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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine Learning-Based Process Control for Injection Molding of Recycled Polypropylene

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.

Krantz, Joshua↗
DOE OSTI2022

MODELLING FIBER ORIENTATION DURING ADDITIVE MANUFACTURING COMPRESSION MOLDING PROCESSES

The production of high-performance thermoplastic composites reinforced with short carbon fibers can be achieved by a novel “additive manufacturing-compression molding” technique. An advantage of such combination is two-fold: controlled fiber orientation in additive manufacturing and less void content by compression molding. In this study, a computational fluid dynamics model has been developed to predict the behavior of printed layers during fiber-reinforced thermoplastic extrusion and subsequent compression molding. The fiber orientation was modelled with a simple quadratic closure model. The interaction between the fibers is included using a rotary diffusion coefficient which becomes significant in concentrated regimes. Finally, the second order orientation tensor is coupled with the momentum equation as an anisotropic part of the stress term. The effect of processing parameters on the behavior of printed layers was investigated to determine the favorable printing scenarios. The developed numerical model enables design of high-performance composites with tunable mechanical properties.

Seta, Berin↗
DOE OSTI2022

MODELLING OF ADDITIVE MANUFACTURING – COMPRESSION MOLDING PROCESS USING COMPUTATIONAL FLUID DYNAMICS

A computational fluid dynamics model has been developed to predict the behavior of a printed strands during a novel material extrusion additive manufacturing and compression molding process. While the traditional additive manufacturing process enables control over the fiber orientation within the part, it would also result in high void content. On the other hand, compression molding produces parts with low porosity levels at rapid processing cycle time but lacks control over the microstructure. The novel additive manufacturing – compression molding (AM-CM) integrating both these processes offers control over both the microstructure and porosity to manufacturing high performance composite parts. The numerical model developed here enables to analyze the effect of processing parameters on the behaviour of printed layer that is subsequently compressed and to determine the optimal printing parameters for high-performance composite part design.

Seta, Berin↗
DOE OSTI2022

Long fiber injection molding

An improved method for obtaining high fiber volume fraction, long fiber injection molded articles is provided. According to one embodiment, the method includes forming an injection molding feedstock by cutting pre-impregnated fiber-reinforced tape into platelets. The platelets can be coated with a thin layer of polymer to allow sliding of the platelets with respect to each other at the early stages of plastication, rather than forcing relative motion of fibers with respect to each other. The method can further include the dispersion of material only in the final stages of the injection molding screw to promote gentle motion of the feedstock at the earlier stages of the plastication process. The method allows improvement of mechanical properties of articles manufactured with equipment and techniques that are prevalent in high volume automotive and consumer industries.

Kunc, Vlastimil↗
DOE OSTI2023

Process Variability Effects on Tensile Response in Injection Molded, Fluorinated Thermoplastics

The mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight-averaged molecular weight. Parallel plate rheology was used to obtain the zero-shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero-shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not presented for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens' sides as a flash due to higher mobility with CTFE segments.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Material extrusion with integrated compression molding of NdFeB/SmFeN nylon bonded magnets using small- and large-scale pellet-based 3D-printers

High-density bonded rare-earth magnets are manufactured using pellet-fed additive manufacturing (AM)/material extrusion and an integrated additive manufacturing-compression molding (AM-CM) process. Neodymium iron boron – samarium iron nitride in polyamide 12 (NdFeB-SmFeN/PA12) of 93 % weight fraction (65 % volume fraction) are used for the study. The mechanical properties (tensile strength and modulus), magnetic properties (maximum energy density, coercivity, remanence) are reported. Manufacturing parameters such as layer height, barrel temperatures, screw speed and gantry feed rate are optimized to obtain the highest possible density of the magnets using a small-scale desktop material extrusion printer. Large scale integrated additive manufacturing-compression molding (AM-CM) is then utilized to increase the density of the magnets by reducing porosity defects common in the material extrusion process. The density of as-printed magnets was 5.2 g/cm 3 with a BH max value of 124.14 kJ/m 3 , tensile strength of 20 MPa and a modulus of 2 GPa. AM-CM increased the density of the compound by 5.5 % (5.49 g/cm 3 ). The reduction in porosity was confirmed using X-ray tomography (XCT). Improvement in mechanical strength of the material was also observed, with an increase in tensile strength of 25 % (25.09 MPa) and increase in tensile modulus of 275 % (5.49 GPa). Scanning electron microscopy showed increased particle-matrix adhesion with the integrated AM-CM process.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Numerical framework for integrated additive manufacturing-compression molding (AM-CM) of thermoplastic composites

