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Results for “Thermoplastic composite joining”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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13 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Oak Ridge National Laboratory Thermoplastic Composite Joining of Pultruded Continuous Fiber Spars for Multi-Functional Battery Enclosure

Oak Ridge National Laboratory (ORNL) worked with DowAksa Industry to investigate the potential of using PA66 material to produce larger composite parts, transcending the limitations of traditional presses. The project aimed to develop a suitable material for printing battery boxes using Nylon 66 infused with 40 wt.% carbon fiber, compatible with the ORNL Additive Manufacturing and Compression Molding (AM-CM) system. However, even with extensive work, it was concluded that the material is not compatible with the AMCM process and suitable for joining using compression technique. Main reason for the incompatibility were found to be very high crystallinity of the PA66 grade chosen for this work. To improve printability, a modifier (proprietary) was added to decrease crystallinity, but this led to reduced thermal stability and adhesion issues.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Fast-rate joining of thermoplastic composites using integrated additive manufacturing and compression molding process

The integration of additive manufacturing (AM) technology with compression molding (CM), has emerged as a high-performance composite manufacturing technology in recent years. In the AM-CM process, quickly deposited AM preform on a mold undergoes a rapid compaction cycle to fabricate structurally robust composite parts due to highly controlled fiber alignment (from AM) and reduced porosity (from CM). Currently, AMCM-based part size is limited by the size of the mold volume, thus posing a challenge to manufacture scalable parts. Here, this work focuses on joining techniques developed to enable fast rate joining of short fiber-reinforced thermoplastic composite parts using the AM-CM process. Acrylonitrile butadiene styrene resin reinforced with 20 wt% short carbon fiber composites was printed onto a flat mold and pressed under a hydraulic press. Fabricated panels were joined by the (a) mechanical impression at the joining interface and (b) over-molded continuous carbon fiber (CCF) sheet. Tensile tests were performed to characterize the joining strength of both mechanical impression-based joints and CCF over molded joints. Among the mechanical impression-based joints, a U-shape channel allowed the fibers to flow between two joint parts, and 280 % increased mechanical properties were observed. The continuous carbon fiber-based over molded joint CCF showed 350 % increase in tensile strength compared to the butt joints.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Macro-level mechanical interlocking: A rapid joining approach for additively manufactured compression molded composite panels

Composite joining typically involves multiple steps, such as drilling and surface treatment, as part of the manufacturing process, which leads to low throughput and long cycle times. In the present study, we demonstrated a macro-level mechanical interlocking (MI) based, rapid joining technique to assemble additively manufactured compression molded (AMCM) panels, enabling the production of parts larger than the mold dimensions. Composite panels made of 20 wt% short carbon fiber reinforced acrylonitrile butadiene styrene (CF/ABS) were joined using MI features of various geometries, namely tree (TR), dovetail (Dov), rectangle 2 (Rect2), and rectangle 1 (Rect1), and their in-plane strength was evaluated. The resultant strength of the tested MI joints reached up to 74 % of the baseline tensile strength (i.e., the ‘no joint’ case). Observations from optical and scanning electron microscopy revealed inadequate polymer diffusion between the adherends, indicating that the joint strength was primarily derived from mechanical interlocking. Additionally, the fracture surfaces exhibited stress-whitening marks, which were characterized using differential scanning calorimetry (DSC). The increase in melting enthalpy suggested local stretching of polymer chains due to MI. Finite element analysis (FEA) indicated that the Rect1 MI feature, which generated the lowest stress concentration, outperformed the others in terms of joint strength, achieving 42 MPa. As a demonstration of the MI joining method, a battery box tray measuring 108 cm × 34 cm using a mold with an effective dimension of 36 cm × 34 cm successfully manufactured, resulting in a part with an area three times larger than the mold. In conclusion, this study presents a promising approach to improving composite joining techniques while minimizing production complexities.

