Search Results (1,122)

This study evaluated the feasibility of mechanical recycling of plastic waste recovered from the disassembly of end-of-life lithium iron phosphate battery cases. In particular, the work focused on the case fraction, corresponding to the outer battery envelope, which represents the largest share of the plastic waste by weight. A preliminary characterization was carried out to determine the physico-chemical properties of the material and assess its suitability for mechanical recycling. The polymer matrix was found to consist of polypropylene, with glass fibers and calcium carbonate as inorganic fillers, each present at 18 wt%. X-ray fluorescence and elemental analysis confirmed the absence of elements of concern and subsequently, recycled material processability was investigated, with particular attention to injection molding, the same technology used to manufacture the original battery cases. Rheological analyses confirmed that the material exhibits Newtonian rheological behavior, suitable for injection molding, as also supported by a melt flow index value equal to 7.5 g/10 min (230 °C, 2.16 kg). The material was then reprocessed, and the resulting specimens were subjected to mechanical tests, with impact energy equal to 30.5 kJ/m² and flexural strength amounting to 36.8 MPa. Overall, the recycled material exhibited adequate processability and mechanical performance after one reprocessing step, suggesting its potential for high-value mechanical recycling. Full article

Small and Midsized Manufacturers (SMM) face challenges as they adopt digital technologies to transform their production environment. A Manufacturing Execution System (MES) requires timely accurate data from shop floor processes to efficiently control production operations. An Industrial Internet of Things (IIoT) platform of sensors provides MES software with operational information through a communications network to enable data-driven decision-making. A midsized manufacturer in southeastern Pennsylvania provides comprehensive thermoformed and injected molded products for diverse markets. Their production equipment includes light and heavy gauge thermoforming, polymer calendaring, and Computer Numerical Control (CNC) part trimming equipment supported by air compressors, vacuum pumps, and water chillers. This project partnered a manufacturer with researchers to deploy engineering and information science students to access, catalog, and characterize shop floor Programmable Logic Controllers (PLC) inputs and outputs. Utilizing this critical PLC data, the expert lead team determined quality-critical parameters, machine counters, and fault codes essential for process optimization. Full article

Injection molding is one of the most widely adopted manufacturing methods in the plastics industry, owing to its high efficiency, design flexibility, and mass production capabilities. Throughout the development and application of Injection molding technology, trade-offs are pervasive, arising from competing requirements such as processability versus material performance, productivity versus quality, and simplicity versus functionality. Injection molding simulation itself embodies such trade-offs, as it is used to design increasingly complex processes and mold systems to achieve improved material properties and part performance, while inevitably balancing physical accuracy against computational efficiency and modeling cost. This review examines typical modeling options in injection-molding simulation from an accuracy–complexity trade-off perspective. The modeling strategies adopted in the primary stages of the molding cycle—namely, the injection, packing, and cooling phases—are systematically reviewed, with emphasis on how simplifying assumptions are introduced to manage numerical complexity. By organizing existing research through the lens of qualitative trade-offs, this review aims to support a more structured understanding of model selection in injection molding simulation for both academic studies and industrial applications. Full article

The foam injection molding process for expanded polypropylene (EPP) offers advantages in energy efficiency, material savings, and mechanical performance, making it suitable for automotive and packaging applications. However, its nonlinear and multivariable nature makes accurate prediction and process optimization difficult using traditional methods. This study presents an AutoML-based surrogate modelling framework for predicting and optimizing two key process outcomes: cycle time and warpage. Experimental data were obtained from 81 production trials conducted on a Teubert injection molding machine using five process parameters. The PyCaret library was used to automate model selection, hyperparameter tuning, and performance evaluation, and 18 regression algorithms were compared. The results showed that tree-based ensemble models clearly outperformed traditional linear models. Gradient Boosting Regressor achieved an R² of 0.9671 for cycle time, while Extra Trees Regressor reached an R² of 0.9874 for warpage. Beyond prediction, the finalized surrogate models were used for Monte Carlo-based design space exploration with 1000 synthetic parameter combinations. Non-dominated solutions were identified to construct the Pareto front, and the top-ranked settings were compared with experimental results. The findings show that AutoML-based surrogate modelling can support both accurate prediction and process window identification in EPP foam injection molding. Full article