Additive manufacturing-compression molding (AM-CM) has emerged as a transformative technology in advanced composite manufacturing. Additive manufacturing (AM) offers high design flexibility and the ability to produce complex geometries with precisely aligned fibers in the preferred orientation. Compression molding (CM) enhances composite materials by providing excellent dimensional stability, reduced porosity, high production rates, and a smooth surface finish. Despite these advantages, extensive integrated analysis is required to optimize processing conditions for improved fiber orientation distribution (FOD) and porosity control. Here, this study develops a comprehensive numerical model to simulate the AM-CM manufacturing process. The model isolates the effects of both the AM and CM phases while also capturing their integration. Additionally, it accounts for heat transfer, temperature-dependent viscosity, and fiber orientation in the extruded fiber-filled polymer, accurately representing material behavior during processing. This approach enables the analysis of interactions between deposited beads of complex strand shapes and their interface regions after full compression. Moreover, the model predicts key parameters such as polymer flowability, fiber orientation, and temperature evolution in AM-CM parts. By optimizing processing conditions, it facilitates a controlled and predictable microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Simulation-driven design optimization of reaction injection molding (RIM) process for polydicyclopentadiene (pDCPD): Minimizing cycle time, defects, and warpage

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this study, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. In conclusion, this optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption.

42 ENGINEERING↗
DOE OSTI2022

A level set approach for the computational study of a yield stress fluid filling a thin mold

Many important engineering and scientific applications such as cement slurries, foams, crude oil, and granular avalanches involve the concept of yield stress. Therefore, modeling yield stress fluids in different flow configurations, including the accurate prediction of the yield surface, is important. In this paper, we present a computational model based on the finite element method to study the flow of yield stress fluids in a thin mold and compare the results with data from flow visualization experiments. Here, we use the level set method to describe the interface between the filling fluid and air. We use polypropylene glycol as a model Newtonian fluid and Carbopol for the model yield stress fluid, as the Carbopol solution demonstrates yielding without thixotropy. To describe the yielding and shear-thinning behavior, we use a generalized Newtonian constitutive equation with a Bingham–Carreau–Yasuda form. We compare the results obtained from the mold filling experiments with the results from the three-dimensional (3D) model and from a reduced-order Hele-Shaw (HS) model that is two-dimensional, including the effect of shear-thinning along the thin direction only approximately. We show that both the 3D and the HS model can capture the experimental meniscus shape reasonably well for all the fluids considered at three different flow rates. This indicates that the shape evolution is insensitive to the dimensionality of the model. However, the viscosity and yield surfaces predicted by the 3D and HS models are different. The HS model underestimates the high viscosity and unyielded regions compared to the estimation by the 3D model.

42 ENGINEERING↗
DOE OSTI2021

Compression molding of anisotropic NdFeB bonded magnets in a polycarbonate matrix

Anisotropic bonded Nd 2 Fe 14 B (NdFeB) magnets in a polycarbonate (PC) binder matrix are fabricated using a compression molding process. The weight fractions (w.f.) of NdFeB in PC on the batch mixer are 20, 50, 75, 85 and 95% compared to the twin screw extruder with 20, 50 and 75% respectively. The density of the 95% batch mixed magnets fabricated was 5.34 g/cm 3 and the magnetic properties are, intrinsic coercivity H ci = 942.99 kA/m, remanence B r = 0.86 T, and energy product (BH) max = 120.96 kJ/m 3 . Furthermore, the measured tensile properties are in the range of 27-59 MPa, comparable to that of polyamide (PA), polyphenylene sulfide (PPS) bonded magnets and demonstrating potential for bonded magnet applications. Scanning electron microscopy showed that the onset of failure occurs in the magnetic particle- matrix interface. This study demonstrates that compression additive molding technique can be used to fabricate high performance NdFeB polycarbonate composite magnets with improved mechanical properties.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Process Variability Effects on Tensile Response in Injection Molded, Fluorinated Thermoplastics

