best-performing design identified ex post within the training data, rather than directly by the surrogate, illustrates the potential of the automated workflow but also highlights insufficient predictive accuracy for locally stress-sensitive quantities. On the process level, the AI-assisted workflow exhibits clear scaling advantages and a distinct break-even point in terms of development effort, suggesting that such methods are currently best suited as complementary tools for early-stage design space exploration. The quantitative effort values and the break-even point, however, are case-specific and should be interpreted as order-of-magnitude indicators rather than universally valid thresholds. Full article

Agricultural residues and agro-waste are increasingly recognized as valuable reinforcements for sustainable composite materials. Natural fibers derived from these biomasses offer biodegradability, low density, renewability, and potential environmental benefits. However, their performance and sustainability depend strongly on extraction, surface treatment, and processing conditions. Therefore, evaluating the environmental emissions associated with natural fiber biocomposites is essential before claiming sustainability advantages. In this research, flax, jute, kenaf, and bagasse fibers were extracted and treated using an eco-friendly sodium bicarbonate solution, then incorporated into polylactic acid (PLA) matrix to fabricate biocomposites via injection molding. A life cycle assessment (LCA) was conducted using the ReCiPe midpoint (H) method, with a functional unit defined as “per kg” of manufactured biocomposite. The results revealed that jute fiber composites generated the highest emissions across several impact categories, including climate change (1.290 × 10¹ kg CO₂-Eq), terrestrial ecotoxicity (6.327 × 10¹ kg 1,4-DCB-Eq), human toxicity: carcinogenic effects (1.923 kg 1,4-DCB-Eq), and fossil resource use (3.202 kg oil-Eq). Jute also showed a 3.6% increase in terrestrial ecotoxicity and a 19.5% increase in land compared to flax, although it exhibited a 6.5% lower impact related to bagasse. A ±20% electricity-consumption sensitivity analysis further highlighted the dependence of environmental impacts on processing energy demand. Full article

In ultra-high-voltage GIS and GIL systems, epoxy resin insulators are still the mainstream choice. However, as a thermosetting material, epoxy resin is difficult to recycle after disposal, which limits its environmental benefits. Thermoplastic insulators, due to their recyclability, are potential alternatives. This study focuses on 30% glass fiber-reinforced PET and PA6 materials. Their injection molding behavior, hydraulic pressure performance, and insulation performance were systematically analyzed using Moldflow, ANSYS, and COMSOL, respectively. For injection molding, Moldflow simulations were conducted for filling, packing, and cooling stages. Melt temperature was varied from 260 to –310 °C (PET) and 250–300 °C (PA6), while mold temperature was varied from 80 to –130 °C (PET) and 70–120 °C (PA6). An optimization objective function, Y = Δp/20 + Δx/0.5 + Δs/1.8, was developed to determine optimal processing parameters. Based on this function, the optimal parameters identified are: PET at 290 °C melt temperature and 120 °C mold temperature; PA6 at 250 °C melt temperature and 70 °C mold temperature. For hydraulic testing, Moldflow–ANSYS coupled simulations were performed under 2.4 MPa pressure with the compliance criteria of bulk stress < 90 MPa and insert-contact stress < 20 MPa. PA6 passed within a processing window of melt temperature < 270 °C and mold temperature < 120 °C. PET failed under all tested conditions, with insert-contact stress ranging from 24.25 to 27.55 MPa, consistently exceeding the 20 MPa threshold. In terms of insulation performance, this paper utilizes COMSOL to study the electric field distribution of thermoplastic insulators in SF₆ GIS/GIL and provides optimization suggestions for insulator geometry design. This study systematically compares the injection molding processes and hydraulic pressure performance of PET and PA6 thermoplastic insulators. These results provide important process insights and design guidance for evaluating thermoplastic materials as potential alternatives to epoxy resin in GIS/GIL applications. Full article

Surface roughness and mechanical performance are critical determinants of the clinical behavior, hygiene, and longevity of denture base materials. This study investigated the influence of two extrusion temperatures—280 °C and 300 °C—on both the surface roughness and compressive strength of ThermoSens thermoplastic polymer specimens over a 7-day immersion period. Surface roughness was evaluated at baseline, 24 h, and 7 days using a contact profilometer, while compressive strength was measured after 7 days following ISO 604 guidelines. Samples processed at 300 °C exhibited a significantly greater reduction in surface roughness over time (28.3%) compared with those processed at 280 °C (18.3%). However, although specimens processed at 300 °C showed a greater percentage reduction, their absolute roughness values remained higher than those processed at 280 °C. Compression testing demonstrated higher strength and modulus values in the 300 °C group (91.6 ± 1.8 MPa; 1887.9 ± 42.3 MPa) compared to the 280 °C group (82.3 ± 2.1 MPa; 1755.4 ± 38.7 MPa). These findings indicate a trade-off between improved mechanical performance at higher processing temperatures and lower surface roughness at lower temperatures, highlighting the need for the careful optimization of processing conditions. Full article