In-Mold Electronics (IMEs) present a highly promising monolithic integration strategy for manufacturing miniaturized 3D MEMS optical tweezers, offering exceptional environmental adaptability and structural compactness. However, the precision of such optical systems is heavily constrained by the injection molding process. During the molding phase, high-pressure melt scouring and severe thermo-mechanical coupling frequently induce geometric misalignment, manifesting as node displacement, localized warpage, and residual stress accumulation in the embedded circuits. This displacement critically alters the cross-sectional area of conductive traces, leading to resistance fluctuations that can destabilize the driving current. According to American Wire Gauge (AWG) standards, ensuring the geometric fidelity of this sensor-CPU interconnect pathway is fundamental to maintaining signal integrity. To address these manufacturing bottlenecks, this study systematically investigates the process stability of IME circuits Cyclic Olefin Copolymer (COC) is strategically selected as the substrate material over Polycarbonate (PC) and Liquid Silicone Rubber (LSR) due to its ultra-high light transmittance, extremely low water absorption, and superior thermomechanical stability. Based on finite element simulation, a data-driven intelligent optimization framework is developed. Latin Hypercube Sampling (LHS) is first utilized to efficiently sample the multi-dimensional process space, comprising melt temperature, packing pressure, and packing time. To handle the non-stationary nature of process feedback signals, wavelet analysis is introduced to decouple high-frequency noise, extracting Wavelet Energy Entropy (WEE) as a highly robust dynamic metric for process stability. Subsequently, a hybrid NSGA-II-MOPSO multi-objective algorithm is deployed to cooperatively optimize the injection parameters. The simulation-based optimization results demonstrate a substantial enhancement in manufacturing precision. Under the optimal parameter configuration, the average node displacement of the embedded circuits decreases significantly from 0.034 mm to 0.014 mm, achieving a 58.82% reduction. Simultaneously, volumetric shrinkage drops from 5.755% to 4.832% (a 16.04% reduction), while residual stress is maintained well within the structural safety threshold of optical-grade polymers. By clarifying the deformation control mechanism during the manufacturing phase, this study provides a highly reliable, data-driven methodological framework for the precision mass production of micro-nano optical systems. Full article

Carbon fiber-reinforced thermoplastic composites (CFRTPs) have attracted increasing attention in aerospace, transportation, marine engineering, and other advanced manufacturing fields owing to their high specific mechanical properties, impact resistance, weldability, reprocessibility, and potential recyclability. However, the high melt viscosity of thermoplastic matrices, the permeability limitations associated with different reinforcement architectures, and the chemical inertness of carbon fiber surfaces continue to restrict resin impregnation, interfacial bonding, defect control, and forming stability. This review systematically summarizes recent advances in CFRTP manufacturing from the perspective of material-derived processing challenges and interface engineering. First, representative thermoplastic matrix systems and reinforcement architectures are discussed, with emphasis on their effects on processability, crystallization behavior, resin flow, and load transfer. Subsequently, six major forming processes, including hot stamping, injection molding, pultrusion, filament winding, automated fiber placement, and additive manufacturing, are critically compared in terms of processing principles, typical defects, technical limitations, and application boundaries. Particular attention is given to process-induced quality issues such as voids, wrinkling, springback, fiber breakage, warpage, insufficient consolidation, and weak interlayer bonding. Finally, interface engineering strategies, including chemical surface modification, interfacial structural design, and functional interlayer design, are reviewed as practical routes to improve wetting, shorten impregnation pathways, and enhance fiber–matrix load transfer in high-viscosity thermoplastic systems. This review highlights that CFRTP manufacturing should be understood as a coupled materials–processing–interface problem rather than a single forming operation. Future development is discussed with emphasis on reproducible manufacturing, processability-oriented materials, scalable interface engineering, predictive modeling, and standardized structural validation. Full article