The mechanical properties of fluorinated thermoplastics (i.e., tensile strength and elongation) can vary with changes in injection molding processing parameters. Four fluoropolymers are examined: poly(vinylidene fluoride) (PVDF) and random poly(vinylidene fluoride-co-chlorotrifluoroethylene) (PVDF-CTFE) with three CTFE concentrations. Dog bones were manufactured with various cylinder dwell times and mold cooling times to assess the manufacturing sensitivity to the tensile response. Dwell and cooling times increasingly impact mechanical performance as CTFE concentration increases. Specimens exhibit higher tensile strength as a function of injection order. The first injected specimen exhibits the lowest tensile strength and highest elongation in all copolymers. This trend becomes more pronounced among fluoropolymers with higher CTFE concentration and lower weight-averaged molecular weight. Parallel plate rheology was used to obtain the zero-shear viscosity as a function of material type, process, and injection order. We found that in the copolymers, the first injected sample exhibited a lower zero-shear viscosity than the next, which indicates a lower molecular weight in the first injected specimen. This phenomenon was not present for the PVDF homopolymer. Copolymer mechanical uncertainties are hypothesized to result from the shorter molecular weight chains extruding out of the specimens’ sides as a flash due to higher mobility with CTFE segments.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Avirulent Isolates of Penicillium chrysogenum to Control the Blue Mold of Apple Caused by P. expansum

Blue mold is an economically significant postharvest disease of pome fruit that is primarily caused by Penicillium expansum. To manage this disease and sustain product quality, novel decay intervention strategies are needed that also maintain long-term efficacy. Biocontrol organisms and natural products are promising tools for managing postharvest diseases. Here, two Penicillium chrysogenum isolates, 404 and 413, were investigated as potential biocontrol agents against P. expansum in apple. Notably, 404 and 413 were non-pathogenic in apple, yet they grew vigorously in vitro when compared to the highly aggressive P. expansum R19 and Pe21 isolates. Whole-genome sequencing and species-specific barcoding identified both strains as P. chrysogenum. Each P. chrysogenum strain was inoculated in apple with the subsequent co-inoculation of R19 or Pe21 simultaneously, 3, or 7 days after prior inoculation with 404 or 413. The co-inoculation of these isolates showed reduced decay incidence and severity, with the most significant reduction from the longer establishment of P. chrysogenum. In vitro growth showed no antagonism between species, further suggesting competitive niche colonization as the mode of action for decay reduction. Both P. chrysogenum isolates had incomplete patulin gene clusters but tolerated patulin treatment. Finally, hygromycin resistance was observed for both P. chrysogenum isolates, yet they are not multiresistant to apple postharvest fungicides. Overall, we demonstrate the translative potential of P. chrysogenum to serve as an effective biocontrol agent against blue mold decay in apples, pending practical optimization and formulation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Boron‐polymer composites engineered for compression molding, foaming, and additive manufacturing

Abstract Boron (specifically 10 B) is the element of choice to shield thermal neutrons due to its large (n, α) cross‐section; however, very few polymer composites containing high boron concentrations are available. This study aimed to determine the maximum possible amount of boron that could be introduced into a polymer matrix. Diverse manufacturing techniques, ranging from additive manufacturing to compression molding, were employed to fabricate inks and filaments for 3D printing, foams, and flexible pads. Composites using siloxanes, poly(lactic acid), and acrylonitrile butadiene styrene containing up to 80 wt% boron were sucessufully fabricated. The addition of known plasticizers (polyethylene glycol) and reinforcing agents (carbon nanofibers and fumed silica) helped to overcome fabrication problems such as clogging of the printing nozzle or crumbling of compression molded parts. In addition, the thermal‐mechanical properties of these novel boron composites were determined and shown to vary according to boron concentration, presence of additives, and fabrication techniques utilized.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Substitution of virgin carbon fiber with low-cost recycled fiber in automotive grade injection molding polyamide 66 for equivalent composite mechanical performance with improved sustainability

Recycled carbon fiber has historically proven challenging to integrate into composite manufacturing due in no small part to the low-density, randomly oriented, discontinuous fiber format that results from typical recycling. Discontinuous fiber requires the use of alternative technologies than those traditionally applied to continuous fiber (e.g., hand lay-up, winding). Extrusion compounding is one such applicable technology, but material transfer into the system requires alternative feeding equipment or the use of an altered procedure as trialed in this research. In this study, an injection molding compound for automotive applications was prepared with recycled carbon fiber and compared against an existing commercial compound. Input fibers and molded compound were evaluated for mechanical performance, while relevant variables such as compounded fiber alignment and aspect ratio were compared to the existing baseline material to confirm a like-for-like composite material structure. Overall, analysis indicates that recycled fiber performs similarly to virgin fiber reinforcement, demonstrating that recycled fiber may be a viable drop-in replacement for short-fiber discontinuous applications.