In-plane joining↗
DOE OSTI2024

Deep reinforcement learning for optimal control of induction welding process

Optimizing induction welding (IW) process parameters for the application of joining thermoplastic composites is challenging as it requires achieving complex spatiotemporal thermal characteristics along the weld-line to obtain desired weld quality. We formulate an optimal control problem which captures these requirements and seeks to optimize the IW coil speed using a fast-acting dynamic IW process model. We develop a novel Deep Reinforcement Learning (DRL) framework to solve this computationally challenging control problem and demonstrate via simulation study that the learned DRL feedback control policy results in better spatiotemporal thermal characteristics as compared to the current state-of-the-art.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Thermoplastic welding apparatus and related methods

Thermoplastic welding apparatus and related methods are disclosed. An example method includes providing a smart susceptor between composite parts that are to be joined via thermoplastic welding. The example method includes positioning the composite parts and the smart susceptor on a tooling surface within a cavity of a tooling apparatus and applying a seal to the composite parts and the tooling surface to form a vacuum chamber between the composite parts and the tooling surface at a welding joint of the composite parts; producing a magnetic field at the welding joint. The example method includes providing a vacuum in the vacuum chamber during a welding operation.

Matsen, Marc R.↗
DOE OSTI2020

Structural validation of a thermoplastic composite wind turbine blade with comparison to a thermoset composite blade

Reactive infusible thermoplastics have the potential to be advantageous for wind turbine blade composites because they are recyclable at end of life, can have reduced manufacturing costs, enable thermal joining and have similar, and in some cases better, structural properties than traditional thermoset epoxy composites. However, these materials are new to the wind industry and there is risk to investing in a material that has not been validated at a blade scale. Industry cannot adopt a new material such as this without large-scale validation and demonstration of system-level advantages in cost and/or performance. This paper presents structural characterization of a 13-m thermoplastic composite wind turbine blade compared to an identical geometry thermoset epoxy blade. In this work, the results of this comparison showed that the flatwise structural static performance and the fatigue performance of the two blades were similar, but the thermoplastic composite blade had increased damping compared to the epoxy blade, which may result in reduced operational loads.

17 WIND ENERGY↗
DOE OSTI2024

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Enhanced joining strength in additive-manufactured polylactic-acid structures fused by embedded heated metallic meshes

Additively manufactured thermoplastic polymers, such as polylactic acid (PLA), hold significant promise for sustainable engineering structures, including wind turbine blades. Upscaling these structures beyond the limitations of 3D printer build volumes is a challenge; fusion joining presents a potential solution. This paper introduces a displacement-controlled resistance welding process for PLA, as an alternative to the typical force-controlled methods. Here, we investigated the bonding quality of resistance-welded and adhesive-bonded PLA beams through three-point bending and measured the surface deformations using digital image correlation. Different metal meshes (30%/0.11 mm Ni—Cu, 34%/0.07 mm Ni—Cu, and 36%/0.25 mm Co—Ni) served as heating elements. The process parameters were varied for the 34%/0.07 mm Ni—Cu mesh to identify an optimum set of parameters. Results showed that this optimized displacement-controlled welding achieved 94% of the original strength of monolithic samples. This indicates that the new welding process not only ensures high-quality bonding and fine surface finishing but also promotes sustainability, recyclability, and economic efficiency in various polymer and composite structural applications.

Additive manufacturing↗
DOE OSTI2021

Validation of a lightning protection system for a fusion-welded thermoplastic composite wind turbine blade tip

Fusion welding for thermoplastic composite wind turbine blades is being investigated as a replacement for adhesives in bond lines to reduce manufacturing time and cost and to promote ease of recyclability. Resistance welding uses an electrically conductive material, such as carbon fiber, embedded in the bond line to heat the joining surfaces and fuse them together under pressure. One main challenge with fusion joining for large wind blades is the ability to use this conductive heating element in the bond line while still protecting the blade from the catastrophic effects of a lightning strike. This paper outlines the design and manufacturing of a lightning protection system for a 5-m-long thermally welded blade tip. The outcomes of high voltage and high current lightning testing applied to the 5 m blade tip show that the proposed design was able to protect the fusion welded bond lines from significant lightning damage.