Tritium breeding blanket is a key component of future fusion power plants, and its performance depends on the selection, fabrication, and qualification of lithium-based ceramic material. Among the proposed lithium ceramics materials, the main candidates for ceramic breeders are lithium orthosilicate (Li₄SiO₄) and lithium metatitanate (Li₂TiO₃). These advanced ceramics and their biphasic composites are the leading candidates due to their high lithium density, favorable tritium breeding ratio (TBR ≈ 1.15–1.25 with Be₁₂Ti multiplier and 90% ⁶Li enrichment), and robust thermo-mechanical behavior within the 200–900 °C operational window of helium-cooled pebble bed (HCPB) blankets. This review provides an engineering-oriented assessment covering fabrication routes (solid-state, hydrothermal, melt-based, drip casting, powder injection molding, microwave sintering, and digital light processing additive manufacturing); microstructure–property relationships and performance under neutron irradiation; and tritium generation, retention, and release as functions of chemical composition, defect structure, and operating temperature. Induced radioactivity of Li-based ceramics and key impurity elements is quantified using activation formalisms applied to WWR-K reactor conditions, providing guidance for raw-material selection and waste-management assessment. Authors’ original contributions include (i) an empirical model of pebble crush load vs. biphasic composition (R² > 0.99); (ii) two universal semi-empirical kinetic models (exponential growth and non-linear strength degradation, R² = 0.97–0.99) for nine structural and mechanical parameters of Li₂TiO₃ under He²⁺ and H⁺ irradiation; (iii) a consolidated table of Arrhenius tritium diffusion parameters from reactor experiments and DFT; and (iv) an induced radioactivity calculation for the biphasic system with two-exponential post-irradiation decay analysis. The review identifies biphasic Li₄SiO₄–Li₂TiO₃ composites with ~30 ± 5 mol.% Li₂TiO₃ as particularly promising and formulates specific data gaps and modeling needs for the reliable deployment of ceramic breeder pebbles in helium-cooled fusion blanket systems. It should be specifically noted that Li₄SiO₄ pebbles fabricated via the melt method, as an example, typically exhibit exceptionally high densities, generally exceeding 90% of the theoretical density (TD). Building on the calculation of induced radioactivity, it is crucial to consider the microstructural distribution of highly radioactive nuclides (e.g., Co, Mn) within the ceramic matrix. If these impurities segregate at grain boundaries rather than being homogeneously distributed, there is a potential pathway to develop targeted wet-chemical methods, such as selective acid leaching, to remove these impurities post-irradiation, thereby lowering the waste disposal classification. Full article

This study examines the effects of 2 wt% orotic acid (OA) nucleation and injection molding conditions on the crystallization behavior and thermo-mechanical performance of polylactic acid (PLA). Differential scanning calorimetry and X-ray diffraction revealed that 2 wt.% OA accelerates crystallization, enabling molded PLA to achieve crystallinity levels as high as 52–53% under low packing pressure and long hold time. Mechanical testing showed that tensile modulus increased with longer hold time, while tensile strength decreased due to constrained relaxation in the skin layer. Flexural strength increased with packing pressure, whereas flexural modulus decreased as the degree of crystallinity decreased under higher pressure conditions. Heat deflection temperature (HDT) showed the greatest sensitivity to processing, rising from 58 °C to 100–131 °C in optimized PLA–OA samples. The highest HDT values occurred under conditions that promoted both high crystallinity and extended lamellar development with strong α-phase formation. These results demonstrate that combining OA nucleation with controlled injection molding enables high-crystallinity, high-HDT PLA without post-annealing, offering a viable route for producing thermally stable PLA components suitable for hot-fill and reheatable food packaging applications. Full article

The Fifth Industrial Revolution (Industry 5.0) requires human-centric mechanisms that preserve the integrity, reproducibility, and interpretability of AI-driven decisions in smart manufacturing. Injection molding generates heterogeneous, imbalanced, and weakly labeled process data, posing reliability and integrity risks to data-driven quality control. This study proposes an integrity-verified and reproducibility-instrumented secure machine learning framework for operating-regime analysis in injection molding that integrates (i) SHA-256-based data-integrity verification at ingestion, (ii) Pearson correlation-based feature selection, and (iii) a Gaussian Mixture Model (GMM) under a passive-adversary threat model with Transport Layer Security (TLS)-secured transmission. Evaluated on real industrial data (n = 6719 cycles, seven process variables), correlation-based feature selection retained four non-redundant variables and improved the GMM Silhouette Score from 0.274 ± 0.075 (all features) to 0.323 ± 0.014 (95% CI [0.318, 0.329]), a +18.2% relative improvement (paired t(29) = 3.39, p = 0.002; Cohen’s d = 0.62; Wilcoxon p = 0.022), while lowering the Davies–Bouldin Index from 1.63 to 1.17. The Silhouette standard deviation of 0.014 over 30 seeds meets the σ ≤ 0.02 reproducibility target. The GMM resolves four interpretable operating regimes—one low-load regime consistent with nominal operation and three elevated-load regimes (left-side, right-side, and bilateral)—with operator-readable per-variable signatures. Relative to hard-partition and projection baselines, the GMM is not Silhouette-optimal but provides an interpretable, generative regime model that meets the σ ≤ 0.02 reproducibility target. The framework operationalizes human-centric manufacturing security as measurable integrity, reproducibility, and interpretability. Full article