42 ENGINEERING↗
DOE OSTI2024

Printed Metal Molds for Mainstream Automotive Production

The main focus of this work was to evaluate additive manufacturing of metal molds, with significantly reduced cost and/or procurement lead time, for production of large composite components for mainstream (> 100k units/yr) automobiles. A two-component large tool with conformal heating channels was designed, optimized, printed, machined, and tested in production. The resulting analysis indicates that large scale AM is suitable for manufacturing complex geometry large scale metal molds.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Enabling Next Generation Reaction Injection Molding (RIM) for Lightweight Structures

Replacing metal components in trucks, trailers, and buses with lightweight polymer composites is challenging due to high temperatures and complex manufacturing. The Reaction Injection Molding (RIM) process using Dicyclopentadiene (DCPD) resin offers a solution by producing robust parts with excellent stiffness, impact strength, and resistance properties. Simulations are essential for optimizing this process, predicting defects, and improving quality. However, most commercial software is tailored for thermoplastics, requiring thermoset users to generate their own datasets. In this project, a material data card for DCPD was developed to perform RIM simulations. Design of Experiments (DOE) was used to identify key factors affecting filling, curing, and warpage, aiming to minimize cycle time and defects. The simulations explored varying injection gate parameters (size, location, number) and process conditions (mold/resin temperature, injection/curing pressure). Results showed that gate design significantly impacts filling behavior and defects. A single central gate provided balanced flow with fewer defects, while two corner gates led to more defects. Additionally, lower injection pressure increased filling time, while higher mold temperature accelerated curing but led to more warpage. This optimization framework aims to enhance DCPD part performance and promote sustainable manufacturing by reducing waste and energy consumption. This research has been performed in collaborations with McClarin Composites. The research outcome has been submitted to the Journal of Manufacturing Processes.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Guide on 3-D Printed Molds for Precast Concrete

This publication provides essential background information and guidance on using three-dimensional (3-D) printed molds to fabricate precast concrete. Their advantages, requirements, applicability, and implementation are discussed, and a detailed case study shows how 3-D printed molds were used to produce precast concrete façade components for an actual construction project. The guide is an outcome of a collaboration between PCI and Oak Ridge National Laboratory.

Hun, Diana E.↗
DOE OSTI2021

Large-scale continuous carbon/glass fiber additive-compression molded composites

Additive Manufacturing (AM) or 3-D printing has advanced from small-scale desktop printers to large-scale printers. Most of the present large-scale printers utilize feedstock materials in the form of pellets to create composite structures. To create structurally robust composite parts, reinforcements in the form of short fibers (carbon or glass) are often used to impart mechanical properties to the printed parts. However, poor mechanical properties in Z-direction and high porosity of pellets-based printed composites compared to composite manufactured using traditional methods are serious concerns. The authors report a combined approach in the present work, where fiber reinforced composites are printed with a high-throughput continuous fiber deposition method followed by a secondary compression molding process. A specially designed end-effector mounted on a robotic arm is used to print composite preforms. Continuous comingled fibers (Thermofiber 12K CF-PA12, Thermofiber 12K S2-PA12, and Hybrid Thermofiber 12K CF-PA12+PEEK PA6) embedded in the thermoplastic nylon matrix are printed to create composite preform plaques. The printed preforms were further compression molded (CM) using a hydraulic hot-press to create highly consolidated composite parts. The mechanical properties of the continuous fiber composites produced by this combined approach are improved significantly due to the highly aligned continuous fibers and reduced porosity. Flexural strength, flexural modulus, and tensile modulus of AM-CM Thermofiber 12K CF-PA12 UD sample were 615.37 MPa, 75.65 GPa, and 122.23 GPa, respectively.

Kumar, Vipin↗