Poly(lactic acid) (PLA)-based biocomposites incorporating collagen (COLL) and hydroxyapatite (HA) were produced via melt micro-compounding and subsequent injection molding. 1,4-phenylene diisocyanate (PDI) was employed as a compatibilizer, while poly(ethylene glycol) (PEG) was used as a plasticizer. The morphological, thermal, rheological, and mechanical properties, as well as surface wettability, degradation behavior, and cytotoxicity, were comprehensively evaluated. SEM and DSC analyses revealed the phase distribution and thermal transitions, while rheological measurements showed that PEG reduced melt viscosity by increasing chain mobility. Mechanical performance was evaluated using tensile, impact, and DMA tests on standard specimens, indicating that HA primarily enhanced stiffness (elastic modulus), whereas PEG improved toughness, resulting in higher impact strength. Biodegradable bone screw prototypes were produced with the same formulations and subjected to torsion, enzymatic degradation, and MTT cytotoxicity tests. Degradation results indicated that biocomposites containing PEG, collagen, and HA exhibited accelerated mass loss. Overall, the 70/20/10 PLA/COLL/HA/PEG/PDI formulation was more suitable for soft (trabecular) bone tissue, while the 70/10/20 PLA/COLL/HA/PDI formulation showed advantages for hard (cortical) bone tissue applications. Full article

This work presents a comparative study of micro- and nano-scale fillers in liquid composite molding processes, focusing on how particle size and morphology affect resin rheology, flow behavior, and filler filtration within fiber preforms. Glass microspheres and organo-modified montmorillonite were dispersed in epoxy resin and injected through glass-mat preforms at different fiber volume fractions (ranging from 0.27 to 0.47). Our study integrates rheological characterization, in situ flow-front tracking, unsaturated permeability analysis, thermogravimetric quantification of retained particles, and microstructural observations by SEM. Despite their smaller loading, nanoclay suspensions showed a markedly higher viscosity increase than microsphere systems, yet their permeability remained nearly unchanged. In contrast, microsphere-filled resins exhibited strong filtration at the flow inlet, density-driven settling near the lower tool face, and significant permeability loss. The results demonstrate that nano-fillers, although more viscous, maintain homogeneous distribution and flow continuity, whereas micro-fillers promote cake formation and local compaction. This controlled side-by-side comparison clarifies how filler size and shape govern filtration mechanisms in liquid composite molding (LCM), providing design guidelines for processing filled resin systems without compromising part quality. Full article

In this study, Delrin^® (POM) polymer was reinforced with 15 wt.% chopped ramie fiber (RF) to develop a sustainable composite, which was injection-molded and machined using abrasive waterjet machining (AWJM). SEM revealed a skin-core morphology with flow-induced RF alignment and small voids at bundle crossovers, indicating interfacial adhesion. A Taguchi L₉ (3³) design evaluated waterjet pressure (WJP: 100–300 MPa), traverse speed (TS: 100–200 mm/min), and stand-off distance (SoD: 1–3 mm) on kerf width (KW) and surface roughness (SR). Increasing WJP from 100 to 300 MPa lowered mean SR from 6.23 to 4.80 µm (23% reduction) and KW from 1.31 to 1.07 mm (reduction of 18%); enlarging SoD from 1 to 3 mm raised SR from 4.98 to 5.55 µm (an 11% increase) and KW from 1.12 to 1.20 mm (a of 7% increase); and raising TS from 100 to 200 mm/min narrowed KW from 1.24 to 1.11 mm (a 10.5% reduction) with a modest SR decrease from 5.45 to 5.28 µm. ANOVA confirmed WJP as the dominant factor for SR (79.8%), as well as a significant SoD (18.3%). For KW, the influence of WJP (68.8%) was substantial, followed by TS (19.9%) and SoD (11%). Linear models captured the trends well (SR: R² = 88.29%; KW: R² = 93.36%). A desirability-based multi-response optimizer yielded ideal conditions for TS (200 mm/min), WJP (300 MPa), and SoD (1 mm), predicting a KW of 0.94 mm and an SR of 4.1567 µm. Confirmation tests produced a KW (0.970 ± 0.01 mm) and SR (4.27 ± 0.05 µm), which are within 3.19% and 2.73% of the predicted values, validating the DoE regression approach. Full article