Optimizing the injection molding of short-fiber-reinforced thermoplastics (SFRTs) is a persistent challenge due to the complex interplay between processing parameters and final mechanical performance. To address this, we developed and validated a machine learning (ML) pipeline to maximize both the tensile strength and Charpy impact resistance in polyamide 6 with 30% glass fiber (PA6-GF30). Through a designed experimental campaign, we systematically varied four key process parameters—melt temperature (260–300 °C), injection pressure (600–1000 bar), packing pressure (400–800 bar), and cooling time (15–35 s). The resulting dataset was used to train and compare three different regression models: Random Forest (RF), Gradient Boosting (GB), and Support Vector Regression (SVR). Our findings indicate that the Gradient Boosting (GB) algorithm yielded the most reliable predictions, significantly outperforming the other evaluated models. Further analysis using SHAP (Shapley Additive exPlanations) identified packing pressure as the dominant factor influencing tensile strength (contributing approximately 40% to the prediction), while melt temperature emerged as the key driver for impact resistance (around 35% contribution). By integrating our best-performing GB model with a multi-objective genetic algorithm, we identified an optimal set of parameters that simultaneously enhances both mechanical properties. Among the evaluated models (Random Forest, Support Vector Regression, and Gradient Boosting), the Gradient Boosting algorithm achieved the highest predictive accuracy. Compared to the baseline condition (280 °C melt temperature, 800 bar injection pressure, 600 bar packing pressure, 25 s cooling time), experimental validation of these optimized settings demonstrated substantial improvement: tensile strength increased from 145 MPa to 171 MPa (an 18% enhancement), and impact resistance rose from 45 kJ/m² to 55 kJ/m² (a 22% gain). This work establishes that an integrated ML and optimization framework can serve as a transformative approach for high-precision manufacturing of advanced engineering polymers. The primary novelty of this work lies in the development of a fully integrated, bias-free methodological framework that explicitly couples physical interpretability with multi-objective optimization, bridging the critical gap between black-box predictions and actionable industrial insights. Full article

Bio-based and biodegradable polymer composites based on polylactic acid (PLA) and polybutylene succinate-co-adipate (PBSA) were developed for rigid food packaging applications. Agro-industrial residues consisting of ground leaves and branches derived from tangerine tree cultivation (pruning) were used as fillers at high loading (30 wt%) before (PRE) or after (POST) extraction of bioactive compounds. The influence of blend composition (PLA/PBSA 60/40 and 30/70), filler extraction, and the addition of antioxidants (0.5 wt%) on material properties was systematically investigated. Composites were processed via extrusion and injection molding and characterized through FTIR, SEM, tensile testing and thermal analysis. The results show that polymer blend morphology affects mechanical behavior, with co-continuous structures (60/40) exhibiting improved ductility compared to dispersed systems (30/70). The incorporation of lignocellulosic residues increased stiffness but reduced elongation at break. Extraction treatment significantly modified filler morphology and interfacial interactions, slightly improving dispersion and processability. The effect of the extracted bioactive compounds on the thermal stabilization of biocomposites was also investigated. Overall, the findings demonstrate the potential of combining biodegradable polymer blends with treated agricultural residues to produce sustainable rigid packaging materials while supporting a bio-circular approach. In fact, preliminary extraction of valuable compounds from tangerine pruning waste appears to be a convenient strategy for its efficient cascade valorization. Full article

Micro-structured SAE H13 tool steel inserts for polymer injection molding require accurate replication of sub-millimeter features while retaining adequate densification and heat-treatment response. This study compared two powder-based routes on the same hemispherical insert containing pyramidal features of approximately 0.145 mm base width: hot embossing (HE) of water-atomized SAE H13 powder (supplier d50 = 5.7 µm, irregular morphology) compounded with a commercial M1 binder, and material extrusion (MEX) of a commercial gas-atomized SAE H13 filament processed on a Markforged Metal X. Rheological screening selected a 57:43 vol% powder-to-binder ratio for the in-house HE feedstock, and DSC/TGA measurements defined two-step debinding windows. The best HE conditions were 220 °C, 8 MPa, and 45 min for the in-house mixture, and 210 °C, 8 MPa, and 30 min for the granulated commercial filament; the latter showed a 0.15% linear deviation from the silicone replica diameter among the best-rated samples. Under the tested commercial MEX configuration, the pyramidal features were not resolved because the 0.40 mm deposition line width exceeded the target feature base width, causing the slicer to omit the sub-line-width geometry. The defect populations differed qualitatively: HE specimens showed porosity and local cracking associated with powder morphology and pressureless sintering, whereas MEX specimens showed build-direction-aligned inter-raster voids associated with the toolpath. Microhardness and tensile data are therefore interpreted as process-history-specific results rather than as a direct route ranking, because sintering conditions were not uniform across all specimens. The study defines an experimentally bound process-selection limit for SAE H13 micro-tooling: HE remains preferable for sub-nozzle surface features, whereas MEX remains attractive for macro-scale geometric freedom, if resolution, densification, and post-sintering consolidation are addressed. Full article