17 WIND ENERGY↗
DOE OSTI2023

A renewable lignin-based thermoplastic adhesive for steel joining

Adhesive bonding of metals has become increasingly relevant in recent years due to the demand for reducing weight and improving performance in structural applications such as automobiles and aerospace. We developed renewable thermoplastic adhesives from technical organosolv lignin isolated from hardwood biomass and acrylonitrile butadiene co-polymer rubber (NBR) for joining steel substrates. NBR33, NBR41 and NBR51 with acrylonitrile molar ratios of 33, 41 and 51%, respectively, were blended with lignin to form two-phase thermoplastic adhesives, and their adhesion, viscoelastic and surface properties were measured. Lignin content in the compositions were varied, ranging from 40% to 80% (w/w), to alter toughness, stiffness, and surface energy characteristics of the material. Better interaction or reactivity between the lignin and NBR phases was observed with greater nitrile content in NBR, leading to greater modulus and stiffness of the adhesive. Simultaneously, increasing the proportion of lignin reduced toughness and improved stiffness, with the highest adhesive strength of 13.1 MPa measured in a 60% lignin loading ratio with NBR51. Surface energy measurements revealed that total surface energy (sum of polar and dispersive surface energy) raised with lignin loading, suggesting that both surface energy and matrix strength play a critical role in the adhesive properties of the synthesized materials. A finite element-based cohesive zone model (CZM) was developed and implemented to study the failure strength of the adhesively bonded joint. Here, this study demonstrates the viability of lignin as a valuable building block for adhesives, not only due to its inherent chemical structure and rigidity, but also for its surface energy characteristics.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Effect of 3D printing conditions on the micro- and macrostructure and properties of high-performance thermoplastic composites

Within the scope of extrusion based additive manufacturing, this chapter provides a review of the research in understanding the physical relationships between microstructure variables and macrostructure properties of high-performance thermoplastic polymers. The development of interlayer strength has been discussed based on the theory of inter-diffusion and re-entanglement of polymer chains at interface. The observations from the study in joining and direct-bonding of polymer surfaces provide insights into the mechanisms of the formation of interface between adjacent layers. Quantitative prediction of interfacial strength as a function of processing temperature and contact time is presented. The mechanisms that lead to reduced mechanical strength at the interface are discussed. The second half of the chapter outlined a set of printing conditions and their influence on structural properties.

Liu, Peng↗
DOE OSTI2024

Empirical Characterization and Modeling of Cohesive – to – Adhesive Shear Fracture Mode Transition due to Increased Adhesive Layer Thicknesses of Fiber Reinforced Composite Single – Lap Joints

Here, to ensure a strong adhesive bond, most standards and adhesive manufacturers specify a maximum adhesive gap of 1 mm when bonding fiber reinforced composite structures. In manufacturing large components, such as joining two halves of wind turbine blades, meeting this gap tolerance specification is impractical; gaps larger than 10 mm are common in large adhesively bonded composite structures using state-of-the-art manufacturing techniques. Currently, there is a lack of fundamental understanding of the failure mechanics of adhesive gaps larger than 3 mm. To create such understanding, glass fiber - acrylic thermoplastic composite panels bonded using different epoxy adhesives within single-lap joint samples with adhesive thicknesses of 0.1 mm, 0.3 mm, 1 mm, 3 mm, 5 mm, and 10 mm were sheared to failure. A transition from cohesive to adhesive failure was observed to occur about 1 mm to 3 mm joint thicknesses. Plotting the shear stress normalized by the ratio of the joint width to thickness as a function of the joint thickness normalized by the joint length is shown to result in the ability to fit simple empirically derived models of the cohesive-to-adhesive failure transition, regardless of the adhesive. Furthermore, using these normalized variables, all the observed cohesively failed specimens collapse to a single master curve, as do the adhesively failed specimens.

36 MATERIALS SCIENCE↗