Different strategies have been proposed to optimize injection mold cooling to reduce cycle time and improve efficiency. While recent research has focused on the design of additively manufactured conformal cooling inserts, the impact of mold maintenance conditions on cooling performance has received limited attention, particularly regarding the formation of limescale. This work presents a numerical modeling approach to quantify the combined effects of thermal resistance and hydraulic restriction caused by limescale accumulation in high-aspect-ratio cooling channels used in slender mold cores. An integrated thermal-fluid analysis is employed to evaluate coolant flow behavior and heat-transfer performance and to assess their coupled influence on cooling efficiency and part dimensional stability. The results show that, in slender cooling channels, even thin limescale deposits can significantly reduce cooling performance, with hydraulic restriction emerging as the dominant mechanism under the investigated conditions due to the reduced effective flow area. Design strategies that reduce effective frictional length and mitigate limescale deposition reduced part temperature by approximately 10 °C and shortened cooling time by about 17%. Further optimization of coolant flow conditions yielded an additional 65% reduction in cooling time. These findings highlight the importance of integrating cooling design with preventive maintenance to achieve robust injection molding performance. Full article

The recycling of technical textile waste represents a major challenge due to the complex and multilayered structure of these materials. Firefighter protective clothing, mainly composed of high-performance aramid fibers combined with polymeric membranes and auxiliary textile components, is commonly landfilled or incinerated at the end of its service life, resulting in a significant environmental impact. This work utilized recycled aramid-rich textile waste obtained from end-of-life firefighter protective clothing as reinforcement for polyamide 6 to develop high-performance thermoplastic composites within a circular economy framework. Composites containing 15, 30, 45, and 60 wt.% of recycled textile waste were manufactured by melt compounding followed by injection molding. In addition, a selected formulation containing 30 wt.% reinforcement was compatibilized using an amino-functional silane to improve interfacial adhesion. The materials were systematically characterized in terms of tensile properties, thermal behavior, thermomechanical performance, water uptake, flammability, colorimetric properties, and fracture morphology by field emission scanning electron microscopy. The results revealed a pronounced increase in stiffness and thermomechanical stability, with tensile strength increasing from approximately 65 MPa for neat PA6 up to 78 MPa at 30 wt.% reinforcement, and elastic modulus exceeding 5000 MPa at high reinforcement contents. An optimal balance between mechanical performance and ductility was achieved at 30 wt.% reinforcement, while higher contents enabled a substantial extension of the service temperature range, with HDT values increasing from 55 °C for neat PA6 up to 173 °C for highly reinforced systems. FESEM analysis confirmed improved interfacial adhesion in silane-compatibilized systems, explaining the enhanced mechanical and thermomechanical behavior. Furthermore, the incorporation of recycled aramid-rich textile waste led to a significant improvement in flame retardancy, enabling UL-94 V-0 classification at 30 wt.% reinforcement and above, without the use of additional flame-retardant additives, enabling UL-94 V-0 classification without additional flame-retardant additives. Overall, this study demonstrates the technical feasibility and high added-value potential of valorizing firefighter protective clothing waste into advanced PA6-based composites with enhanced mechanical, thermal, and fire-resistant properties, providing a sustainable route for the valorization of high-performance textile waste. Full article

Maintaining stable molding quality in hydraulic injection molding machines is difficult because the internal state of molten resin cannot be directly observed and varies with material properties and operating conditions. This difficulty is intensified by variations in hydraulic characteristics caused by oil temperature changes. This study proposes an adaptive feedforward control (AFC) framework that improves injection velocity tracking while utilizing AFC learning parameters as indicators of resin state. AFC is implemented as a multi-frequency feedforward controller whose parameters are updated through repetitive injection cycles. To overcome the limited learning duration within a single injection shot, a shot-to-shot compensation mechanism accumulates and transfers learning results across consecutive shots. Experiments are conducted on a hydraulic injection molding machine using polypropylene materials with different viscosities. The results show that the converged AFC learning parameters vary systematically with material changes and correspond to differences in molded product appearance. Furthermore, by adjusting the cylinder temperature of another material, the AFC parameters converge to values close to those of a reference material, resulting in similar molded products. These findings demonstrate that AFC learning parameters reflect variations in resin state and can serve as practical state indicators for aligning molding conditions. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article