This contribution presents a simulation-based approach for optimizing injection molding processes using digital twins. It combines surrogate modeling via response surface methodology (RSM) with the evolutionary algorithm NSGA-II to efficiently capture complex relationships between process parameters and objectives. A key element is the adaptive enhancement of the training dataset within the decision-relevant region of interest (ADEROI) by a modified greedy max–min algorithm. This strategy closes data gaps, improves model accuracy in the potentially optimal region, and directs additional simulations to informative areas. Leave-one-out (LOO) and hold-out (HO) cross-validations show strong root mean square error (RMSE) and $R^2$ values for deformation, shrinkage, cycle time, and mass. NSGA-II converges after 403 generations and results in 191 Pareto-optimal solutions, which are consolidated into preference-consistent operating points. These points make trade-offs between analyzed objectives’ deformation, shrinkage, and cycle time explicit for process pre-design. Preferred solutions are identified through weighted sums of normalized objectives and inversely mapped process parameters. Their agreement with the physics-based digital twin at the hundredths level supports the plausibility of the selected operating points within the investigated simulation-based workflow. A retrospective benchmark against a scaled single-stage LHS baseline shows that ADEROI achieves ROI-equivalent point density with fewer simulation runs for the investigated case, reducing the estimated runtime by $39.1\%$ and resulting in a $1.64\times$ speed-up. The quantitative validation is limited to one thin-walled PP keyholder component; further geometries, mold layouts, and polymer materials are required to empirically assess generalizability. Full article

This study aims to develop sustainable conductive nanocomposites based on poly(lactic acid) (PLA)/poly(ε-caprolactone) (PCL) blends reinforced with multi-walled carbon nanotubes (MWCNT) and graphene nanoplatelets (G), focusing on their multifunctional performance. The novelty lies in the production of hybrid nanocomposites based on PLA/PCL blends with MWCNT/G using conventional industrial processing techniques, enabling the development of eco-friendly nanocomposites with tailored electrical, mechanical, and electromagnetic properties. The nanocomposites were prepared by twin-screw extrusion followed by injection molding. Rheological, scanning electron microscopy (SEM), mechanical, thermal, thermomechanical, electrical conductivity, and electromagnetic shielding properties were systematically evaluated. From a rheological perspective, the PLA/PCL/MWCNT and PLA/PCL/MWCNT/G nanocomposites exhibited a plateau at low frequencies, associated with the formation of a percolated network. This was confirmed by the significant increase in electrical conductivity and electromagnetic shielding response. The morphology observed by SEM showed a refinement of the PCL phase in the PLA matrix with the incorporation of MWCNT. The PLA/PCL/MWCNT/G (4/2 parts per hundred resin, phr) nanocomposite showed a 309% increase in impact strength compared to neat PLA, while maintaining the heat deflection temperature (HDT). The elastic modulus exceeded 2300 MPa and accelerated the crystallization process by more than 15 °C compared to PLA, which makes it important to reduce injection molding time. Additionally, it exhibited the highest electrical conductivity level, around 6.79 × 10⁻⁵ S/cm, which resulted in improved electromagnetic shielding performance in the 8.2–18 GHz range, highlighting the synergistic effect between 1D and 2D fillers. The developed PLA/PCL/MWCNT and PLA/PCL/MWCNT/G nanocomposites demonstrate potential for antistatic applications, combining sustainability with multifunctional performance and industrial scalability. Full article

Eco-friendly biocomposites were prepared from poly(lactic acid) (PLA) plasticized with polyethylene glycol (PEG) and reinforced with Moroccan sugarcane bagasse fibers at 5, 10 and 15 wt%. The aim was to enhance PLA ductility through PEG incorporation while valorizing locally available lignocellulosic residues. Two processing methods, injection molding and melt extrusion additive manufacturing (MEX, 3D printing), were employed to investigate the influence of manufacturing method on the morphological, thermal, rheological and mechanical properties of the composites. Thermal analysis confirmed that PLA maintained its stability within the processing temperature range, supporting its suitability for MEX. Morphological observations revealed improved fiber dispersion and reduced porosity in injection-molded samples, whereas MEX-printed parts exhibited visible interlayer voids. These microstructural differences explained the superior tensile strength and modulus of injection-molded specimens compared to MEX ones. Full article